PirateBay Download | complete data science machine learning z python

complete data science machine learning z python

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Uploaded On:: April 30, 2023, 2:11 p.m.
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Description Hello there, Welcome to the ” Complete Data Science & Machine Learning A-Z with Python “ Course Machine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, Kaggle Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field. Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions. It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data science Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. Pandas is built on top of another package named Numpy, which provides support for multi-dimensional arrays. Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. data analysis, pandas, numpy, numpy stack, numpy python, python data analysis, python, Python numpy, data visualization, pandas python, python pandas, python for data analysis, python data, data visualization. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels. Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Moreover, Numpy forms the foundation of the Machine Learning stack. Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python’s simple syntax is especially suited for desktop, web, and business applications. Python’s design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python’s large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks. Do you know data science needs will create 11.5 million job openings by 2026? Do you know the average salary is $100.000 for data science careers! Data Science Careers Are Shaping The Future Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand. If you want to learn one of the employer’s most request skills? If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python? If you are an experienced developer and looking for a landing in Data Science! In all cases, you are at the right place! We’ve designed for you “Machine Learning & Data Science with Python & Kaggle | A-Z” a straightforward course for Python Programming Language and Machine Learning. In the course, you will have down-to-earth way explanations with projects. With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples. Also you will get to know the Kaggle platform step by step with hearth attack prediction kaggle project. Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community-published data & code. Kaggle is a platform where data scientists can compete in machine learning challenges. These challenges can be anything from predicting housing prices to detecting cancer cells. Kaggle has a massive community of data scientists who are always willing to help others with their data science problems. You will learn the Numpy and Pandas Python Programming Language libraries step by step. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. You will learn data analysis and visualization in detail. Data visualization is the graphical representation of information and data. It is a storytelling tool that provides a way to communicate the meaning behind a data set. Simply put, data visualization helps users — the individuals or teams who generate the data, and in many cases, their audience — make sense of data and make the best data-driven decisions Statistics alone can fall flat. That’s why data visualization is so important to communicating the meaning behind data sets. Good visualizations can magically transform complex data analysis into appealing and easily understood representations that in turn inform smarter, more calculated business moves. Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning python algorithms. This Machine Learning course is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher). What is machine learning? Machine learning describes systems that make predictions using a model trained on real-world data. For example, let’s say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it’s fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model. Why we use a Python programming language in Machine learning? Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, it supports a lot of today’s technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what not! Also, it supports all kinds of App development. What is machine learning used for? Machine learning a-z is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions. Does Machine learning require coding? It’s possible to use machine learning data science without coding, but building new systems generally requires code. For example, Amazon’s Rekognition service allows you to upload an image via a web browser, which then identifies objects in the image. This uses a pre-trained model, with no coding required. However, developing machine learning systems involves writing some Python code to train, tune, and deploy your models. It’s hard to avoid writing code to pre-process the data feeding into your model. Most of the work done by a machine learning practitioner involves cleaning the data used to train the machine. They also perform “feature engineering” to find what data to use and how to prepare it for use in a machine learning model. Tools like AutoML and SageMaker automate the tuning of models. Often only a few lines of code can train a model and make predictions from it What is the best language for machine learning? Python is the most used language in machine learning using python. