Udemy - 2021 Python for Data Science & Machine Learning from A-Z

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Udemy - 2021 Python for Data Science & Machine Learning from A-Z (Size: 7.38 GB)
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  [TutsNode.com] - 2021 Python for Data Science & Machine Learning from A-Z
  1. Introduction
  1. Who is This Course For.mp4 17.16 MB
  1. Who is This Course For.srt 3.95 KB
  2. Data Science + Machine Learning Marketplace.mp4 46.94 MB
  2. Data Science + Machine Learning Marketplace.srt 10.4 KB
  3. Data Science Job Opportunities.mp4 29.42 MB
  3. Data Science Job Opportunities.srt 6.86 KB
  4. Data Science Job Roles.mp4 79.8 MB
  4. Data Science Job Roles.srt 15.73 KB
  5. What is a Data Scientist.mp4 127.47 MB
  5. What is a Data Scientist.srt 26.86 KB
  6. How To Get a Data Science Job.mp4 131.19 MB
  6. How To Get a Data Science Job.srt 30.62 KB
  7. Data Science Projects Overview.mp4 79.48 MB
  7. Data Science Projects Overview.srt 19.09 KB
  10. Data Loading & Exploration
  1. Exploratory Data Analysis.mp4 50.56 MB
  1. Exploratory Data Analysis.srt 19.07 KB
  11. Data Cleaning
  1. Feature Scaling.mp4 19.38 MB
  1. Feature Scaling.srt 11.58 KB
  2. Data Cleaning.mp4 30.21 MB
  2. Data Cleaning.srt 11.53 KB
  12. Feature Selecting and Engineering
  1. Feature Engineering.mp4 18.41 MB
  1. Feature Engineering.srt 9.42 KB
  13. Linear and Logistic Regression
  1. Linear Regression Intro.mp4 30.8 MB
  1. Linear Regression Intro.srt 12.16 KB
  2. Gradient Descent.mp4 15.93 MB
  2. Gradient Descent.srt 8.44 KB
  3. Linear Regression + Correlation Methods.mp4 110.38 MB
  3. Linear Regression + Correlation Methods.srt 38.61 KB
  4. Linear Regression Implementation.mp4 17.86 MB
  4. Linear Regression Implementation.srt 6.88 KB
  5. Logistic Regression.mp4 8.9 MB
  5. Logistic Regression.srt 4.93 KB
  14. K Nearest Neighbors
  1. KNN Overview.mp4 12.89 MB
  1. KNN Overview.srt 4.27 KB
  10. Feature scaling in KNN.mp4 49.39 MB
  10. Feature scaling in KNN.srt 8.06 KB
  11. Curse of dimensionality.mp4 45.99 MB
  11. Curse of dimensionality.srt 9.6 KB
  12. KNN use cases.mp4 28.92 MB
  12. KNN use cases.srt 4.85 KB
  13. KNN pros and cons.mp4 30.45 MB
  13. KNN pros and cons.srt 7.75 KB
  2. parametric vs non-parametric models.mp4 15.63 MB
  2. parametric vs non-parametric models.srt 4.73 KB
  3. EDA on Iris Dataset.mp4 161.88 MB
  3. EDA on Iris Dataset.srt 31.65 KB
  4. The KNN Intuition.mp4 8.09 MB
  4. The KNN Intuition.srt 3.06 KB
  5. Implement the KNN algorithm from scratch.mp4 86.97 MB
  5. Implement the KNN algorithm from scratch.srt 17.14 KB
  6. Compare the result with the sklearn library.mp4 24.57 MB
  6. Compare the result with the sklearn library.srt 5.1 KB
  7. Hyperparameter tuning using the cross-validation.mp4 90.3 MB
  7. Hyperparameter tuning using the cross-validation.srt 14.66 KB
  8. The decision boundary visualization.mp4 16.94 MB
  8. The decision boundary visualization.srt 7.07 KB
  9. Manhattan vs Euclidean Distance.mp4 30.49 MB
  9. Manhattan vs Euclidean Distance.srt 7.75 KB
  15. Decision Trees
  1. Decision Trees Section Overview.mp4 16.46 MB
  1. Decision Trees Section Overview.srt 5.6 KB
  10. Visualizing the tree.mp4 68.17 MB
  10. Visualizing the tree.srt 15 KB
  11. Plot the features importance.mp4 31.67 MB
  11. Plot the features importance.srt 7.75 KB
  12. Decision Trees Hyper-parameters.mp4 81.27 MB
