About Course
This course covers the fundamentals of artificial intelligence, machine learning, and data science. You will learn key concepts, techniques, and tools used in AI and data science to analyze and manipulate data, build predictive models, and apply machine learning algorithms.
Learn the fundamentals of AI, machine learning, and data science for beginners.
Requirements
- ✓Basic understanding of mathematics and statistics.
- ✓A computer with internet access.
- ✓Willingness to learn machine learning algorithms and data science techniques.
Course Curriculum
Section 1: What is YouTube
1. What is Machine Learning
2. Data Science Play Ground
3. First Image Classifier
Assessment Exercise-01
Section 2: Data Science and Machine Learning
4. Recommender System
5. Data Science vs Machine Learning vs Artificial Intelligence
6. Summarizing it all
Assessment Exercise- 02
Section 3: AI Project Life Cycle
7. AI Project Framework
8. Step-1 Problem Definition
9. Step-2 Data
10. Step-3 Evaluation
11. Step-4 Features
12. Step-5 Modelling
13. Step-5 Data Validation
14. Step-6 Course Correction
15. Tools needed for AI Project
Assessment Exercise- 03
Section 4: Python the Most Powerful Language
16. What is Programming Language
17. Python Interpreter and First Code
18. Python 3 vs Python 2
19. Formula to Learn Coding
20. Data Types and Basic Arithmetic
21. Rule of Programming
22. Mathematical Operators and Order of Precedence
Assessment Exercise- 04
Section 5: Python the Most Powerful Language Part 02
23. Variables and their BIG No No
24. Statement vs Expression
25. Augmented Assignment Operator
26. String Data Type
27. String Concatenation
28. Type Conversion
29. String Formatting
30. Indexing
31. Immutability
Assessment Exercise- 05
Section 6: Python the Most Powerful Language Part 03
32. Built in Function and Methods
33. Boolean Data Type
34. Exercise
35. Data Structure and Lists
36. Lists continued
37. Matrix from Lists
38. List Methods
39. Lists Methods 2
40. Creating Lists Programmatically
Assessment Exercise- 06
Section 7: Python the Most Powerful Language Part 04
41. Dictionary
42. Dic Key is Un Changeable
43. Most Used Methods on Dictionaries
44. Tuple Data Types
45. Sets Data Types
46. Intro to Process of Coding Conditionals
47. if else Statement
48. AND OR keywords
49. Boolean result of Different values
50. Logical Operators
Assessment Exercise- 07
Section 8: Python the Most Powerful Language Part 05
51. Identity Operator
52. for loop and Iterables
53. Nested For loop
54. Exercise for loop
55. Range Function
56. While Loop
57. Continue Break Pass Keywords
58. Exercise Draw a Shape
Assessment Exercise- 08
Section 9: Python Part-2
59. Functions
60. Why of Functions
61. Parameter vs Argument
62. Default Parameters
63. Return Keyword
64. Doc String
65. Good Programming Practices
66. args and kwargs
67. Exercise
68. Scope of a Function
69. Scope Rules-1
70. Scope Rules-2
Assessment Exercise- 09
Section 10: Python Part-3
71. Global vs nonlocal Keywords
72. Programming Best Practices-2
73. Special Functions map
74. Special Functions Filter
75. Special Functions Zip
76. Special Functions reduce
77. List Comprehension Case-12 and 3
78. Sets and Dictionary Comprehension
79. Python Modules
80. Python packages
Assessment Exercise- 10
Section 11: Environment Setup for Machine Learning Projects
81. Who is Mr. Conda
82. Tools for Data Science Environment
83. Setting Up Machine Learning Project
84. Blueprint of Machine Learning Project
85. Installing conda
86. Installing tools
87. Starting Jupyter Notebook
88. Installing for MacOS and Linux
89. Walkthrough of Jupyter notebook 1
90. Walkthrough of Jupyter notebook 2
91. Loading and Visualizing Data
92. Summing it Up
Assessment Exercise- 11
Section 12: Pandas for Data Analysis
93. Tools needed
94. Pandas and What we Will cover
95. Data Frames
96. How to Import Data
97. Describing Data
98. Data Selection
99. Data Selection 2
100. Changing Data
101. Add Remove Data
102. Manipulating Data
Assessment Exercise- 12
Section 13: NumPy
103. What and Why of Numpy
104. Numpy Array
105. Shape of Array
106. Important Functions on Arrays
107. Creating Numpy array
108. Random seed
109. Accessing Elements
110. Array Manipulation
111. Aggregations
Assessment Exercise- 13
Section 14: NumPy Part 02
112. mean variance and std
113. Dot Product vs Matrix Manipulation
114. Dot Product
115. Reshape and Transpose
116. Exercise
117. Comparison Operators
118. Sorting Arrays
119. Reading Images
Assessment Exercise- 14
Section 15: Matplotlib
120. matplotlib Intro
121. First Plot with matplotlib
122. Methods to Plot
123. Setting up Features
