Self-Paced Online

AI and Data Science for Beginners

AI and Data Science for Beginners: a self-paced PACI course with 248 lessons in 26 sections and a quiz after each section.

Machine Learning
English language
Beginner

What you’ll learn

Data Science and Machine Learning
AI Project Life Cycle
Python the Most Powerful Language
Python the Most Powerful Language Part 02
Python the Most Powerful Language Part 03
Python the Most Powerful Language Part 04

About this course

AI and Data Science for Beginners is a self-paced recorded course from the Punjab Artificial Intelligence and Cybersecurity Initiative (PACI). It has 248 lessons in 26 sections (223 video lessons, 25 quizzes). What the course covers: - Data Science and Machine Learning - AI Project Life Cycle - Python the Most Powerful Language - Python the Most Powerful Language Part 02 - Python the Most Powerful Language Part 03 - Python the Most Powerful Language Part 04 - Python the Most Powerful Language Part 05 - Python Part-2 - Python Part-3 - Environment Setup for Machine Learning Projects - Pandas for Data Analysis - NumPy - NumPy Part 02 - Matplotlib - and more

Course content

26 modules · 248 lessons

01Section 1.Introduction3 lessons

1. What is Machine Learning
2. Data Science Play Ground
3. First Image CLassifier

02Section 2. Data Science and Machine Learning4 lessons

4. Recommender Systemt using
5. Data Science vs Machine Learning vs Artificial Intelligence
6. Sumarizing it all
Quiz

03Section 3. AI Project Life Cycle10 lessons

7. AI Project Framework
8. STep-1 Problem Defination
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
Quiz

04Section 4. Python the Most Powerful Language7 lessons

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 Arithmatic
21. Basic Arithmetic Part-2
Quiz

05Section 5. Python the Most Powerful Language Part 029 lessons

22. Rule of Programming
23. Mathematical Operators and Order of Precedence
24. Statement vs Expression
25. Augmented Assignment Operator
26. String Data Type
27. String Concatenation
28. type Conversion
29. String Formatting
Quiz

06Section 6. Python the Most Powerful Language Part 0310 lessons

30. Indexing
31. Immutability
32. Built in Function and Methods
33. Boolean Data Type
34. Exercise
35. Data Structor and Lists
36. Lists continued
37. Matrix from Lists
38. List Methods
Quiz

07Section 7. Python the Most Powerful Language Part 0411 lessons

39. Lists Methods 2
40. creating Lists Programatically
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
Quiz

08Section 8. Python the Most Powerful Language Part 0510 lessons

49. Boolean result of Different values
50. Logical Operators
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
Quiz

09Section 9. Python Part-213 lessons

58. Exercise Draw a Shape
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
Quiz

10Section 10. Python Part-312 lessons

70. Scope Rules-2
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-1,2 and 3
78. Sets and Dictionary Comprehension
79. Python Modules
80. Python packages
Quiz

11Section 11. Environment Setup for Machine Learning Projects13 lessons

81. Tools for Data Science Environment
82. Who is Mr. Conda
83. Setting Up Machine Learning Project
84. Blue Print 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 Jupyternotebook 2
91. Loading and Visualizing Data
92. Summing it Up
Quiz

12Section 12. Pandas for Data Analysis11 lessons

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
Quiz

13Section 13.NumPy10 lessons

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
Quiz

14Section 14. NumPy Part 029 lessons

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
Quiz

15Section 15. Matplotlib10 lessons

120. matplotlib Into
121. First Plot with matplotlib
122. Methods to Plot
123. settingup Features
124. One Figure Many Plots
125. Most Used Plots Bar plot
126. Histogram
127. Four plot one figure
128. Plotting from Pandas Data Frame
Quiz

16Section 16. Matplotlib Part 028 lessons

129. Bar plot from Pandas Data Frame
130. pyplot vs OO methods
131. Life Cycle of OO method
132. Life Cycle of OO method Advanced
133. Customization Part-2
134. Customization Part-3
135. Figure Styling
Quiz

17Section 17. Scikit-Learn10 lessons

136. Naming Entire Figure
137. What Actually ML Model is
138. Intro to Sklearn
139. Step-1 Getting Data Ready Split Data
140. Step-2 Choosing ML model
141. Step-3 Fit Model
142. Step-4 Evaluate Model
143. Step-5 Improve Model
144. Step-6 Save Model
Quiz