  12. Decision Trees Hyper-parameters.srt 16.12 KB
  13. Pruning.mp4 112.97 MB
  13. Pruning.srt 24.35 KB
  14. [Optional] Gain Ration.mp4 19.18 MB
  14. [Optional] Gain Ration.srt 3.7 KB
  15. Decision Trees Pros and Cons.mp4 47.74 MB
  15. Decision Trees Pros and Cons.srt 10.6 KB
  16. [Project] Predict whether income exceeds $50Kyr - Overview.mp4 15.11 MB
  16. [Project] Predict whether income exceeds $50Kyr - Overview.srt 3.6 KB
  2. EDA on Adult Dataset.mp4 123.19 MB
  2. EDA on Adult Dataset.srt 23.77 KB
  3. What is Entropy and Information Gain.mp4 136.08 MB
  3. What is Entropy and Information Gain.srt 29.33 KB
  4. The Decision Tree ID3 algorithm from scratch Part 1.mp4 85.27 MB
  4. The Decision Tree ID3 algorithm from scratch Part 1.srt 14.91 KB
  5. The Decision Tree ID3 algorithm from scratch Part 2.mp4 63.96 MB
  5. The Decision Tree ID3 algorithm from scratch Part 2.srt 10.62 KB
  6. The Decision Tree ID3 algorithm from scratch Part 3.mp4 33.41 MB
  6. The Decision Tree ID3 algorithm from scratch Part 3.srt 5.75 KB
  7. ID3 - Putting Everything Together.mp4 182.48 MB
  7. ID3 - Putting Everything Together.srt 31.03 KB
  8. Evaluating our ID3 implementation.mp4 121.94 MB
  8. Evaluating our ID3 implementation.srt 24.54 KB
  9. Compare with Sklearn implementation.mp4 65.58 MB
  9. Compare with Sklearn implementation.srt 12.27 KB
  16. Ensemble Learning and Random Forests
  1. Ensemble Learning Section Overview.mp4 16.07 MB
  1. Ensemble Learning Section Overview.srt 5.17 KB
  10. Random Forests Pros and Cons.mp4 19.69 MB
  10. Random Forests Pros and Cons.srt 7.85 KB
  11. What is Boosting.mp4 35.44 MB
  11. What is Boosting.srt 6.86 KB
  12. AdaBoost Part 1.mp4 25.53 MB
  12. AdaBoost Part 1.srt 5.5 KB
  13. AdaBoost Part 2.mp4 85.94 MB
  13. AdaBoost Part 2.srt 20.87 KB
  2. What is Ensemble Learning.mp4 91.97 MB
  2. What is Ensemble Learning.srt 17.42 KB
  3. What is Bootstrap Sampling.mp4 55.88 MB
  3. What is Bootstrap Sampling.srt 11.13 KB
  4. What is Bagging.mp4 29.48 MB
  4. What is Bagging.srt 7.75 KB
  5. Out-of-Bag Error (OOB Error).mp4 42.03 MB
  5. Out-of-Bag Error (OOB Error).srt 9.97 KB
  6. Implementing Random Forests from scratch Part 1.mp4 202.55 MB
  6. Implementing Random Forests from scratch Part 1.srt 30.12 KB
  7. Implementing Random Forests from scratch Part 2.mp4 50.5 MB
  7. Implementing Random Forests from scratch Part 2.srt 8.27 KB
  8. Compare with sklearn implementation.mp4 27.65 MB
  8. Compare with sklearn implementation.srt 4.94 KB
  9. Random Forests Hyper-Parameters.mp4 39.67 MB
  9. Random Forests Hyper-Parameters.srt 5.97 KB
  17. Support Vector Machines
  1. SVM Outline.mp4 35.31 MB
  1. SVM Outline.srt 7.4 KB
  10. SMV - Project Overview.mp4 39.61 MB
  10. SMV - Project Overview.srt 6.23 KB
  2. SVM intuition.mp4 48.86 MB
  2. SVM intuition.srt 16.01 KB
  3. Hard vs Soft Margins.mp4 65.64 MB
  3. Hard vs Soft Margins.srt 18.92 KB
  4. C hyper-parameter.mp4 21.06 MB
  4. C hyper-parameter.srt 5.64 KB
  5. Kernel Trick.mp4 77.05 MB
  5. Kernel Trick.srt 18.19 KB
  6. SVM - Kernel Types.mp4 126.38 MB
  6. SVM - Kernel Types.srt 26.75 KB
  7. SVM with Linear Dataset (Iris).mp4 101.56 MB
  7. SVM with Linear Dataset (Iris).srt 19.85 KB
  8. SVM with Non-linear Dataset.mp4 111.55 MB
  8. SVM with Non-linear Dataset.srt 18.38 KB
  9. SVM with Regression.mp4 25 MB
  9. SVM with Regression.srt 8 KB
