Curriculum
21 Sections
141 Lessons
Lifetime
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Section 1: 1 Introduction
7
1.1
1. Who is this course for
3 Minutes
1.2
2. Data science + machine learning marketplace
7 Minutes
1.3
3. Data science job opportunities
5 Minutes
1.4
4. Data science job roles
11 Minutes
1.5
5. What is data scientist
17 Minutes
1.6
6. How to get a data science job
19 Minutes
1.7
7. Data science projects overview
12 Minutes
Section 2: 2 Data Science and Machine Learning Concepts
6
2.1
8. Why we use python
3 Minutes
2.2
9. What is data science
13 Minutes
2.3
10. What is machine learning
15 Minutes
2.4
11. Machine learning concepts & algorithms
15 Minutes
2.5
12. What is deep learning
10 Minutes
2.6
13. Machine learning vs deep learning
11 Minutes
Section 3: 3 Python For Data Science
19
3.1
14. What is programming
6 Minutes
3.2
15. Why python for data concepts
5 Minutes
3.3
16. What is jupyter
4 Minutes
3.4
17. What is google colab
4 Minutes
3.5
18. Python variables, Booleans and none
12 Minutes
3.6
19. Getting started with google colab
9 Minutes
3.7
20. Python operators
26 Minutes
3.8
21. Python numbers & Booleans
8 Minutes
3.9
22. Python strings
13 Minutes
3.10
23. Python conditional statements
14 Minutes
3.11
24. Python for loops and while loops
8 Minutes
3.12
25. Python lists
6 Minutes
3.13
26. More about lists
15 Minutes
3.14
27. Python tuples
12 Minutes
3.15
28. Python dictionaries
21 Minutes
3.16
29. Python sets
10 Minutes
3.17
30. Compound datatypes & when to use each one
13 Minutes
3.18
31. Python functions
15 Minutes
3.19
32. Object oriented programming in python
19 Minutes
Section 4: 4 Statistics for Data Science
8
4.1
33. Intro to statistics
7 Minutes
4.2
34. Descriptive statistics
7 Minutes
4.3
35. Measure of variability
13 Minutes
4.4
36. Measure of variability continued
10 Minutes
4.5
37. Measure of variability relationship
8 Minutes
4.6
38. Inferential statistics
16 Minutes
4.7
39. Measure of asymmetry
2 Minutes
4.8
40. Sampling distribution
8 Minutes
Section 5: 5 Probability & Hypothesis Testing
4
5.1
41. What exactly is probability
4 Minutes
5.2
42. Expected values
3 Minutes
5.3
43. Relative frequency
5 Minutes
5.4
44. Hypothesis testing overview
9 Minutes
Section 6: 6 NumPy Data Analysis
6
6.1
45. Intro numpy array datatypes ( 1.1 NumPy Basics PDF )
13 Minutes
6.2
46. Numpy arrays
9 Minutes
6.3
47. Numpy arrays basics
12 Minutes
6.4
48. Numpy array Indexing
9 Minutes
6.5
49. Numpy array computations
6 Minutes
6.6
50. Broadcasting
5 Minutes
Section 7: 7 Pandas Data Analysis
2
7.1
51. Introduction to pandas (1.1 Pandas & 1.2 Pandas Basics PDF )
16 Minutes
7.2
52. Introduction to pandas continues
18 Minutes
Section 8: 8 Python Data Visualization
3
8.1
53. Data Visualization Overview
25 Minutes
8.2
54. Different Data Visualization Libraries in Python
6 Minutes
8.3
55. Python Data Visualization Implementation
9 Minutes
Section 9: 9 Machine Learning
1
9.1
56. Introduction To Machine Learning (1.1 Supervised Learning PDF )
26 Minutes
Section 10: 10 Data Loading & Exploration
1
10.1
57. Exploratory Data Analysis
13 Minutes
Section 11: 11 Data Cleaning
2
11.1
58. Feature scaling
8 Minutes
11.2
59. Data cleaning
8 Minutes
Section 12: 12 Feature Selecting and Engineering
1
12.1
60. Feature Engineering
6 Minutes
Section 13: 13 Linear and Logistic Regression
5
13.1
61. Linear Regression Intro
8 Minutes
13.2
62. Gradient Descent
6 Minutes
13.3
63. Linear Regression + Correlation Methods
27 Minutes
13.4
64. Linear Regression Implementation
5 Minutes
13.5
65. Logistic Regression
4 Minutes
Section 14: 14 K Nearest Neighbors
13
14.1
66. KNN Overview
3 Minutes
14.2
67. Parametric vs non-parametric models
4 Minutes
14.3
68. EDA on Iris Dataset
22 Minutes
14.4
69. The KNN Intuition
2 Minutes
14.5
70. Implement the KNN algorithm from scratch
