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| 1.1 NumPy Basics.pdf |
77.10Кб |
| 1.1 Pandas.pdf |
110.18Кб |
| 1.1 Supervised Learning.pdf |
836.74Кб |
| 1.1 Unsupervised Learning.pdf |
636.55Кб |
| 1.2 Pandas Basics.pdf |
77.06Кб |
| 1. Creating A Data Science Resume.mp4 |
37.08Мб |
| 1. Creating A Data Science Resume.srt |
10.52Кб |
| 1. Data Visualization Overview.mp4 |
73.08Мб |
| 1. Data Visualization Overview.srt |
36.78Кб |
| 1. Decision Trees Section Overview.mp4 |
16.46Мб |
| 1. Decision Trees Section Overview.srt |
5.60Кб |
| 1. Ensemble Learning Section Overview.mp4 |
16.07Мб |
| 1. Ensemble Learning Section Overview.srt |
5.17Кб |
| 1. Exploratory Data Analysis.mp4 |
50.56Мб |
| 1. Exploratory Data Analysis.srt |
19.07Кб |
| 1. Feature Engineering.mp4 |
18.41Мб |
| 1. Feature Engineering.srt |
9.42Кб |
| 1. Feature Scaling.mp4 |
19.38Мб |
| 1. Feature Scaling.srt |
11.58Кб |
| 1. Introduction To Machine Learning.mp4 |
98.71Мб |
| 1. Introduction To Machine Learning.srt |
36.98Кб |
| 1. Introduction to Pandas.mp4 |
46.83Мб |
| 1. Introduction to Pandas.srt |
22.30Кб |
| 1. Intro NumPy Array Data Types.mp4 |
34.67Мб |
| 1. Intro NumPy Array Data Types.srt |
18.29Кб |
| 1. Intro To Statistics.mp4 |
21.24Мб |
| 1. Intro To Statistics.srt |
11.17Кб |
| 1. KNN Overview.mp4 |
12.89Мб |
| 1. KNN Overview.srt |
4.27Кб |
| 1. Linear Regression Intro.mp4 |
30.80Мб |
| 1. Linear Regression Intro.srt |
12.16Кб |
| 1. PCA Section Overview.mp4 |
31.77Мб |
| 1. PCA Section Overview.srt |
7.06Кб |
| 1. SVM Outline.mp4 |
35.31Мб |
| 1. SVM Outline.srt |
7.40Кб |
| 1. Unsupervised Machine Learning Intro.mp4 |
100.92Мб |
| 1. Unsupervised Machine Learning Intro.srt |
29.36Кб |
| 1. What is Exactly is Probability.mp4 |
27.17Мб |
| 1. What is Exactly is Probability.srt |
6.73Кб |
| 1. What is Programming.mp4 |
18.35Мб |
| 1. What is Programming.srt |
9.03Кб |
| 1. Who is This Course For.mp4 |
17.16Мб |
| 1. Who is This Course For.srt |
3.95Кб |
| 1. Why We Use Python.mp4 |
13.51Мб |
| 1. Why We Use Python.srt |
4.91Кб |
| 10. Feature scaling in KNN.mp4 |
49.39Мб |
| 10. Feature scaling in KNN.srt |
8.06Кб |
| 10. PCA - Feature Scaling and Screen Plot.mp4 |
68.20Мб |
| 10. PCA - Feature Scaling and Screen Plot.srt |
14.40Кб |
| 10. Python Conditional Statements.mp4 |
54.61Мб |
| 10. Python Conditional Statements.srt |
18.15Кб |
| 10. Random Forests Pros and Cons.mp4 |
19.69Мб |
| 10. Random Forests Pros and Cons.srt |
7.85Кб |
| 10. SMV - Project Overview.mp4 |
39.61Мб |
| 10. SMV - Project Overview.srt |
6.23Кб |
| 10. Visualizing the tree.mp4 |
68.17Мб |
| 10. Visualizing the tree.srt |
15.00Кб |
| 11. Curse of dimensionality.mp4 |
45.99Мб |
| 11. Curse of dimensionality.srt |
9.60Кб |