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It’s useful to have a development environment such as Python so that you don’t need to compile and package code before running it each time. Python is not the only language choice for machine learning. Tensorflow is a popular framework for developing neural networks and offers a C++ API. There is a complete machine learning framework for C# called ML. NET. Scala or Java are sometimes used with Apache Spark to build machine learning systems that ingest massive data sets. What is data visualization? Data visualization is the graphical representation of information and data. It is a storytelling tool that provides a way to communicate the meaning behind a data set. Simply put, data visualization helps users — the individuals or teams who generate the data, and in many cases, their audience — make sense of data and make the best data-driven decisions. Good visualizations can magically transform complex data analysis into appealing and easily understood representations that, in turn, inform smarter, more calculated business moves. Using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. What is a Kaggle? Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. How does Kaggle work? Every competition on Kaggle has a dataset associated with it and a goal you must reach (i.e., predict housing prices or detect cancer cells). You can access the data as often as possible and build your prediction model. Still, once you submit your solution, you cannot use it to make future submissions. This ensures that everyone is starting from the same point when competing against one another, so there are no advantages given to those with more computational power than others trying to solve the problem. Competitions are separated into different categories depending on their complexity level, how long they take, whether or not prize money is involved, etc., so users with varying experience levels can compete against each other in the same arena. What is a Pandas in Python? Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. It is built on top of another package named Numpy, which provides support for multi-dimensional arrays. What is Pandas used for? Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. What is difference between NumPy and pandas? NumPy library provides objects for multi-dimensional arrays, whereas Pandas is capable of offering an in-memory 2d table object called DataFrame. NumPy consumes less memory as compared to Pandas. Indexing of the Series objects is quite slow as compared to NumPy arrays. What are the different types of machine learning? Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning, we train machine learning models on labeled data. For example, an algorithm meant to detect spam might ingest thousands of email addresses labeled ‘spam’ or ‘not spam.’ That trained model could then identify new spam emails even from data it’s never seen. In unsupervised learning, a machine learning model looks for patterns in unstructured data. One type of unsupervised learning is clustering. In this example, a model could identify similar movies by studying their scripts or cast, then group the movies together into genres. This unsupervised model was not trained to know which genre a movie belongs to. Rather, it learned the genres by studying the attributes of the movies themselves. There are many techniques available within. Is Machine learning a good career? Machine learning python is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving, you can apply machine learning to a variety of industries, from shipping and fulfillment to medical sciences. Machine learning engineers work to create artificial intelligence that can better identify patterns and solve problems. The machine learning discipline frequently deals with cutting-edge, disruptive technologies. However, because it has become a popular career choice, it can also be competitive. Aspiring machine learning engineers can differentiate themselves from the competition through certifications, boot camps, code repository submissions, and hands-on experience. What is the difference between machine learning and artifical intelligence? Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any “intelligent machine” that can derive information and make decisions, machine learning describes a method by which it can do so. Through machine learning, applications can derive knowledge without the user explicitly giving out the information. This is one of the first and early steps toward “true artificial intelligence” and is extremely useful for numerous practical applications. In machine learning applications, an AI is fed sets of information. It learns from these sets of information about what to expect and what to predict. But it still has limitations. A machine learning engineer must ensure that the AI is fed the right information and can use its logic to analyze that information correctly. What skills should a machine learning engineer know? A python machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science, and artificial intelligence theory. Machine learning engineers must be able to dig deep into complex applications and their programming. As with other disciplines, there are entry-level machine learning engineers and machine learning engineers with high-level expertise. Python and R are two of the most popular languages within the machine learning field. What is data science? We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods. Why would you want to take this course? Our answer is simple: The quality of teaching. OAK Academy based in London is an online education company. OAK Academy gives education in the field of IT, Software, Design, development in English, Portuguese, Spanish, Turkish, and a lot of different languages on the Udemy platform where it has over 1000 hours of video education lessons. OAK Academy both increases its education series number by publishing new courses, and it makes students aware of all the innovations of already published courses by upgrading. When you enroll, you will feel the OAK Academy`s seasoned developers’ expertise. Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest. Video and Audio Production Quality All our videos are created/produced as high-quality video and audio to provide you the best learning experience. You will be, Seeing clearly Hearing clearly Moving through the course without distractions You’ll also get: Lifetime Access to The Course Fast & Friendly Support in the Q&A section Udemy Certificate of Completion Ready for Download We offer full support, answering any questions. If you are ready to learn Dive in now into the ” Complete Data Science & Machine Learning A-Z with Python” Course Machine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, Kaggle See you in the course! Who this course is for: Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. It is for everyone Anyone who wants to start learning “Machine Learning” Anyone who needs a complete guide on how to start and continue their career with machine learning Students Interested in Beginning Data Science Applications in Python Environment People Wanting to Specialize in Anaconda Python Environment for Data Science and Scientific Computing Students Wanting to Learn the Application of Supervised Learning (Classification) on Real Data Using Python Anyone eager to learn python for data science and machine learning bootcamp with no coding background Anyone interested in data sciences Anyone who plans a career in data scientist, Software developer whom want to learn python, Anyone interested in machine learning a-z Requirements Basic knowledge of Python Programming Language Be Able To Operate & Install Software On A Computer Free software and tools used during the machine learning a-z course Motivation to learn the the second largest number of job postings relative program language among all others Data visualization libraries in python such as seaborn, matplotlib Desire to learn Python and machine learning python Desire to work on python machine learning Desire to learn pandas Desire to learn numpy Any device you can watch the course, such as a mobile phone, computer or tablet. Watching the lecture videos completely, to the end and in order. Nothing else! It’s just you, your computer and your ambition to get started today. LIFETIME ACCESS, course updates, new content, anytime, anywhere, on any device. Last Updated 4/2023

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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/42. Competition Section on Kaggle/2. Competitions on Kaggle Lesson 2.mp4 191.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/14. Functions That Can Be Applied on a DataFrame/3. Aggregation Functions in Pandas DataFrames.mp4 90.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/23. Geoplotlib/3. Example - 2.mp4 81.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 80.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/44. Code Section on Kaggle/1. Examining the Code Section in Kaggle Lesson 1.mp4 79.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/5. Examining the Project Topic.mp4 76.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/27. Linear Regression Algorithm in Machine Learning A-Z/2. Linear Regression Algorithm With Python Part 1.mp4 76.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/19. Fundamentals of Python 3/5. Lists, Tuples, Dictionaries and Sets in pyhton.mp4 75.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 74.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/47. Details on Kaggle/2. Treasure in The Kaggle.mp4 74.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/29. Logistic Regression Algorithm in Machine Learning A-Z/2. Logistic Regression Algorithm with Python Part 1.mp4 72.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/6. Operations in Numpy Library/2. Arithmetic Operations in Numpy.mp4 71.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/27. Linear Regression Algorithm in Machine Learning A-Z/4. Linear Regression Algorithm With Python Part 3.mp4 70.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/21. Matplotlib/4. Figure, Subplot and Axex.mp4 69.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 68.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/11. Structural Operations on Pandas DataFrame/3. Null Values in Pandas Dataframes.mp4 67.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/16. File Operations in Pandas Library/2. Data Entry with Csv and Txt Files.mp4 64.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/49. First Organization/3. Initial analysis on the dataset.mp4 64.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/13. Structural Concatenation Operations in Pandas DataFrame/1. Concatenating Pandas Dataframes Concat Function.mp4 63.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/49. First Organization/1. Required Python Libraries.mp4 63.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/21. Matplotlib/5. Figure Customization.mp4 63.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/20. Object Oriented Programming (OOP)/5. Overriding and Overloading in Object Oriented Programming (OOP).mp4 62.