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AI and Data Science for Beginners

Name: AI and Data Science for Beginners
Price: 6000 PKR
Availability: InStock

New

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

Created by PACI

Learners

0

Language

English

Course type

recorded

Level

beginner

Course promise

What you will 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

Explore

Course content

26 sections · 248 lessons

Section 1.Introduction3 lessons

1. What is Machine Learning

video

2. Data Science Play Ground

video

Details

Course description

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

Instructor

Meet your guide

P

PACI

Punjab Artificial Intelligence and Cybersecurity Initiative

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

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Python the Most Powerful Language Part 04

3. First Image CLassifier

video

Section 2. Data Science and Machine Learning4 lessons

4. Recommender Systemt using

video

5. Data Science vs Machine Learning vs Artificial Intelligence

video

6. Sumarizing it all

video

Quiz

quiz

Section 3. AI Project Life Cycle10 lessons

7. AI Project Framework

video

8. STep-1 Problem Defination

video

9. Step-2 Data

video

10. Step-3 Evaluation

video

11. Step-4 Features

video

12. Step-5 Modelling

video

13. Step-5 Data Validation

video

14. Step-6 Course Correction

video

15. Tools needed for AI Project

video

Quiz

quiz

Section 4. Python the Most Powerful Language7 lessons

16. What is Programming Language

video

17. Python Interpreter and First Code

video

18. Python 3 vs Python 2

video

19. Formula to Learn Coding

video

20. Data Types and Basic Arithmatic

video

21. Basic Arithmetic Part-2

video

Quiz

quiz

Section 5. Python the Most Powerful Language Part 029 lessons

22. Rule of Programming

video

23. Mathematical Operators and Order of Precedence

video

24. Statement vs Expression

video

25. Augmented Assignment Operator

video

26. String Data Type

video

27. String Concatenation

video

28. type Conversion

video

29. String Formatting

video

Quiz

quiz

Section 6. Python the Most Powerful Language Part 0310 lessons

30. Indexing

video

31. Immutability

video

32. Built in Function and Methods

video

33. Boolean Data Type

video

34. Exercise

video

35. Data Structor and Lists

video

36. Lists continued

video

37. Matrix from Lists

video

38. List Methods

video

Quiz

quiz

Section 7. Python the Most Powerful Language Part 0411 lessons

39. Lists Methods 2

video

40. creating Lists Programatically

video

41. Dictionary

video

42. Dic key is Un Changeable

video

43. Most Used Methods on Dictionaries

video

44. Tuple Data Types

video

45. Sets data Types

video

46. Intro to Process of Coding-Conditionals

video

47. if else Statement

video

48. AND OR keywords

video

Quiz

quiz

Section 8. Python the Most Powerful Language Part 0510 lessons

49. Boolean result of Different values

video

50. Logical Operators

video

51. Identity Operator

video

52. for loop and Iterables

video

53. Nested For loop

video

54. Exercise for loop

video

55. Range Function

video

56. While Loop

video

57. Continue Break Pass Keywords

video

Quiz

quiz

Section 9. Python Part-213 lessons

58. Exercise Draw a Shape

video

59. Functions

video

60. Why of Functions

video

61. Parameter vs Argument

video

62. Default Parameters

video

63. Return Keyword

video

64. Doc String

video

65. Good Programming Practices

video

66. args and kwargs

video

67. Exercise

video

68. Scope of a Function

video

69. Scope Rules-1

video

Quiz

quiz

Section 10. Python Part-312 lessons

70. Scope Rules-2

video

71. GLobal vs nonlocal Keywords

video

72. Programming Best Practices-2

video

73. Special Functions map

video

74. Special Functions Filter

video

75. Special Functions Zip

video

76. Special Functions reduce

video

77. List Comprehension Case-1,2 and 3

video

78. Sets and Dictionary Comprehension

video

79. Python Modules

video

80. Python packages

video

Quiz

quiz

Section 11. Environment Setup for Machine Learning Projects13 lessons

81. Tools for Data Science Environment

video

82. Who is Mr. Conda

video

83. Setting Up Machine Learning Project

video

84. Blue Print of Machine Learning Project

video

85. Installing conda

video

86. Installing tools

video

87. Starting Jupyter Notebook

video

88. Installing for MacOS and Linux

video

89. Walkthrough of Jupyter notebook 1

video

90. Walkthrough of Jupyternotebook 2

video

91. Loading and Visualizing Data

video

92. Summing it Up

video

Quiz

quiz

Section 12. Pandas for Data Analysis11 lessons

93. Tools needed

video

94. Pandas and What we Will cover

video

95. Data Frames

video

96. How to Import Data

video

97. Describing Data

video

98. Data Selection

video

99. Data Selection 2

video

100. Changing Data

video

101. Add Remove Data

video

102. Manipulating Data

video

Quiz

quiz

Section 13.NumPy10 lessons

103. What and Why of Numpy

video

104. Numpy Array

video

105. Shape of Array

video

106. Important Functions on Arrays

video

107. Creating Numpy array

video

108. random seed

video

109. Accessing Elements

video

110. Array Manipulation

video

111. Aggregations

video

Quiz

quiz

Section 14. NumPy Part 029 lessons

112. mean variance and std

video

113. Dot Product vs Matrix Manipulation

video

114. Dot Product

video

115. Reshape and Transpose

video

116. Exercise

video

117. Comparison Operators

video

118. Sorting Arrays