  18. K-means
  1. Unsupervised Machine Learning Intro.mp4 100.92 MB
  1. Unsupervised Machine Learning Intro.srt 29.36 KB
  1.1 Unsupervised Learning.pdf 636.55 KB
  2. Unsupervised Machine Learning Continued.mp4 83.13 MB
  2. Unsupervised Machine Learning Continued.srt 29.19 KB
  3. Representing Clusters.mp4 109.62 MB
  3. Representing Clusters.srt 28.32 KB
  19. PCA
  1. PCA Section Overview.mp4 31.77 MB
  1. PCA Section Overview.srt 7.06 KB
  10. PCA - Feature Scaling and Screen Plot.mp4 68.2 MB
  10. PCA - Feature Scaling and Screen Plot.srt 14.4 KB
  11. PCA - Supervised vs Unsupervised.mp4 35.79 MB
  11. PCA - Supervised vs Unsupervised.srt 7.12 KB
  12. PCA - Visualization.mp4 68.02 MB
  12. PCA - Visualization.srt 10.96 KB
  2. What is PCA.mp4 47.26 MB
  2. What is PCA.srt 14.59 KB
  3. PCA Drawbacks.mp4 19.44 MB
  3. PCA Drawbacks.srt 4.9 KB
  4. PCA Algorithm Steps (Mathematics).mp4 57.73 MB
  4. PCA Algorithm Steps (Mathematics).srt 18.59 KB
  5. Covariance Matrix vs SVD.mp4 38.74 MB
  5. Covariance Matrix vs SVD.srt 6.54 KB
  6. PCA - Main Applications.mp4 10.05 MB
  6. PCA - Main Applications.srt 3.9 KB
  7. PCA - Image Compression.mp4 249.92 MB
  7. PCA - Image Compression.srt 39.37 KB
  8. PCA Data Preprocessing.mp4 120.46 MB
  8. PCA Data Preprocessing.srt 21.05 KB
  9. PCA - Biplot and the Screen Plot.mp4 135.6 MB
  9. PCA - Biplot and the Screen Plot.srt 26.37 KB
  2. Data Science & Machine Learning Concepts
  1. Why We Use Python.mp4 13.51 MB
  1. Why We Use Python.srt 4.91 KB
  2. What is Data Science.mp4 87.99 MB
  2. What is Data Science.srt 21.23 KB
  3. What is Machine Learning.mp4 83.41 MB
  3. What is Machine Learning.srt 22.93 KB
  4. Machine Learning Concepts & Algorithms.mp4 77.98 MB
  4. Machine Learning Concepts & Algorithms.srt 23.56 KB
  5. What is Deep Learning.mp4 77.81 MB
  5. What is Deep Learning.srt 15.83 KB
  6. Machine Learning vs Deep Learning.mp4 75.92 MB
  6. Machine Learning vs Deep Learning.srt 17.9 KB
  20. Data Science Career
  1. Creating A Data Science Resume.mp4 37.08 MB
  1. Creating A Data Science Resume.srt 10.52 KB
  2. Data Science Cover Letter.mp4 22.96 MB
  2. Data Science Cover Letter.srt 5.99 KB
  3. How to Contact Recruiters.mp4 24.65 MB
  3. How to Contact Recruiters.srt 7.34 KB
  4. Getting Started with Freelancing.mp4 30.24 MB
  4. Getting Started with Freelancing.srt 7.08 KB
  5. Top Freelance Websites.mp4 29.55 MB
  5. Top Freelance Websites.srt 8.37 KB
  6. Personal Branding.mp4 30.49 MB
  6. Personal Branding.srt 6.42 KB
  7. Networking Do's and Don'ts.mp4 23.7 MB
  7. Networking Do's and Don'ts.srt 6.28 KB
  8. Importance of a Website.mp4 15.37 MB
  8. Importance of a Website.srt 4.79 KB
  3. Python For Data Science
  1. What is Programming.mp4 18.35 MB
  1. What is Programming.srt 9.03 KB
  10. Python Conditional Statements.mp4 54.61 MB
  10. Python Conditional Statements.srt 18.15 KB
  11. Python For Loops and While Loops.mp4 25.61 MB
  11. Python For Loops and While Loops.srt 10.74 KB
  12. Python Lists.mp4 21.44 MB
  12. Python Lists.srt 7.12 KB
  13. More about Lists.mp4 60.42 MB
  13. More about Lists.srt 19.44 KB
  14. Python Tuples.mp4 54.53 MB
  14. Python Tuples.srt 14.96 KB
  15. Python Dictionaries.mp4 104.18 MB
  15. Python Dictionaries.srt 27.72 KB
  16. Python Sets.mp4 29.43 MB
  16. Python Sets.srt 13.48 KB
  17. Compound Data Types & When to use each one.mp4 47.07 MB