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/13. Structural Concatenation Operations in Pandas DataFrame/4. Merge Pandas Dataframes Merge() Function Lesson 3.mp4 60.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/22. Seaborn/7. Regression Plots and Squarify in Seaborn.mp4 60.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/2. NumPy Library Introduction/2. The Power of NumPy.mp4 59.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/31. K Nearest Neighbors Algorithm in Machine Learning A-Z/3. K Nearest Neighbors Algorithm with Python Part 2.mp4 59.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/19. Fundamentals of Python 3/4. Loops in Python.mp4 58.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/54. Modelling for Machine Learning/4. Hyperparameter Optimization (with GridSearchCV).mp4 58.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/47. Details on Kaggle/4. What Should Be Done to Achieve Success in Kaggle.mp4 58.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/13. Structural Concatenation Operations in Pandas DataFrame/2. Merge Pandas Dataframes Merge() Function Lesson 1.mp4 57.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 56.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/13. Structural Concatenation Operations in Pandas DataFrame/6. Joining Pandas Dataframes Join() Function.mp4 56.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/28. Bias Variance Trade-Off in Machine Learning/1. What is Bias Variance Trade-Off.mp4 55.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/22. Seaborn/3. Example in Seaborn.mp4 54.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/21. Matplotlib/9. Basic Plots in Matplotlib II.mp4 54.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/15. Pivot Tables in Pandas Library/2. Pivot Tables in Pandas Library.mp4 54.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/5. Examining the Missing Data According to the Analysis Result.mp4 53.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/8. Creating a New DataFrame with the Melt() Function.mp4 52.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/54. Modelling for Machine Learning/8. Hyperparameter Optimization (with GridSearchCV).mp4 52.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/46. Other Most Used Options on Kaggle/1. Courses in Kaggle.mp4 52.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/19. Fundamentals of Python 3/10. Exercise - Solution in Python.mp4 51.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/11. Structural Operations on Pandas DataFrame/5. Filling Null Values Fillna() Function.mp4 51.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/23. Geoplotlib/4. Example - 3.mp4 51.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 49.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/33. Decision Tree Algorithm in Machine Learning A-Z/3. Decision Tree Algorithm with Python Part 2.mp4 48.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/22. Seaborn/4. Color Palettes in Seaborn.mp4 48.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/8. Series Structures in the Pandas Library/6. Most Applied Methods on Pandas Series.mp4 48.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/32. Hyperparameter Optimization/2. Hyperparameter Optimization with Python.mp4 47.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/29. Logistic Regression Algorithm in Machine Learning A-Z/4. Logistic Regression Algorithm with Python Part 3.mp4 47.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/29. Logistic Regression Algorithm in Machine Learning A-Z/5. Logistic Regression Algorithm with Python Part 4.mp4 47.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 47.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/14. Functions That Can Be Applied on a DataFrame/8. Advanced Aggregation Functions Transform() Function.mp4 47.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/19. Fundamentals of Python 3/1. Data Types in Python.mp4 47.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/14. Functions That Can Be Applied on a DataFrame/4. Examining the Data Set 2.mp4 46.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/10. Element Selection Operations in DataFrame Structures/6. Element Selection with Conditional Operations in.mp4 46.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/1. Installations/3. Installing Anaconda Distribution for MacOs.mp4 46.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/50. Preparation For Exploratory Data Analysis (EDA) in Data Science/1. Examining Missing Values.mp4 45.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/5. Indexing, Slicing, and Assigning NumPy Arrays/7. Fancy Indexing of Two-Dimensional Arrrays.mp4 45.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/25. Evaluation Metrics in Machine Learning/3. Evaluating Performance Regression Error Metrics in Python.mp4 45.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/2. NumPy Library Introduction/1. Introduction to NumPy Library.mp4 45.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/50. Preparation For Exploratory Data Analysis (EDA) in Data Science/2. Examining Unique Values.mp4 44.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/53. Preparation for Modelling in Machine Learning/4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 43.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/19. Fundamentals of Python 3/6. Data Type Operators and Methods in Python.mp4 43.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/41. First Contact with Kaggle/3. Registering on Kaggle and Member Login Procedures.mp4 43.