18Section 18. Scikit-Learn Part 0212 lessons

145. What we are going to Do
146. Step-1 Getting Data Split Data
147. Step-1 Getting Data Ready Converting Part-1
148. Getting Data Ready Converting Part-2
149. Getting Data Anatomy of Conversion
150. Getting Data Second Method of Conversion
151. Getting Data Missing Values
152. Getting Data Missing Values method 2
153. Choosing Machine Learning Model
154. Using map to choose model
155. Step-2 How to Choose Better model
Quiz

19Section 19. Scikit-Learn Part 0311 lessons

156. Choosing Model for Classification problem
157. Fit the Model
158. Running Prediction
159. Step-3 predict_proba method
160. Step-3 Running Prediction on Regression Problem
161. Step-4 Evaluating Machine Learning Model Default Scoring
162. Step-4 WHat is Cross Validation
163. Step-4 Accuracy (Classification Model)
164. Step-4 Area Under the Curve Part-1
165. Step-4 Area Under the Curve Part-2
Quiz

20Section 20. Scikit-Learn Part 0413 lessons

166. Step-4 Area Under the Curve Part-3 Plotting
167. Confusion Matrix Calculate
168. Step-4 Confusion Matrix Plot
169. Step-4 Classification Report Important concepts
170. Step-4 Classification Report Fully Explained
171. Step-4 R2 for Regression Problems
172.Step-4 Mean Absolute Error for Regression Problems
173. Step-4 Mean Square Error for Regression Problems
174. Step-4 Scoring parameters for Classification
175. Step-4 Scoring parameters for Regression
176. Step-4 Evaluation using Functions Classification
177. Step-4 Evaluation using Functions Regression
Quiz

21Section 21. Scikit-Learn Part 0510 lessons

178. Step-5 Improving Model by Hyper parameters
179. Step-5 Improving Model by Hyperparameters manually
180. Step-5 Hyperparameters Task-1
181. Step-5 Evaluation Metrics in One Function
182. Step-5 Hyperparameters Comparison
183. Tunning Hyperparameters using RSCV
184. Tunning Hyperparameters using RSCV Part-2
185. Tunning Hyperparameters using GSCV
186. Results Comparison
Quiz

22Section 22.Scikit Learn Part 067 lessons

187. Save Load Model with Pickle Method-1
188. Save Load Model with joblib Method-2
189. Building Entire Model using Pipeline Part-1
190. Building Entire Model using Pipeline Part-2
191. Building Entire Model using Pipeline Part-3
192. Building Entire Model using Pipeline Part-4
Quiz

23Section 23. Project-1 Part 0114 lessons

193. Mile Stone Project-1 Intro
194. Creating Project Environment
195. First 4 Steps
196. Data Features Recognition
197. Importing Tools and Libraries
198. Exploratory Data Analysis Part-1
199. Exploratory Data Analysis Part-2
200. Be Careful with Plot choice
201. Scatter Plot to see any Pattren
202. Age Distribution
203. Chest paint type and Target relation Part-1
204. Chest paint type and Target relation Part-2
205. Correlation Matrix Part-1
Quiz

24Section 24. Project-1 Part 0210 lessons

206. Plotting Correlation Matrix Part-2
207. Modelling Split the data
208. Choosing the Right Model
209. Improving Model
210. Plotting the Improved Model Score
211. Hyperparameter Tunning using GSCV
212. Hyperparamters for RandomForestClassifier
213. Running the model with Hyperparemeters using GSCV
214. Score Comparison after tunning
Quiz

25Section 25. Project-1 Part 0310 lessons

215. Hyperparameters Tunning Using Grid Search CV
216. Summarizing
217. What have we learnt
218. Area under the curve and Confusion Matrix
219. Plot the Classification report
220. Lets see if Cross Validation layers help us
221. Visualizing Cross Validation Score
222. Features Improvement
223. Conclusion
Quiz

26Final Exam1 lessons

Final Exam

Your instructor

PACI

Punjab Artificial Intelligence and Cybersecurity Initiative

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PACI (Punjab Artificial Intelligence and Cybersecurity Initiative) publishes recorded courses in artificial intelligence, cybersecurity, web development and data skills.

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