  17. Compound Data Types & When to use each one.srt 17.98 KB
  18. Python Functions.mp4 62.51 MB
  18. Python Functions.srt 20.84 KB
  19. Object Oriented Programming in Python.mp4 70.25 MB
  19. Object Oriented Programming in Python.srt 25.73 KB
  2. Why Python for Data Science.mp4 16.32 MB
  2. Why Python for Data Science.srt 6.77 KB
  2.1 Importing Python Data.pdf 61.55 KB
  2.2 Python Basics.pdf 127.71 KB
  3. What is Jupyter.mp4 14.57 MB
  3. What is Jupyter.srt 5.92 KB
  3.1 Jupyter Notebook.pdf 307.15 KB
  4. What is Google Colab.mp4 8.26 MB
  4. What is Google Colab.srt 4.89 KB
  5. Python Variables, Booleans and None.mp4 38.26 MB
  5. Python Variables, Booleans and None.srt 15.22 KB
  6. Getting Started with Google Colab.mp4 35.09 MB
  6. Getting Started with Google Colab.srt 12.39 KB
  7. Python Operators.mp4 86.76 MB
  7. Python Operators.srt 31.44 KB
  8. Python Numbers & Booleans.mp4 25.61 MB
  8. Python Numbers & Booleans.srt 9.6 KB
  9. Python Strings.mp4 56.27 MB
  9. Python Strings.srt 16.14 KB
  4. Statistics for Data Science
  1. Intro To Statistics.mp4 21.24 MB
  1. Intro To Statistics.srt 11.17 KB
  2. Descriptive Statistics.mp4 21.48 MB
  2. Descriptive Statistics.srt 10.04 KB
  3. Measure of Variability.mp4 38.2 MB
  3. Measure of Variability.srt 18.19 KB
  4. Measure of Variability Continued.mp4 34.61 MB
  4. Measure of Variability Continued.srt 13.32 KB
  5. Measures of Variable Relationship.mp4 23.57 MB
  5. Measures of Variable Relationship.srt 10.72 KB
  6. Inferential Statistics.mp4 45.01 MB
  6. Inferential Statistics.srt 22.1 KB
  7. Measure of Asymmetry.mp4 6.76 MB
  7. Measure of Asymmetry.srt 2.75 KB
  8. Sampling Distribution.mp4 26.46 MB
  8. Sampling Distribution.srt 10.25 KB
  5. Probability & Hypothesis Testing
  1. What is Exactly is Probability.mp4 27.17 MB
  1. What is Exactly is Probability.srt 6.73 KB
  2. Expected Values.mp4 14.72 MB
  2. Expected Values.srt 4.11 KB
  3. Relative Frequency.mp4 32.69 MB
  3. Relative Frequency.srt 8.67 KB
  4. Hypothesis Testing Overview.mp4 60.59 MB
  4. Hypothesis Testing Overview.srt 14.58 KB
  6. NumPy Data Analysis
  1. Intro NumPy Array Data Types.mp4 34.67 MB
  1. Intro NumPy Array Data Types.srt 18.29 KB
  1.1 NumPy Basics.pdf 77.1 KB
  2. NumPy Arrays.mp4 32.33 MB
  2. NumPy Arrays.srt 11.09 KB
  3. NumPy Arrays Basics.mp4 39.98 MB
  3. NumPy Arrays Basics.srt 16.84 KB
  4. NumPy Array Indexing.mp4 34.74 MB
  4. NumPy Array Indexing.srt 14.02 KB
  5. NumPy Array Computations.mp4 16.96 MB
  5. NumPy Array Computations.srt 8.6 KB
  6. Broadcasting.mp4 17.86 MB
  6. Broadcasting.srt 6.36 KB
  7. Pandas Data Analysis
  1. Introduction to Pandas.mp4 46.83 MB
  1. Introduction to Pandas.srt 22.3 KB
  1.1 Pandas.pdf 110.18 KB
  1.2 Pandas Basics.pdf 77.06 KB
  2. Introduction to Pandas Continued.mp4 71.1 MB
  2. Introduction to Pandas Continued.srt 26.81 KB
  8. Python Data Visualization
  1. Data Visualization Overview.mp4 73.08 MB
  1. Data Visualization Overview.srt 36.78 KB
  2. Different Data Visualization Libraries in Python.mp4 15.95 MB
  2. Different Data Visualization Libraries in Python.srt 8.76 KB
  3. Python Data Visualization Implementation.mp4 27.43 MB
  3. Python Data Visualization Implementation.srt 12.44 KB
  9. Machine Learning
  1. Introduction To Machine Learning.mp4 98.71 MB
  1. Introduction To Machine Learning.srt 36.98 KB
  1.1 Supervised Learning.pdf 836.74 KB