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/3. Creating NumPy Array in Python/8. Creating NumPy Array with Random() Function.mp4 43.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/22. Seaborn/6. Multi-Plots in Seaborn.mp4 43.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/14. Functions That Can Be Applied on a DataFrame/2. Examining the Data Set 1.mp4 42.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/53. Preparation for Modelling in Machine Learning/3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 42.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/12. Multi-Indexed DataFrame Structures/1. Multi-Index and Index Hierarchy in Pandas DataFrames.mp4 42.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/33. Decision Tree Algorithm in Machine Learning A-Z/5. Decision Tree Algorithm with Python Part 4.mp4 42.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/22. Seaborn/2. Controlling Figure Aesthetics in Seaborn.mp4 41.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/35. Support Vector Machine Algorithm in Machine Learning A-Z/3. Support Vector Machine Algorithm with Python Part 2.mp4 41.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 41.7 MB
.pad/96 291.4 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/54. Modelling for Machine Learning/3. Roc Curve and Area Under Curve (AUC).mp4 41.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/14. Functions That Can Be Applied on a DataFrame/9. Advanced Aggregation Functions Apply() Function.mp4 41.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/19. Fundamentals of Python 3/3. Conditionals in Python.mp4 41.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/46. Other Most Used Options on Kaggle/3. Blog and Documentation Sections.mp4 40.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/13. Structural Concatenation Operations in Pandas DataFrame/5. Merge Pandas Dataframes Merge() Function Lesson 4.mp4 40.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/45. Discussion Section on Kaggle/1. What is Discussion on Kaggle.mp4 40.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/11. Structural Operations on Pandas DataFrame/6. Setting Index in Pandas DataFrames.mp4 39.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/29. Logistic Regression Algorithm in Machine Learning A-Z/6. Logistic Regression Algorithm with Python Part 5.mp4 39.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/8. Series Structures in the Pandas Library/1. Creating a Pandas Series with a List.mp4 39.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/15. Pivot Tables in Pandas Library/1. Examining the Data Set 3.mp4 39.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/23. Geoplotlib/2. Example - 1.mp4 38.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/34. Random Forest Algorithm in Machine Learning A-Z/3. Random Forest Algorithm with Pyhon Part 2.mp4 38.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/34. Random Forest Algorithm in Machine Learning A-Z/2. Random Forest Algorithm with Pyhon Part 1.mp4 38.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/4. Functions in the NumPy Library/4. Concatenating Numpy Arrays Concatenate() Functio.mp4 38.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/10. Element Selection Operations in DataFrame Structures/3. Top Level Element Selection in Pandas DataFramesLesson 1.mp4 38.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/47. Details on Kaggle/3. Publishing Notebooks on Kaggle.mp4 38.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 38.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/39. Principal Component Analysis (PCA) in Machine Learning A-Z/1. Principal Component Analysis (PCA) Theory.mp4 38.0 MB
.pad/114 41.4 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/14. Functions That Can Be Applied on a DataFrame/1. Loading a Dataset from the Seaborn Library.mp4 37.7 MB
.pad/115 284.3 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/35. Support Vector Machine Algorithm in Machine Learning A-Z/5. Support Vector Machine Algorithm with Python Part 4.mp4 37.6 MB
.pad/116 455.7 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/39. Principal Component Analysis (PCA) in Machine Learning A-Z/4. Principal Component Analysis (PCA) with Python Part 3.mp4 37.2 MB
.pad/117 768.6 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 36.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/53. Preparation for Modelling in Machine Learning/5. Dealing with Outliers – Thalach Variable.mp4 36.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/53. Preparation for Modelling in Machine Learning/6. Dealing with Outliers – Oldpeak Variable.mp4 36.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/20. Object Oriented Programming (OOP)/2. Constructor in Object Oriented Programming (OOP).mp4 35.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/33. Decision Tree Algorithm in Machine Learning A-Z/1. Decision Tree Algorithm Theory.mp4 35.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/4. Functions in the NumPy Library/6. Splitting Two-Dimensional Numpy Arrays Split(),.mp4 35.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/19. Fundamentals of Python 3/2. Operators in Python.mp4 35.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/16. File Operations in Pandas Library/4. Outputting as an CSV Extension.mp4 35.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 35.6 MB
