AI and Data Science for Beginners
Build a strong foundation in AI and data science. Cover key topics like machine learning algorithms, data manipulation, and model building using Python tools.
Course Level
Beginner to Advanced
Certification
Complete All Assessments
Career Impact
High Growth Potential
Course Overview
Build a strong foundation in AI and data science. Cover key topics like machine learning algorithms, data manipulation, and model building using Python tools.
What You'll Learn
Differentiate AI, ML, and data science.
Build simple ML models.
Use Python for data tasks.
Apply regression and classification.
Follow AI project cycles.
Handle data preprocessing.
Evaluate model performance.
Visualize data insights.
Deploy basic models.
Explore ethical AI considerations.
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Detailed Curriculum
Section 1: AI Overview
1. Machine Learning Intro
2. Data Playground
3. Image Classifier
Assessment Quiz 1
Section 2: DS and ML
4. Recommender Systems
5. DS vs ML vs AI
6. Summary
Assessment Quiz 2
Section 3: Project Cycle
7. AI Framework
8. Problem Definition
9. Data Handling
10. Evaluation Metrics
11. Feature Selection
12. Modeling Steps
13. Validation
14. Corrections
15. Tools Overview
Assessment Quiz 3
Section 4: Python Basics
16. Programming Languages
17. First Python Code
18. Python Versions
19. Learning Formula
20. Data Types
21. Programming Rules
22. Operators
Assessment Quiz 4
Section 5: Python Continued
23. Variables
24. Statements
25. Augmented Operators
26. Strings
27. Concatenation
28. Conversion
29. Formatting
30. Indexing
31. Immutability
Assessment Quiz 5
Section 6: Data Structures
32. Functions and Methods
33. Booleans
34. Exercises
35. Lists Intro
36. Lists Advanced
37. Matrices
38. List Methods
39. More Methods
40. Programmatic Lists
Assessment Quiz 6
Section 7: Advanced Python
41. Dictionaries
42. Immutable Keys
43. Dict Methods
44. Tuples
45. Sets
46. Conditionals
47. If-Else
48. Logical Operators
49. Boolean Values
50. Operators
Assessment Quiz 7
Section 8: Loops and Controls
51. Identity Ops
52. For Loops
53. Nested Loops
54. Loop Exercises
55. Range Function
56. While Loops
57. Control Keywords
58. Shape Exercise
Assessment Quiz 8
Section 9: Functions
59. Function Basics
60. Why Functions
61. Params vs Args
62. Defaults
63. Returns
64. Docstrings
65. Practices
66. Args Kwargs
67. Exercises
68. Scope
69. Scope Rules 1
70. Scope Rules 2
Assessment Quiz 9
Section 10: Special Functions
71. Global Nonlocal
72. Practices 2
73. Map Function
74. Filter
75. Zip
76. Reduce
77. List Comps
78. Set Dict Comps
79. Modules
80. Packages
Assessment Quiz 10
Section 11: Environment Setup
81. Conda Intro
82. DS Tools
83. Project Setup
84. Blueprint
85. Conda Install
86. Tool Installs
87. Jupyter Start
88. Mac/Linux Install
89. Jupyter Walkthrough 1
90. Jupyter Walkthrough 2
91. Data Loading
92. Summary
Assessment Quiz 11
Section 12: Pandas Analysis
93. Pandas Intro
94. Coverage
95. DataFrames
96. Importing Data
97. Describing
98. Selection 1
99. Selection 2
100. Changing Data
101. Add/Remove
102. Manipulation
Assessment Quiz 12
Section 13: NumPy Basics
103. NumPy Why
104. Arrays
105. Shapes
106. Array Functions
107. Creating Arrays
108. Random Seeds
109. Accessing
110. Manipulation
111. Aggregations
Assessment Quiz 13
Section 14: NumPy Advanced
112. Stats Functions
113. Dot vs Matrix
114. Dot Product
115. Reshape Transpose
116. Exercises
117. Comparisons
118. Sorting
119. Image Reading
Assessment Quiz 14
Section 15: Matplotlib Plotting
120. Matplotlib Intro
121. First Plots
122. Plot Methods
123. Features Setup
124. Multi-Plots
125. Bar Plots
126. Histograms
127. Four Plots
128. Pandas Frames
Assessment Quiz 15
Section 16: Matplotlib Advanced
129. Pandas Plotting
130. Bar from Pandas
131. Pyplot vs OO
132. OO Life Cycle
133. OO Advanced
134. Customization 2
135. Customization 3
136. Styling
137. Figure Naming
Assessment Quiz 16
Section 17: Scikit-Learn Intro
138. ML Models
139. Sklearn Overview
140. Data Prep Split
141. Model Choice
142. Fitting
143. Evaluation
144. Improvement
145. Saving
Assessment Quiz 17
Section 18: Scikit-Learn Data Prep
146. Plan Overview
147. Data Split
148. Conversion 1
149. Conversion 2
150. Conversion Anatomy
151. Second Conversion
152. Missing Values
153. Missing Method 2
154. Model Selection
Assessment Quiz 18
Section 19: Scikit-Learn Modeling
155. Classification Models
156. Model Fitting
157. Predictions
158. Proba Method
159. Regression Predictions
160. Scoring Defaults
161. Cross Validation
162. Accuracy Metrics
163. AUC Part 1
164. AUC Part 2
Assessment Quiz 19
Section 20: Evaluation Metrics
165. AUC Part 3
166. Confusion Matrix
167. Matrix Plot
168. Report Concepts
169. Report Explained
170. R2 for Regression
171. MAE for Regression
172. MSE for Regression
173. Classification Params
174. Regression Params
175. Function Evaluation Class
176. Function Evaluation Reg
Assessment Quiz 20
Section 21: Hyperparameters
177. Hyperparam Improvement
178. Manual Tuning
179. Task 1
180. Metrics Function
181. Comparison
182. RSCV Tuning
183. RSCV Part 2
184. GSCV Tuning
185. Results Compare
Assessment Quiz 21
Section 22: Model Saving
186. Pickle Save Load
187. Joblib Method
188. Pipeline Part 1
189. Pipeline Part 2
190. Pipeline Part 3
191. Pipeline Part 4
Assessment Quiz 22
Section 23: Project 1 Basics
192. Project Intro
193. Environment Creation
194. Initial Steps
195. Feature Recognition
196. Tools Import
197. EDA Part 1
198. EDA Part 2
Assessment Quiz 23
Section 24: Project 1 Analysis
199. Correlation Matrix 1
200. Matrix Part 2
201. Data Split
202. Model Choice
203. Model Improvement
204. Score Plotting
205. GSCV Tuning
206. RFC Hyperparams
207. Model with Params
208. Tuning Comparison
Assessment Quiz 24
Section 25: Project 1 Evaluation
209. Grid Search Tuning
210. Summary
211. Learnings
212. AUC and Matrix
213. Report Plot
214. CV Layers
215. Score Visualization
216. Feature Improvement
217. Conclusion
Assessment Quiz 25
Section 26: Course Review
Comprehensive Assessment
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