Description



Description

Learn Python for Data Science & Machine Learning from A-Z

In this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner.

Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.

We’ll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +

NumPy — A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.
Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.

NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.

This Machine Learning with Python course dives into the basics of machine learning using Python. You’ll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!

Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

Together we’re going to give you the foundational education that you need to know not just on how to write code in Python, analyze and visualize data and utilize machine learning algorithms but also how to get paid for your newly developed programming skills.

The course covers 5 main areas:

1: PYTHON FOR DS+ML COURSE INTRO

This intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles.

Intro to Data Science + Machine Learning with Python
Data Science Industry and Marketplace
Data Science Job Opportunities
How To Get a Data Science Job
Machine Learning Concepts & Algorithms

2: PYTHON DATA ANALYSIS/VISUALIZATION

This section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.

Python Crash Course
NumPy Data Analysis
Pandas Data Analysis
Matplotlib
Seaborn
Plotly

3: MATHEMATICS FOR DATA SCIENCE

This section gives you a full introduction to the mathematics for data science such as statistics and probability.

Descriptive Statistics
Measure of Variability
Inferential Statistics
Probability
Hypothesis Testing

4: MACHINE LEARNING

This section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.

Intro to Machine Learning
Data Preprocessing
Linear Regression
Logistic Regression
K-Nearest Neighbors
Decision Trees
Ensemble Learning
Support Vector Machines
K-Means Clustering
PCA

5: STARTING A DATA SCIENCE CAREER

This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.

Creating a Resume
Creating a Cover Letter
Personal Branding
Freelancing + Freelance websites
Importance of Having a Website
Networking

By the end of the course you’ll be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.
Who this course is for:

Students who want to learn about Python for Data Science & Machine Learning

Requirements

Students should have basic computer skills
Students would benefit from having prior Python Experience but not necessary

Last Updated 1/2021

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