12 Minutes
14.6
71. Compare the result with the sklearn library
4 Minutes
14.7
72. Hyperparameter tuning using the cross-validation
11 Minutes
14.8
73. The decision boundary visualization
5 Minutes
14.9
74. Manhattan vs Euclidean Distance
6 Minutes
14.10
75. Feature scaling in KNN
6 Minutes
14.11
76. Curse of dimensionality
8 Minutes
14.12
77. KNN use cases
4 Minutes
14.13
78. KNN pros and cons
6 Minutes
Section 15: 15 Decision Trees
16
15.1
79. Decision Trees Section Overview
5 Minutes
15.2
80. EDA on Adult Dataset
17 Minutes
15.3
81. What is Entropy and Information Gain
22 Minutes
15.4
82. The Decision Tree ID3 algorithm from scratch Part 1
12 Minutes
15.5
83. The Decision Tree ID3 algorithm from scratch Part 2
8 Minutes
15.6
84. The Decision Tree ID3 algorithm from scratch Part 3
4 Minutes
15.7
85. ID3 – Putting Everything Together
22 Minutes
15.8
86. Evaluating our ID3 implementation
17 Minutes
15.9
87. Compare with Sklearn implementation
9 Minutes
15.10
88. Visualizing the tree
11 Minutes
15.11
89. Plot the features importance
6 Minutes
15.12
90. Decision Trees Hyper-parameters
12 Minutes
15.13
91. Pruning
17 Minutes
15.14
92. [Optional] Gain Ration
3 Minutes
15.15
93. Decision Trees Pros and Cons
8 Minutes
15.16
94. [Project] Predict whether income exceeds $50Kyr – Overview
3 Minutes
Section 16: 16 Ensemble Learning and Random Forests
13
16.1
95. Ensemble Learning Section Overview
4 Minutes
16.2
96. What is Ensemble Learning
13 Minutes
16.3
97. What is Bootstrap Sampling
9 Minutes
16.4
98. What is Bagging
6 Minutes
16.5
99. Out-of-Bag Error (OOB Error)
8 Minutes
16.6
100. Implementing Random Forests from scratch Part 1
23 Minutes
16.7
101. Implementing Random Forests from scratch Part 2
6 Minutes
16.8
102. Compare with sklearn implementation
4 Minutes
16.9
103. Random Forests Hyper-Parameters
5 Minutes
16.10
104. Random Forests Pros and Cons
6 Minutes
16.11
105. What is Boosting
5 Minutes
16.12
106. AdaBoost Part 1
4 Minutes
16.13
107. AdaBoost Part 2
15 Minutes
Section 17: 17 Support Vector Machines
10
17.1
108. SVM Outline
5 Minutes
17.2
109. SVM intuition
12 Minutes
17.3
110. Hard vs Soft Margins
14 Minutes
17.4
111. C hyper-parameter
5 Minutes
17.5
112. Kernel Trick
13 Minutes
17.6
113. SVM – Kernel Types
19 Minutes
17.7
114. SVM with Linear Dataset (Iris)
14 Minutes
17.8
115. SVM with Non-linear Dataset
13 Minutes
17.9
116. SVM with Regression
6 Minutes
17.10
117. SMV – Project Overview
5 Minutes
Section 18: 18 K-means
3
18.1
118. Unsupervised Machine Learning Intro
21 Minutes
18.2
119. Unsupervised Machine Learning Continued
21 Minutes
18.3
120. Representing Clusters
19 Minutes
Section 19: 19 PCA
12
19.1
121. PCA Section Overview
5 Minutes
19.2
122. What is PCA
10 Minutes
19.3
123. PCA Drawbacks
4 Minutes
19.4
124. PCA Algorithm Steps (Mathematics)
13 Minutes
19.5
125. Covariance Matrix vs SVD
5 Minutes
19.6
126. PCA – Main Applications
3 Minutes
19.7
127. PCA – Image Compression
27 Minutes
19.8
128. PCA – Data Preprocessing
15 Minutes
19.9
129. PCA – Biplot and the Screen Plot
18 Minutes
19.10
130. PCA – Feature Scaling and Screen Plot
10 Minutes
19.11
131. PCA – Supervised vs Unsupervised
5 Minutes
19.12
132. PCA – Visualization
8 Minutes
Section 20: 20 Data Science Career
8
20.1
133. Creating A Data Science Resume
7 Minutes
20.2
134. Data Science Cover Letter
4 Minutes
20.3
135. How to Contact Recruiters
5 Minutes
20.4
136. Getting Started with Freelancing
4 Minutes
20.5
137. Top Freelance Websites
6 Minutes
20.6
138. Personal Branding
4 Minutes
20.7
139. Networking Do’s and Don’ts
4 Minutes
20.8
140. Importance of a Website
3 Minutes
Section 21: 21 Additional Content: Grand Finale
1
21.1
141. Bonus Lectures. Enjoy the Benefits
Machine Learning and Data Science with Python Masterclass
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