Skillshare Data Science and Machine Learning with Python Masterclass

1. Data Science and Machine Learning Course Intro
2. Data Science + Machine Learning Marketplace
3. Data Science Job Opportunities
4. Data Science Job Roles
5. What is a Data Scientist
6. How To Get a Data Science Job
7. Data Science Projects Overview
8. Why We Use Python
9. What is Data Science
10. What is Machine Learning
11. Machine Learning Concepts & Algorithms
12. Machine Learning vs Deep Learning
13. What is Deep Learning
14. What is Python Programming
15. Why Python for Data Science
16. What is Jupyter
17. What is Google Colab
18. Getting Started with Colab
19. Python Variables and Booleans
20. Python Operators
21. Python Numbers and Booleans
22. Python Strings
23. Python Conditional Statements
24. Python For Loops and While Loops
25. Python Lists
26. More About Python Lists
27. Python Tuples
28. Python Dictionaries
29. Python Sets
30. Compound Data Types
31. Python Object Oriented Programming
32. Intro to Statistics
33. Descriptive Statistics
34. Measure of Variability
35. Measure of Variability Continued
36. Measures of Variable Relationship
37. Inferential Statistics
38. Measures of Asymmetry
39. Sampling Distribution
40. What Exactly Probability
41. Expected Values
42. Relative Frequency
43. Hypothesis Testing Overview
44. NumPy Array Data Types
45. NumPy Arrays
46. NumPy Array Basics
47. NumPy Array Indexing
48. NumPy Array Computations
49. Broadcasting
50. Intro to Pandas
51. Pandas Continued
52. Data Visualization Overview
53. Different Data Visualization Libraries
54. Python Data Visualization Implementation
55. Intro to Machine Learning
56. Exploratory Data Analysis
57. Feature Scaling
58. Data Cleaning
59. Feature Engineering
60. Linear Regression Intro
61. Gradient Descent
62. Linear Regression + Correlation Methods
63. Linear Regression Implementation
64. Logistic Regression
65. KNN Overview
66. Parametic vs Non-Parametic Models
67. EDA on Iris Dataset
68. KNN Intuition
69. Implement the KNN algorithm from scratch
70. Compare the Result with Sklearn Library
71. KNN Hyperparameter tuning using the cross-validation
72. The decision boundary visualization
73. KNN - Manhattan vs Euclidean Distance
74. KNN Scaling
75. Curse of dimensionality
76. KNN use cases
77. KNN pros and cons
78. Decision Trees Section Overview
79. EDA on Adult Dataset
80. What is Entropy and Info Gain
81. The Decisions Tree ID3 Algorithm Part 1
82. The Decisions Tree ID3 Algorithm Part 2
83. The Decisions Tree ID3 Algorithm Part 3
84. Putting Everything Together
85. Evaluating our ID3 Implementation
86. Compare with Sklearn implementation
87. Visualization the Tree
88. Plot the Features Importance
89. Decision Tree Hyper-Parameters
90. Pruning
91. [Optional] Gain Ration
92. What is Ensemble Learning?
93. What is Bootstrap Sampling?
94. What is Bagging?
95. Out-of-Bag Error (OOB Error)
96. Implementing Random Forests from scratch Part 1
97. Implementing Random Forests from scratch Part 2
98. Random Forests Hyper-Parameters
99. What is Boosting?
100. AdaBoost Part 1
101. AdaBoost Part 2
102. SVM Outline
103. SVM intuition
104. Hard vs Soft Margins
105. C hyper-parameter
106. Kernel Trick
107. SVM - Kernel Types
108. SVM with Linear Dataset (Iris)
109. SVM with Non-linear Dataset
110. SVM with Regression
111. [Project] Voice Gender Recognition using SVM
112. Unsupervised Machine Learning Intro
113. Unsupervised Machine Learning Continued
114. Data Standardization
115. PCA Section Overview
116. What is PCA?
117. Covariance Matrix vs SVD
118. Image Compression Scratch
119. Data Preprocessing Scratch
120. Creating a Data Science Resume
121. Data Science Cover Letter
122. How To Contact Recruiters
123. Getting Started with Freelancing
124. Top Freelance Websites
125. Personal Branding
126. Networking Do's and Don'ts
127. Importance of a Website

©2021 | All rights reserved.