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2021 Python for Data Science & Machine Learning from A-Z
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[TutsNode.com] - 2021 Python for Data Science & Machine Learning from A-Z (Size: 7.38 GB) (Files: 429)
[TutsNode.com] - 2021 Python for Data Science & Machine Learning from A-Z
19. PCA
7. PCA - Image Compression.mp4
7. PCA - Image Compression.srt
9. PCA - Biplot and the Screen Plot.mp4
9. PCA - Biplot and the Screen Plot.srt
8. PCA Data Preprocessing.srt
4. PCA Algorithm Steps (Mathematics).srt
2. What is PCA.srt
10. PCA - Feature Scaling and Screen Plot.srt
12. PCA - Visualization.srt
11. PCA - Supervised vs Unsupervised.srt
1. PCA Section Overview.srt
5. Covariance Matrix vs SVD.srt
3. PCA Drawbacks.srt
6. PCA - Main Applications.srt
8. PCA Data Preprocessing.mp4
10. PCA - Feature Scaling and Screen Plot.mp4
12. PCA - Visualization.mp4
4. PCA Algorithm Steps (Mathematics).mp4
2. What is PCA.mp4
5. Covariance Matrix vs SVD.mp4
11. PCA - Supervised vs Unsupervised.mp4
1. PCA Section Overview.mp4
3. PCA Drawbacks.mp4
6. PCA - Main Applications.mp4
6. NumPy Data Analysis
1.1 NumPy Basics.pdf
1. Intro NumPy Array Data Types.srt
3. NumPy Arrays Basics.srt
4. NumPy Array Indexing.srt
2. NumPy Arrays.srt
5. NumPy Array Computations.srt
6. Broadcasting.srt
3. NumPy Arrays Basics.mp4
4. NumPy Array Indexing.mp4
1. Intro NumPy Array Data Types.mp4
2. NumPy Arrays.mp4
6. Broadcasting.mp4
5. NumPy Array Computations.mp4
16. Ensemble Learning and Random Forests
6. Implementing Random Forests from scratch Part 1.mp4
6. Implementing Random Forests from scratch Part 1.srt
13. AdaBoost Part 2.srt
2. What is Ensemble Learning.srt
3. What is Bootstrap Sampling.srt
5. Out-of-Bag Error (OOB Error).srt
7. Implementing Random Forests from scratch Part 2.srt
10. Random Forests Pros and Cons.srt
4. What is Bagging.srt
11. What is Boosting.srt
9. Random Forests Hyper-Parameters.srt
12. AdaBoost Part 1.srt
1. Ensemble Learning Section Overview.srt
8. Compare with sklearn implementation.srt
2. What is Ensemble Learning.mp4
13. AdaBoost Part 2.mp4
3. What is Bootstrap Sampling.mp4
7. Implementing Random Forests from scratch Part 2.mp4
5. Out-of-Bag Error (OOB Error).mp4
9. Random Forests Hyper-Parameters.mp4
11. What is Boosting.mp4
4. What is Bagging.mp4
8. Compare with sklearn implementation.mp4
12. AdaBoost Part 1.mp4
10. Random Forests Pros and Cons.mp4
1. Ensemble Learning Section Overview.mp4
3. Python For Data Science
3.1 Jupyter Notebook.pdf
2.2 Python Basics.pdf
2.1 Importing Python Data.pdf
7. Python Operators.srt
15. Python Dictionaries.srt
13. More about Lists.srt
19. Object Oriented Programming in Python.srt
18. Python Functions.srt
10. Python Conditional Statements.srt
17. Compound Data Types & When to use each one.srt
9. Python Strings.srt
5. Python Variables, Booleans and None.srt
14. Python Tuples.srt
16. Python Sets.srt
6. Getting Started with Google Colab.srt
11. Python For Loops and While Loops.srt
8. Python Numbers & Booleans.srt
1. What is Programming.srt
12. Python Lists.srt
2. Why Python for Data Science.srt
3. What is Jupyter.srt
4. What is Google Colab.srt
15. Python Dictionaries.mp4
7. Python Operators.mp4
19. Object Oriented Programming in Python.mp4
18. Python Functions.mp4
13. More about Lists.mp4
9. Python Strings.mp4
10. Python Conditional Statements.mp4
14. Python Tuples.mp4
17. Compound Data Types & When to use each one.mp4
5. Python Variables, Booleans and None.mp4
6. Getting Started with Google Colab.mp4