| 11. PCA - Supervised vs Unsupervised.mp4 |
35.79Мб |
| 11. PCA - Supervised vs Unsupervised.srt |
7.12Кб |
| 11. Plot the features importance.mp4 |
31.67Мб |
| 11. Plot the features importance.srt |
7.75Кб |
| 11. Python For Loops and While Loops.mp4 |
25.61Мб |
| 11. Python For Loops and While Loops.srt |
10.74Кб |
| 11. What is Boosting.mp4 |
35.44Мб |
| 11. What is Boosting.srt |
6.86Кб |
| 12. AdaBoost Part 1.mp4 |
25.53Мб |
| 12. AdaBoost Part 1.srt |
5.50Кб |
| 12. Decision Trees Hyper-parameters.mp4 |
81.27Мб |
| 12. Decision Trees Hyper-parameters.srt |
16.12Кб |
| 12. KNN use cases.mp4 |
28.92Мб |
| 12. KNN use cases.srt |
4.85Кб |
| 12. PCA - Visualization.mp4 |
68.02Мб |
| 12. PCA - Visualization.srt |
10.96Кб |
| 12. Python Lists.mp4 |
21.44Мб |
| 12. Python Lists.srt |
7.12Кб |
| 13. AdaBoost Part 2.mp4 |
85.94Мб |
| 13. AdaBoost Part 2.srt |
20.87Кб |
| 13. KNN pros and cons.mp4 |
30.45Мб |
| 13. KNN pros and cons.srt |
7.75Кб |
| 13. More about Lists.mp4 |
60.42Мб |
| 13. More about Lists.srt |
19.44Кб |
| 13. Pruning.mp4 |
112.97Мб |
| 13. Pruning.srt |
24.35Кб |
| 14. [Optional] Gain Ration.mp4 |
19.18Мб |
| 14. [Optional] Gain Ration.srt |
3.70Кб |
| 14. Python Tuples.mp4 |
54.53Мб |
| 14. Python Tuples.srt |
14.96Кб |
| 15. Decision Trees Pros and Cons.mp4 |
47.74Мб |
| 15. Decision Trees Pros and Cons.srt |
10.60Кб |
| 15. Python Dictionaries.mp4 |
104.18Мб |
| 15. Python Dictionaries.srt |
27.72Кб |
| 16. [Project] Predict whether income exceeds $50Kyr - Overview.mp4 |
15.11Мб |
| 16. [Project] Predict whether income exceeds $50Kyr - Overview.srt |
3.60Кб |
| 16. Python Sets.mp4 |
29.43Мб |
| 16. Python Sets.srt |
13.48Кб |
| 17. Compound Data Types & When to use each one.mp4 |
47.07Мб |
| 17. Compound Data Types & When to use each one.srt |
17.98Кб |
| 18. Python Functions.mp4 |
62.51Мб |
| 18. Python Functions.srt |
20.84Кб |
| 19. Object Oriented Programming in Python.mp4 |
70.25Мб |
| 19. Object Oriented Programming in Python.srt |
25.73Кб |
| 2.1 Importing Python Data.pdf |
61.55Кб |
| 2.2 Python Basics.pdf |
127.71Кб |
| 2. Data Cleaning.mp4 |
30.21Мб |
| 2. Data Cleaning.srt |
11.53Кб |
| 2. Data Science + Machine Learning Marketplace.mp4 |
46.94Мб |
| 2. Data Science + Machine Learning Marketplace.srt |
10.40Кб |
| 2. Data Science Cover Letter.mp4 |
22.96Мб |
| 2. Data Science Cover Letter.srt |
5.99Кб |
| 2. Descriptive Statistics.mp4 |
21.48Мб |
| 2. Descriptive Statistics.srt |
10.04Кб |
| 2. Different Data Visualization Libraries in Python.mp4 |
15.95Мб |
| 2. Different Data Visualization Libraries in Python.srt |
8.76Кб |
| 2. EDA on Adult Dataset.mp4 |
123.19Мб |
| 2. EDA on Adult Dataset.srt |
23.77Кб |
| 2. Expected Values.mp4 |
14.72Мб |
| 2. Expected Values.srt |
4.11Кб |