.pad/126 373.4 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/35. Support Vector Machine Algorithm in Machine Learning A-Z/2. Support Vector Machine Algorithm with Python Part 1.mp4 35.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/38. Hierarchical Clustering Algorithm in machine learning data science/2. Hierarchical Clustering Algorithm with Python Part 2.mp4 35.5 MB
.pad/128 494.3 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 35.5 MB
.pad/129 546.3 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/5. Indexing, Slicing, and Assigning NumPy Arrays/5. Assigning Value to Two-Dimensional Array.mp4 35.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/7. Feature Scaling with the Robust Scaler Method.mp4 35.2 MB
.pad/131 820.3 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/31. K Nearest Neighbors Algorithm in Machine Learning A-Z/2. K Nearest Neighbors Algorithm with Python Part 1.mp4 35.0 MB
.pad/132 990.0 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/53. Preparation for Modelling in Machine Learning/2. Visualizing Outliers.mp4 34.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/35. Support Vector Machine Algorithm in Machine Learning A-Z/4. Support Vector Machine Algorithm with Python Part 3.mp4 34.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/30. K-fold Cross-Validation in Machine Learning A-Z/2. K-Fold Cross-Validation with Python.mp4 34.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/16. File Operations in Pandas Library/1. Accessing and Making Files Available.mp4 34.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/20. Object Oriented Programming (OOP)/4. Inheritance in Object Oriented Programming (OOP).mp4 34.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/11. Structural Operations on Pandas DataFrame/4. Dropping Null Values Dropna() Function.mp4 34.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/5. Indexing, Slicing, and Assigning NumPy Arrays/3. Slicing Two-Dimensional Numpy Arrays.mp4 34.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/23. Geoplotlib/1. What is Geoplotlib.mp4 34.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/27. Linear Regression Algorithm in Machine Learning A-Z/1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4 34.1 MB
.pad/141 1.9 MB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/7. Pandas Library Introduction/1. Introduction to Pandas Library.mp4 33.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/11. Structural Operations on Pandas DataFrame/1. Adding Columns to Pandas Data Frames.mp4 33.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/32. Hyperparameter Optimization/1. Hyperparameter Optimization Theory.mp4 33.1 MB
.pad/144 880.5 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/33. Decision Tree Algorithm in Machine Learning A-Z/6. Decision Tree Algorithm with Python Part 5.mp4 32.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/6. Operations in Numpy Library/3. Statistical Operations in Numpy.mp4 32.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/10. Element Selection Operations in DataFrame Structures/2. Element Selection Operations in Pandas DataFrames Lesson 2.mp4 31.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/26. Supervised Learning with Machine Learning/1. What is Supervised Learning in Machine Learning.mp4 31.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/33. Decision Tree Algorithm in Machine Learning A-Z/2. Decision Tree Algorithm with Python Part 1.mp4 31.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/10. Element Selection Operations in DataFrame Structures/4. Top Level Element Selection in Pandas DataFramesLesson 2.mp4 31.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/31. K Nearest Neighbors Algorithm in Machine Learning A-Z/4. K Nearest Neighbors Algorithm with Python Part 3.mp4 31.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/12. Multi-Indexed DataFrame Structures/3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp4 31.3 MB
.pad/152 764.4 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/13. Structural Concatenation Operations in Pandas DataFrame/3. Merge Pandas Dataframes Merge() Function Lesson 2.mp4 30.5 MB
.pad/153 1.5 MB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/54. Modelling for Machine Learning/2. Cross Validation.mp4 30.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/37. K Means Clustering Algorithm in Machine Learning A-Z/2. K Means Clustering Algorithm with Python Part 1.mp4 30.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/10. Element Selection Operations in DataFrame Structures/1. Element Selection Operations in Pandas DataFrames Lesson 1.mp4 29.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/8. Series Structures in the Pandas Library/7. Indexing and Slicing Pandas Series.mp4 29.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/54. Modelling for Machine Learning/7. Random Forest Algorithm.mp4 29.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/53. Preparation for Modelling in Machine Learning/11. Separating Data into Test and Training Set.mp4 29.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/37. K Means Clustering Algorithm in Machine Learning A-Z/3. K Means Clustering Algorithm with Python Part 2.mp4 29.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/3. Creating NumPy Array in Python/1. Creating NumPy Array with The Array() Function.mp4 29.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/54. Modelling for Machine Learning/1. Logistic Regression.mp4 29.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/14. Functions That Can Be Applied on a DataFrame/6. Advanced Aggregation Functions Aggregate() Function.mp4 29.