video

119. Reading Images

video

Quiz

quiz

Section 15. Matplotlib10 lessons

120. matplotlib Into

video

121. First Plot with matplotlib

video

122. Methods to Plot

video

123. settingup Features

video

124. One Figure Many Plots

video

125. Most Used Plots Bar plot

video

126. Histogram

video

127. Four plot one figure

video

128. Plotting from Pandas Data Frame

video

Quiz

quiz

Section 16. Matplotlib Part 028 lessons

129. Bar plot from Pandas Data Frame

video

130. pyplot vs OO methods

video

131. Life Cycle of OO method

video

132. Life Cycle of OO method Advanced

video

133. Customization Part-2

video

134. Customization Part-3

video

135. Figure Styling

video

Quiz

quiz

Section 17. Scikit-Learn10 lessons

136. Naming Entire Figure

video

137. What Actually ML Model is

video

138. Intro to Sklearn

video

139. Step-1 Getting Data Ready Split Data

video

140. Step-2 Choosing ML model

video

141. Step-3 Fit Model

video

142. Step-4 Evaluate Model

video

143. Step-5 Improve Model

video

144. Step-6 Save Model

video

Quiz

quiz

Section 18. Scikit-Learn Part 0212 lessons

145. What we are going to Do

video

146. Step-1 Getting Data Split Data

video

147. Step-1 Getting Data Ready Converting Part-1

video

148. Getting Data Ready Converting Part-2

video

149. Getting Data Anatomy of Conversion

video

150. Getting Data Second Method of Conversion

video

151. Getting Data Missing Values

video

152. Getting Data Missing Values method 2

video

153. Choosing Machine Learning Model

video

154. Using map to choose model

video

155. Step-2 How to Choose Better model

video

Quiz

quiz

Section 19. Scikit-Learn Part 0311 lessons

156. Choosing Model for Classification problem

video

157. Fit the Model

video

158. Running Prediction

video

159. Step-3 predict_proba method

video

160. Step-3 Running Prediction on Regression Problem

video

161. Step-4 Evaluating Machine Learning Model Default Scoring

video

162. Step-4 WHat is Cross Validation

video

163. Step-4 Accuracy (Classification Model)

video

164. Step-4 Area Under the Curve Part-1

video

165. Step-4 Area Under the Curve Part-2

video

Quiz

quiz

Section 20. Scikit-Learn Part 0413 lessons

166. Step-4 Area Under the Curve Part-3 Plotting

video

167. Confusion Matrix Calculate

video

168. Step-4 Confusion Matrix Plot

video

169. Step-4 Classification Report Important concepts

video

170. Step-4 Classification Report Fully Explained

video

171. Step-4 R2 for Regression Problems

video

172.Step-4 Mean Absolute Error for Regression Problems

video

173. Step-4 Mean Square Error for Regression Problems

video

174. Step-4 Scoring parameters for Classification

video

175. Step-4 Scoring parameters for Regression

video

176. Step-4 Evaluation using Functions Classification

video

177. Step-4 Evaluation using Functions Regression

video

Quiz

quiz

Section 21. Scikit-Learn Part 0510 lessons

178. Step-5 Improving Model by Hyper parameters

video

179. Step-5 Improving Model by Hyperparameters manually

video

180. Step-5 Hyperparameters Task-1

video

181. Step-5 Evaluation Metrics in One Function

video

182. Step-5 Hyperparameters Comparison

video

183. Tunning Hyperparameters using RSCV

video

184. Tunning Hyperparameters using RSCV Part-2

video

185. Tunning Hyperparameters using GSCV

video

186. Results Comparison

video

Quiz

quiz

Section 22.Scikit Learn Part 067 lessons

187. Save Load Model with Pickle Method-1

video

188. Save Load Model with joblib Method-2

video

189. Building Entire Model using Pipeline Part-1

video

190. Building Entire Model using Pipeline Part-2

video

191. Building Entire Model using Pipeline Part-3

video

192. Building Entire Model using Pipeline Part-4

video

Quiz

quiz

Section 23. Project-1 Part 0114 lessons

193. Mile Stone Project-1 Intro

video

194. Creating Project Environment

video

195. First 4 Steps

video

196. Data Features Recognition

video

197. Importing Tools and Libraries

video

198. Exploratory Data Analysis Part-1

video

199. Exploratory Data Analysis Part-2

video

200. Be Careful with Plot choice

video

201. Scatter Plot to see any Pattren

video

202. Age Distribution

video

203. Chest paint type and Target relation Part-1

video

204. Chest paint type and Target relation Part-2

video

205. Correlation Matrix Part-1

video

Quiz

quiz

Section 24. Project-1 Part 0210 lessons

206. Plotting Correlation Matrix Part-2

video

207. Modelling Split the data

video

208. Choosing the Right Model

video

209. Improving Model

video

210. Plotting the Improved Model Score

video

211. Hyperparameter Tunning using GSCV

video

212. Hyperparamters for RandomForestClassifier

video

213. Running the model with Hyperparemeters using GSCV

video

214. Score Comparison after tunning

video

Quiz

quiz

Section 25. Project-1 Part 0310 lessons

215. Hyperparameters Tunning Using Grid Search CV

video

216. Summarizing

video

217. What have we learnt

video

218. Area under the curve and Confusion Matrix

video

219. Plot the Classification report

video

220. Lets see if Cross Validation layers help us

video

221. Visualizing Cross Validation Score

video

222. Features Improvement

video

223. Conclusion

video

Quiz

quiz

Final Exam1 lessons

Final Exam

quiz

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AI and Data Science for Beginners

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Course content

Course description

Meet your guide

PACI

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