| 2. Gradient Descent.mp4 |
15.93Мб |
| 2. Gradient Descent.srt |
8.44Кб |
| 2. Introduction to Pandas Continued.mp4 |
71.10Мб |
| 2. Introduction to Pandas Continued.srt |
26.81Кб |
| 2. NumPy Arrays.mp4 |
32.33Мб |
| 2. NumPy Arrays.srt |
11.09Кб |
| 2. parametric vs non-parametric models.mp4 |
15.63Мб |
| 2. parametric vs non-parametric models.srt |
4.73Кб |
| 2. SVM intuition.mp4 |
48.86Мб |
| 2. SVM intuition.srt |
16.01Кб |
| 2. Unsupervised Machine Learning Continued.mp4 |
83.13Мб |
| 2. Unsupervised Machine Learning Continued.srt |
29.19Кб |
| 2. What is Data Science.mp4 |
87.99Мб |
| 2. What is Data Science.srt |
21.23Кб |
| 2. What is Ensemble Learning.mp4 |
91.97Мб |
| 2. What is Ensemble Learning.srt |
17.42Кб |
| 2. What is PCA.mp4 |
47.26Мб |
| 2. What is PCA.srt |
14.59Кб |
| 2. Why Python for Data Science.mp4 |
16.32Мб |
| 2. Why Python for Data Science.srt |
6.77Кб |
| 3.1 Jupyter Notebook.pdf |
307.15Кб |
| 3. Data Science Job Opportunities.mp4 |
29.42Мб |
| 3. Data Science Job Opportunities.srt |
6.86Кб |
| 3. EDA on Iris Dataset.mp4 |
161.88Мб |
| 3. EDA on Iris Dataset.srt |
31.65Кб |
| 3. Hard vs Soft Margins.mp4 |
65.64Мб |
| 3. Hard vs Soft Margins.srt |
18.92Кб |
| 3. How to Contact Recruiters.mp4 |
24.65Мб |
| 3. How to Contact Recruiters.srt |
7.34Кб |
| 3. Linear Regression + Correlation Methods.mp4 |
110.38Мб |
| 3. Linear Regression + Correlation Methods.srt |
38.61Кб |
| 3. Measure of Variability.mp4 |
38.20Мб |
| 3. Measure of Variability.srt |
18.19Кб |
| 3. NumPy Arrays Basics.mp4 |
39.98Мб |
| 3. NumPy Arrays Basics.srt |
16.84Кб |
| 3. PCA Drawbacks.mp4 |
19.44Мб |
| 3. PCA Drawbacks.srt |
4.90Кб |
| 3. Python Data Visualization Implementation.mp4 |
27.43Мб |
| 3. Python Data Visualization Implementation.srt |
12.44Кб |
| 3. Relative Frequency.mp4 |
32.69Мб |
| 3. Relative Frequency.srt |
8.67Кб |
| 3. Representing Clusters.mp4 |
109.62Мб |
| 3. Representing Clusters.srt |
28.32Кб |
| 3. What is Bootstrap Sampling.mp4 |
55.88Мб |
| 3. What is Bootstrap Sampling.srt |
11.13Кб |
| 3. What is Entropy and Information Gain.mp4 |
136.08Мб |
| 3. What is Entropy and Information Gain.srt |
29.33Кб |
| 3. What is Jupyter.mp4 |
14.57Мб |
| 3. What is Jupyter.srt |
5.92Кб |
| 3. What is Machine Learning.mp4 |
83.41Мб |
| 3. What is Machine Learning.srt |
22.93Кб |
| 4. C hyper-parameter.mp4 |
21.06Мб |
| 4. C hyper-parameter.srt |
5.64Кб |
| 4. Data Science Job Roles.mp4 |
79.80Мб |
| 4. Data Science Job Roles.srt |
15.73Кб |
| 4. Getting Started with Freelancing.mp4 |
30.24Мб |
| 4. Getting Started with Freelancing.srt |
7.08Кб |
| 4. Hypothesis Testing Overview.mp4 |
60.59Мб |
| 4. Hypothesis Testing Overview.srt |
14.58Кб |
| 4. Linear Regression Implementation.mp4 |
17.86Мб |