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/37. K Means Clustering Algorithm in Machine Learning A-Z/5. K Means Clustering Algorithm with Python Part 4.mp4 29.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/19. Fundamentals of Python 3/8. Functions in Python.mp4 28.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/38. Hierarchical Clustering Algorithm in machine learning data science/3. Hierarchical Clustering Algorithm with Python Part 2.mp4 28.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/55. Conclusion/1. Project Conclusion and Sharing.mp4 28.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/31. K Nearest Neighbors Algorithm in Machine Learning A-Z/1. K Nearest Neighbors Algorithm Theory.mp4 28.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/38. Hierarchical Clustering Algorithm in machine learning data science/1. Hierarchical Clustering Algorithm Theory.mp4 28.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/21. Matplotlib/3. Pyplot – Pylab - Matplotlib.mp4 28.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 28.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/21. Matplotlib/2. Using Pyplot.mp4 28.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/29. Logistic Regression Algorithm in Machine Learning A-Z/1. What is Logistic Regression Algorithm in Machine Learning.mp4 27.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/37. K Means Clustering Algorithm in Machine Learning A-Z/4. K Means Clustering Algorithm with Python Part 3.mp4 27.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/24. First Contact with Machine Learning/1. What is Machine Learning.mp4 27.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/21. Matplotlib/6. Plot Customization.mp4 27.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/53. Preparation for Modelling in Machine Learning/1. Dropping Columns with Low Correlation.mp4 26.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/5. Indexing, Slicing, and Assigning NumPy Arrays/1. Indexing Numpy Arrays,.mp4 26.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/4. Functions in the NumPy Library/1. Reshaping a NumPy Array Reshape() Function.mp4 26.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/39. Principal Component Analysis (PCA) in Machine Learning A-Z/2. Principal Component Analysis (PCA) with Python Part 1.mp4 26.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/9. DataFrame Structures in Pandas Library/4. Examining the Properties of Pandas DataFrames.mp4 25.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/54. Modelling for Machine Learning/5. Decision Tree Algorithm.mp4 25.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/53. Preparation for Modelling in Machine Learning/7. Determining Distributions of Numeric Variables.mp4 25.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/20. Object Oriented Programming (OOP)/3. Methods in Object Oriented Programming (OOP).mp4 25.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/12. Multi-Indexed DataFrame Structures/2. Element Selection in Multi-Indexed DataFrames.mp4 24.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/54. Modelling for Machine Learning/6. Support Vector Machine Algorithm.mp4 24.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/14. Functions That Can Be Applied on a DataFrame/7. Advanced Aggregation Functions Filter() Function.mp4 24.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/6. Operations in Numpy Library/4. Solving Second-Degree Equations with NumPy.mp4 24.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 24.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/53. Preparation for Modelling in Machine Learning/9. Applying One Hot Encoding Method to Categorical Variables.mp4 24.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/3. Creating NumPy Array in Python/2. Creating NumPy Array with Zeros() Function.mp4 24.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/53. Preparation for Modelling in Machine Learning/8. Transformation Operations on Unsymmetrical Data.mp4 24.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/19. Fundamentals of Python 3/7. Modules in Python.mp4 23.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/21. Matplotlib/7. Grid, Spines, Ticks.mp4 23.9 MB
.pad/194 115.0 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/40. Recommender System Algorithm in Machine Learning A-Z/1. What is the Recommender System Part 1.mp4 23.0 MB
.pad/195 984.3 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/34. Random Forest Algorithm in Machine Learning A-Z/1. Random Forest Algorithm Theory.mp4 22.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/9. DataFrame Structures in Pandas Library/1. Creating Pandas DataFrame with List.mp4 22.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/5. Indexing, Slicing, and Assigning NumPy Arrays/2. Slicing One-Dimensional Numpy Arrays.mp4 22.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/10. Element Selection Operations in DataFrame Structures/5. Top Level Element Selection in Pandas DataFramesLesson 3.mp4 22.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/3. Creating NumPy Array in Python/9. Properties of NumPy Array.mp4 22.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/35. Support Vector Machine Algorithm in Machine Learning A-Z/1. Support Vector Machine Algorithm Theory.mp4 21.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/16. File Operations in Pandas Library/3. Data Entry with Excel Files.mp4 21.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/6. Operations in Numpy Library/1. Operations with Comparison Operators.mp4 21.