16. Python Sets.mp4
8. Python Numbers & Booleans.mp4
11. Python For Loops and While Loops.mp4
12. Python Lists.mp4
1. What is Programming.mp4
2. Why Python for Data Science.mp4
3. What is Jupyter.mp4
4. What is Google Colab.mp4
18. K-means
1. Unsupervised Machine Learning Intro.srt
2. Unsupervised Machine Learning Continued.srt
3. Representing Clusters.srt
1.1 Unsupervised Learning.pdf
3. Representing Clusters.mp4
1. Unsupervised Machine Learning Intro.mp4
2. Unsupervised Machine Learning Continued.mp4
15. Decision Trees
7. ID3 - Putting Everything Together.mp4
7. ID3 - Putting Everything Together.srt
3. What is Entropy and Information Gain.srt
3. What is Entropy and Information Gain.mp4
8. Evaluating our ID3 implementation.srt
13. Pruning.srt
2. EDA on Adult Dataset.srt
12. Decision Trees Hyper-parameters.srt
10. Visualizing the tree.srt
4. The Decision Tree ID3 algorithm from scratch Part 1.srt
9. Compare with Sklearn implementation.srt
5. The Decision Tree ID3 algorithm from scratch Part 2.srt
15. Decision Trees Pros and Cons.srt
16. [Project] Predict whether income exceeds $50Kyr - Overview.srt
11. Plot the features importance.srt
6. The Decision Tree ID3 algorithm from scratch Part 3.srt
1. Decision Trees Section Overview.srt
14. [Optional] Gain Ration.srt
2. EDA on Adult Dataset.mp4
8. Evaluating our ID3 implementation.mp4
13. Pruning.mp4
4. The Decision Tree ID3 algorithm from scratch Part 1.mp4
12. Decision Trees Hyper-parameters.mp4
10. Visualizing the tree.mp4
9. Compare with Sklearn implementation.mp4
5. The Decision Tree ID3 algorithm from scratch Part 2.mp4
15. Decision Trees Pros and Cons.mp4
6. The Decision Tree ID3 algorithm from scratch Part 3.mp4
11. Plot the features importance.mp4
14. [Optional] Gain Ration.mp4
1. Decision Trees Section Overview.mp4
16. [Project] Predict whether income exceeds $50Kyr - Overview.mp4
7. Pandas Data Analysis
1.1 Pandas.pdf
1.2 Pandas Basics.pdf
2. Introduction to Pandas Continued.srt
1. Introduction to Pandas.srt
2. Introduction to Pandas Continued.mp4
1. Introduction to Pandas.mp4
13. Linear and Logistic Regression
3. Linear Regression + Correlation Methods.srt
1. Linear Regression Intro.srt
2. Gradient Descent.srt
4. Linear Regression Implementation.srt
5. Logistic Regression.srt
3. Linear Regression + Correlation Methods.mp4
1. Linear Regression Intro.mp4
4. Linear Regression Implementation.mp4
2. Gradient Descent.mp4
5. Logistic Regression.mp4
9. Machine Learning
1. Introduction To Machine Learning.srt
1.1 Supervised Learning.pdf
1. Introduction To Machine Learning.mp4
8. Python Data Visualization
1. Data Visualization Overview.srt
2. Different Data Visualization Libraries in Python.srt
3. Python Data Visualization Implementation.srt
1. Data Visualization Overview.mp4
3. Python Data Visualization Implementation.mp4
2. Different Data Visualization Libraries in Python.mp4
14. K Nearest Neighbors
3. EDA on Iris Dataset.srt
3. EDA on Iris Dataset.mp4
5. Implement the KNN algorithm from scratch.srt
7. Hyperparameter tuning using the cross-validation.srt
11. Curse of dimensionality.srt
10. Feature scaling in KNN.srt
9. Manhattan vs Euclidean Distance.srt
13. KNN pros and cons.srt
8. The decision boundary visualization.srt
6. Compare the result with the sklearn library.srt
12. KNN use cases.srt
2. parametric vs non-parametric models.srt
1. KNN Overview.srt
4. The KNN Intuition.srt
7. Hyperparameter tuning using the cross-validation.mp4
5. Implement the KNN algorithm from scratch.mp4
10. Feature scaling in KNN.mp4
11. Curse of dimensionality.mp4