124. One Figure Many Plots
125. Most Used Plots Bar plot
126. Histogram
127. Four plot one figure
128. Pandas Data Frame
Assessment Exercise- 14
129. Plotting from Pandas Data Frame
Section 16: Matplotlib Part 02
130. Bar plot from Pandas Data Frame
131. pyplot vs OO methods
132. Life Cycle of OO method
133. Life Cycle of OO method Advanced
134. Customization Part-2
135. Customization Part-3
136. Figure Styling
137. Naming Entire Figure
Assessment Exercise- 15
Section 17: Scikit-Learn
138. What Actually ML Model is
139. Intro to Sklearn
140. Step-1 Getting Data Ready Split Data
141. Step-2 Choosing ML model
142. Step-3 Fit Model
143. Step-4 Evaluate Model
144. Step-5 Improve Model
145. Step-6 Save Model
Assessment Exercise- 16
Section 18: Scikit-Learn Part 02
146. What we are going to Do
147. Step-1 Getting Data Split Data
148. Step-1 Getting Data Ready Converting Part-1
149. Getting Data Ready Converting Part-2
150. Getting Data Anatomy of Conversion
151. Getting Data Second Method of Conversion
152. Getting Data Missing Values
153. Getting Data Missing Values method 2
154. Choosing Machine Learning Model
Assessment Exercise- 17
Section 19: Scikit-Learn Part 03
157. Choosing Model for Classification problem
158. Fit the Model
159. Running Prediction
160. Step-3 predict proba method
161. Step-3 Running Prediction on Regression Problem
162. Step-4 Evaluating Machine Learning Model Default Scoring
163. Step-4 What is Cross Validation
164. Step-4 Accuracy (Classification Model)
165. Step-4 Area Under the Curve Part-1
166. Step-4 Area Under the Curve Part-2
Assessment Exercise- 18
Section 20: Scikit-Learn Part 04
167. Step-4 Area Under the Curve Part-3 Plotting
168. Confusion Matrix Calculate
169. Step-4 Confusion Matrix Plot
170. Step-4 Classification Report Important concepts
171. Step-4 Classification Report Fully Explained
172. Step-4 R2 for Regression Problems
173. Step-4 Mean Absolute Error for Regression Problems
174. Step-4 Mean Square Error for Regression Problems
175. Step-4 Scoring parameters for Classification
176. Step-4 Scoring parameters for Regression
177. Step-4 Evaluation using Functions Classification
178. Step-4 Evaluation using Functions Regression
Assessment Exercise- 19
Section 21: Scikit-Learn Part 05
179. Step-5 Improving Model by Hyperparameters
180. Step-5 Improving Model by Hyperparameters manually
181. Step-5 Hyperparameters Task-1
182. Step-5 Evaluation Metrics in One Function
183. Step-5 Hyperparameters Comparison
184. Tuning Hyperparameters using RSCV
185. Tuning Hyperparameters using RSCV Part-2
186. Tuning Hyperparameters using GSCV
187. Results Comparison
Assessment Exercise- 20
Section 22: Scikit Learn Part 06
188. Save Load Model with Pickle Method-1
189. Save Load Model with joblib Method-2
190. Building Entire Model using Pipeline Part-1
191. Building Entire Model using Pipeline Part-2
192. Building Entire Model using Pipeline Part-3
193. Building Entire Model using Pipeline Part-4
Assessment Exercise- 21
Section 23: Project-1 Part 01
194. Milestone Project-1 Intro
195. Creating Project Environment
196. First 4 Steps
197. Data Features Recognition
198. Importing Tools and Libraries
199. Exploratory Data Analysis Part-1
200. Exploratory Data Analysis Part-2
Assessment Exercise- 22
Section 24: Project-1 Part 02
207. Plotting Correlation Matrix Part-2
208. Modelling Split the data
209. Choosing the Right Model
210. Improving Model
211. Plotting the Improved Model Score
212. Hyperparameter Tuning using GSCV
213. Hyperparameters for RandomForestClassifier
214. Running the model with Hyperparameters using GSCV
215. Score Comparison after tuning
Assessment Exercise- 24
Section 25: Project-1 Part 03
216. Hyperparameters Tuning Using Grid Search CV
217. Summarizing
218. What have we learnt
219. Area under the curve and Confusion Matrix
220. Plot the Classification report
221. Let's see if Cross Validation layers help us
222. Visualizing Cross Validation Score
223. Features Improvement
224. Conclusion
Assessment Exercise- 23
Section 26: Final Exam
Final Exam
What You Will Learn
- ✓Understand the basics of AI and data science, and the difference between them.
- ✓Learn to build basic machine learning models and perform data analysis.
- ✓Familiarize yourself with key AI and ML concepts like algorithms, datasets, and model evaluation.
- ✓Work with Python and popular libraries like Pandas and NumPy for data manipulation.
- ✓Learn how to apply machine learning algorithms like regression, classification, and clustering.
- ✓Understand the full life cycle of an AI project from problem definition to model deployment.