| 4. Linear Regression Implementation.srt |
6.88Кб |
| 4. Machine Learning Concepts & Algorithms.mp4 |
77.98Мб |
| 4. Machine Learning Concepts & Algorithms.srt |
23.56Кб |
| 4. Measure of Variability Continued.mp4 |
34.61Мб |
| 4. Measure of Variability Continued.srt |
13.32Кб |
| 4. NumPy Array Indexing.mp4 |
34.74Мб |
| 4. NumPy Array Indexing.srt |
14.02Кб |
| 4. PCA Algorithm Steps (Mathematics).mp4 |
57.73Мб |
| 4. PCA Algorithm Steps (Mathematics).srt |
18.59Кб |
| 4. The Decision Tree ID3 algorithm from scratch Part 1.mp4 |
85.27Мб |
| 4. The Decision Tree ID3 algorithm from scratch Part 1.srt |
14.91Кб |
| 4. The KNN Intuition.mp4 |
8.09Мб |
| 4. The KNN Intuition.srt |
3.06Кб |
| 4. What is Bagging.mp4 |
29.48Мб |
| 4. What is Bagging.srt |
7.75Кб |
| 4. What is Google Colab.mp4 |
8.26Мб |
| 4. What is Google Colab.srt |
4.89Кб |
| 5. Covariance Matrix vs SVD.mp4 |
38.74Мб |
| 5. Covariance Matrix vs SVD.srt |
6.54Кб |
| 5. Implement the KNN algorithm from scratch.mp4 |
86.97Мб |
| 5. Implement the KNN algorithm from scratch.srt |
17.14Кб |
| 5. Kernel Trick.mp4 |
77.05Мб |
| 5. Kernel Trick.srt |
18.19Кб |
| 5. Logistic Regression.mp4 |
8.90Мб |
| 5. Logistic Regression.srt |
4.93Кб |
| 5. Measures of Variable Relationship.mp4 |
23.57Мб |
| 5. Measures of Variable Relationship.srt |
10.72Кб |
| 5. NumPy Array Computations.mp4 |
16.96Мб |
| 5. NumPy Array Computations.srt |
8.60Кб |
| 5. Out-of-Bag Error (OOB Error).mp4 |
42.03Мб |
| 5. Out-of-Bag Error (OOB Error).srt |
9.97Кб |
| 5. Python Variables, Booleans and None.mp4 |
38.26Мб |
| 5. Python Variables, Booleans and None.srt |
15.22Кб |
| 5. The Decision Tree ID3 algorithm from scratch Part 2.mp4 |
63.96Мб |
| 5. The Decision Tree ID3 algorithm from scratch Part 2.srt |
10.62Кб |
| 5. Top Freelance Websites.mp4 |
29.55Мб |
| 5. Top Freelance Websites.srt |
8.37Кб |
| 5. What is a Data Scientist.mp4 |
127.47Мб |
| 5. What is a Data Scientist.srt |
26.86Кб |
| 5. What is Deep Learning.mp4 |
77.81Мб |
| 5. What is Deep Learning.srt |
15.83Кб |
| 6. Broadcasting.mp4 |
17.86Мб |
| 6. Broadcasting.srt |
6.36Кб |
| 6. Compare the result with the sklearn library.mp4 |
24.57Мб |
| 6. Compare the result with the sklearn library.srt |
5.10Кб |
| 6. Getting Started with Google Colab.mp4 |
35.09Мб |
| 6. Getting Started with Google Colab.srt |
12.39Кб |
| 6. How To Get a Data Science Job.mp4 |
131.19Мб |
| 6. How To Get a Data Science Job.srt |
30.62Кб |
| 6. Implementing Random Forests from scratch Part 1.mp4 |
202.55Мб |
| 6. Implementing Random Forests from scratch Part 1.srt |
30.12Кб |
| 6. Inferential Statistics.mp4 |
45.01Мб |
| 6. Inferential Statistics.srt |
22.10Кб |
| 6. Machine Learning vs Deep Learning.mp4 |
75.92Мб |