9. Manhattan vs Euclidean Distance.mp4
13. KNN pros and cons.mp4
12. KNN use cases.mp4
6. Compare the result with the sklearn library.mp4
8. The decision boundary visualization.mp4
2. parametric vs non-parametric models.mp4
1. KNN Overview.mp4
4. The KNN Intuition.mp4
1. Introduction
6. How To Get a Data Science Job.srt
5. What is a Data Scientist.srt
7. Data Science Projects Overview.srt
6. How To Get a Data Science Job.mp4
5. What is a Data Scientist.mp4
4. Data Science Job Roles.srt
2. Data Science + Machine Learning Marketplace.srt
3. Data Science Job Opportunities.srt
1. Who is This Course For.srt
4. Data Science Job Roles.mp4
7. Data Science Projects Overview.mp4
2. Data Science + Machine Learning Marketplace.mp4
3. Data Science Job Opportunities.mp4
1. Who is This Course For.mp4
4. Statistics for Data Science
2. Descriptive Statistics.srt
6. Inferential Statistics.srt
3. Measure of Variability.srt
7. Measure of Asymmetry.srt
4. Measure of Variability Continued.srt
1. Intro To Statistics.srt
5. Measures of Variable Relationship.srt
8. Sampling Distribution.srt
6. Inferential Statistics.mp4
3. Measure of Variability.mp4
4. Measure of Variability Continued.mp4
8. Sampling Distribution.mp4
5. Measures of Variable Relationship.mp4
2. Descriptive Statistics.mp4
1. Intro To Statistics.mp4
7. Measure of Asymmetry.mp4
17. Support Vector Machines
6. SVM - Kernel Types.srt
7. SVM with Linear Dataset (Iris).srt
3. Hard vs Soft Margins.srt
8. SVM with Non-linear Dataset.srt
5. Kernel Trick.srt
2. SVM intuition.srt
9. SVM with Regression.srt
6. SVM - Kernel Types.mp4
1. SVM Outline.srt
10. SMV - Project Overview.srt
4. C hyper-parameter.srt
8. SVM with Non-linear Dataset.mp4
7. SVM with Linear Dataset (Iris).mp4
5. Kernel Trick.mp4
3. Hard vs Soft Margins.mp4
2. SVM intuition.mp4
10. SMV - Project Overview.mp4
1. SVM Outline.mp4
9. SVM with Regression.mp4
4. C hyper-parameter.mp4
2. Data Science & Machine Learning Concepts
4. Machine Learning Concepts & Algorithms.srt
3. What is Machine Learning.srt
2. What is Data Science.srt
6. Machine Learning vs Deep Learning.srt
5. What is Deep Learning.srt
1. Why We Use Python.srt
2. What is Data Science.mp4
3. What is Machine Learning.mp4
4. Machine Learning Concepts & Algorithms.mp4
5. What is Deep Learning.mp4
6. Machine Learning vs Deep Learning.mp4
1. Why We Use Python.mp4
10. Data Loading & Exploration
1. Exploratory Data Analysis.srt
1. Exploratory Data Analysis.mp4
5. Probability & Hypothesis Testing
4. Hypothesis Testing Overview.srt
3. Relative Frequency.srt
1. What is Exactly is Probability.srt
2. Expected Values.srt
4. Hypothesis Testing Overview.mp4
3. Relative Frequency.mp4
1. What is Exactly is Probability.mp4
2. Expected Values.mp4
11. Data Cleaning
1. Feature Scaling.srt
2. Data Cleaning.srt
2. Data Cleaning.mp4
1. Feature Scaling.mp4
20. Data Science Career
1. Creating A Data Science Resume.srt
5. Top Freelance Websites.srt
3. How to Contact Recruiters.srt
4. Getting Started with Freelancing.srt
6. Personal Branding.srt
7. Networking Do's and Don'ts.srt
2. Data Science Cover Letter.srt
8. Importance of a Website.srt
1. Creating A Data Science Resume.mp4
6. Personal Branding.mp4
4. Getting Started with Freelancing.mp4
5. Top Freelance Websites.mp4
3. How to Contact Recruiters.mp4
7. Networking Do's and Don'ts.mp4
2. Data Science Cover Letter.mp4
8. Importance of a Website.mp4
12. Feature Selecting and Engineering
1. Feature Engineering.srt
1. Feature Engineering.mp4
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