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/4. Functions in the NumPy Library/5. Splitting One-Dimensional Numpy Arrays The Split.mp4 20.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/5. Indexing, Slicing, and Assigning NumPy Arrays/6. Fancy Indexing of One-Dimensional Arrrays.mp4 20.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/25. Evaluation Metrics in Machine Learning/1. Classification vs Regression in Machine Learning.mp4 19.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 19.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/16. File Operations in Pandas Library/5. Outputting as an Excel File.mp4 19.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/8. Series Structures in the Pandas Library/4. Object Types in Series.mp4 19.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/21. Matplotlib/1. What is Matplotlib.mp4 19.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/8. Series Structures in the Pandas Library/5. Examining the Primary Features of the Pandas Seri.mp4 18.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/8. Series Structures in the Pandas Library/2. Creating a Pandas Series with a Dictionary.mp4 18.3 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/5. Indexing, Slicing, and Assigning NumPy Arrays/4. Assigning Value to One-Dimensional Arrays.mp4 18.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/40. Recommender System Algorithm in Machine Learning A-Z/2. What is the Recommender System Part 2.mp4 18.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/30. K-fold Cross-Validation in Machine Learning A-Z/1. K-Fold Cross-Validation Theory.mp4 17.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/20. Object Oriented Programming (OOP)/1. Logic of Object Oriented Programming.mp4 17.4 MB
.pad/216 630.4 KB
[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/37. K Means Clustering Algorithm in Machine Learning A-Z/1. K Means Clustering Algorithm Theory.mp4 17.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/4. Functions in the NumPy Library/7. Sorting Numpy Arrays Sort() Function.mp4 17.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/36. Unsupervised Learning with Machine Learning/1. Unsupervised Learning Overview.mp4 16.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/5. Indexing, Slicing, and Assigning NumPy Arrays/9. Combining Fancy Index with Normal Slicing.mp4 16.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/3. Creating NumPy Array in Python/3. Creating NumPy Array with Ones() Function.mp4 15.9 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/9. DataFrame Structures in Pandas Library/3. Creating Pandas DataFrame with Dictionary.mp4 15.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/50. Preparation For Exploratory Data Analysis (EDA) in Data Science/3. Separating variables (Numeric or Categorical).mp4 15.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/11. Structural Operations on Pandas DataFrame/2. Removing Rows and Columns from Pandas Data frames.mp4 15.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/4. Functions in the NumPy Library/2. Identifying the Largest Element of a Numpy Array.mp4 15.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/33. Decision Tree Algorithm in Machine Learning A-Z/4. Decision Tree Algorithm with Python Part 3.mp4 14.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/24. First Contact with Machine Learning/2. Machine Learning Terminology.mp4 14.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/22. Seaborn/1. What is Seaborn.mp4 13.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/18. Introduction to Data Visualization with Python/1. Introduction to Data Visualization with Python.mp4 12.8 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/5. Indexing, Slicing, and Assigning NumPy Arrays/8. Combining Fancy Index with Normal Indexing.mp4 12.7 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/3. Creating NumPy Array in Python/6. Creating NumPy Array with Eye() Function.mp4 12.6 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/3. Creating NumPy Array in Python/5. Creating NumPy Array with Arange() Function.mp4 12.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/9. DataFrame Structures in Pandas Library/2. Creating Pandas DataFrame with NumPy Array.mp4 12.1 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/8. Series Structures in the Pandas Library/3. Creating Pandas Series with NumPy Array.mp4 12.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/53. Preparation for Modelling in Machine Learning/10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 11.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/3. Creating NumPy Array in Python/4. Creating NumPy Array with Full() Function.mp4 11.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/4. Functions in the NumPy Library/3. Detecting Least Element of Numpy Array Min(), Ar.mp4 10.2 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/49. First Organization/2. Loading the Statistics Dataset in Data Science.mp4 10.0 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/19. Fundamentals of Python 3/9. Exercise - Analyse in Python.mp4 8.5 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/39. Principal Component Analysis (PCA) in Machine Learning A-Z/3. Principal Component Analysis (PCA) with Python Part 2.mp4 8.4 MB
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[TutsNode.net] - Complete Data Science & Machine Learning A-Z with Python/3. Creating NumPy Array in Python/7. Creating NumPy Array with Linspace() Function.mp4 7.3 MB

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