| 6. Machine Learning vs Deep Learning.srt |
17.90Кб |
| 6. PCA - Main Applications.mp4 |
10.05Мб |
| 6. PCA - Main Applications.srt |
3.90Кб |
| 6. Personal Branding.mp4 |
30.49Мб |
| 6. Personal Branding.srt |
6.42Кб |
| 6. SVM - Kernel Types.mp4 |
126.38Мб |
| 6. SVM - Kernel Types.srt |
26.75Кб |
| 6. The Decision Tree ID3 algorithm from scratch Part 3.mp4 |
33.41Мб |
| 6. The Decision Tree ID3 algorithm from scratch Part 3.srt |
5.75Кб |
| 7. Data Science Projects Overview.mp4 |
79.48Мб |
| 7. Data Science Projects Overview.srt |
19.09Кб |
| 7. Hyperparameter tuning using the cross-validation.mp4 |
90.30Мб |
| 7. Hyperparameter tuning using the cross-validation.srt |
14.66Кб |
| 7. ID3 - Putting Everything Together.mp4 |
182.48Мб |
| 7. ID3 - Putting Everything Together.srt |
31.03Кб |
| 7. Implementing Random Forests from scratch Part 2.mp4 |
50.50Мб |
| 7. Implementing Random Forests from scratch Part 2.srt |
8.27Кб |
| 7. Measure of Asymmetry.mp4 |
6.76Мб |
| 7. Measure of Asymmetry.srt |
2.75Кб |
| 7. Networking Do's and Don'ts.mp4 |
23.70Мб |
| 7. Networking Do's and Don'ts.srt |
6.28Кб |
| 7. PCA - Image Compression.mp4 |
249.92Мб |
| 7. PCA - Image Compression.srt |
39.37Кб |
| 7. Python Operators.mp4 |
86.76Мб |
| 7. Python Operators.srt |
31.44Кб |
| 7. SVM with Linear Dataset (Iris).mp4 |
101.56Мб |
| 7. SVM with Linear Dataset (Iris).srt |
19.85Кб |
| 8. Compare with sklearn implementation.mp4 |
27.65Мб |
| 8. Compare with sklearn implementation.srt |
4.94Кб |
| 8. Evaluating our ID3 implementation.mp4 |
121.94Мб |
| 8. Evaluating our ID3 implementation.srt |
24.54Кб |
| 8. Importance of a Website.mp4 |
15.37Мб |
| 8. Importance of a Website.srt |
4.79Кб |
| 8. PCA Data Preprocessing.mp4 |
120.46Мб |
| 8. PCA Data Preprocessing.srt |
21.05Кб |
| 8. Python Numbers & Booleans.mp4 |
25.61Мб |
| 8. Python Numbers & Booleans.srt |
9.60Кб |
| 8. Sampling Distribution.mp4 |
26.46Мб |
| 8. Sampling Distribution.srt |
10.25Кб |
| 8. SVM with Non-linear Dataset.mp4 |
111.55Мб |
| 8. SVM with Non-linear Dataset.srt |
18.38Кб |
| 8. The decision boundary visualization.mp4 |
16.94Мб |
| 8. The decision boundary visualization.srt |
7.07Кб |
| 9. Compare with Sklearn implementation.mp4 |
65.58Мб |
| 9. Compare with Sklearn implementation.srt |
12.27Кб |
| 9. Manhattan vs Euclidean Distance.mp4 |
30.49Мб |
| 9. Manhattan vs Euclidean Distance.srt |
7.75Кб |
| 9. PCA - Biplot and the Screen Plot.mp4 |
135.60Мб |
| 9. PCA - Biplot and the Screen Plot.srt |
26.37Кб |
| 9. Python Strings.mp4 |
56.27Мб |
| 9. Python Strings.srt |
16.14Кб |
| 9. Random Forests Hyper-Parameters.mp4 |
39.67Мб |
| 9. Random Forests Hyper-Parameters.srt |
5.97Кб |
| 9. SVM with Regression.mp4 |
25.00Мб |
| 9. SVM with Regression.srt |
8.00Кб |