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Machine Learning, AI & Data Science
Section 1: Introduction
How to Use LMS & Start Learning? (6:19)
1.What is Machine Learning (7:20)
2.Data Science Play Ground (6:07)
3.First Image CLassifier (6:55)
Resources
Assessment Exercise-01
Section 2: Data Science and Machine Learning
5.Data Science vs Machine Learning vs Artificial Intelligence (11:20)
4. Recommender Systemt using (4:06)
6.Sumarizing it all (2:47)
Assessment Exercise- 02
Section 3: AI Project Life Cycle
7.AI Project Framework (11:40)
8.STep 1 Problem Defination (11:54)
9.Step 2 Data (5:35)
10.Step 3 Evaluation (3:16)
11.Step 4 Features (5:52)
12. Step 5 Modelling (10:05)
13.Step 5 Data Validation (8:39)
14.Step 6 Course Correction (4:27)
15.Tools needed for AI Project (5:39)
Assessment Exercise-03
Section 4: Python the Most Powerful Language
16.What is Programming Language (6:09)
17.Python Interpreter and First Code (7:45)
18.Python 3 vs Python 2 (4:36)
19.Formula to Learn Coding (4:12)
20.Data Types and Basic Arithmatic (10:25)
21.Basic Arithmetic Part 2 (5:59)
22.Rule of Programming (4:50)
Assessment Exercise-04
Section 5: Python the Most Powerful Language Part 02
23.Mathematical Operators and Order of Precedence (5:05)
24.Variables and their BIG No No (10:00)
25.Statement vs Expression (3:18)
26.Augmented Assignment Operator (3:14)
27.String Data Type (6:01)
28.String Concatenation (9:08)
29.type Conversion (4:53)
30.String Formatting (7:15)
31.Indexing (9:55)
Assessment Exercise-05
Section 6: Python the Most Powerful Language Part 03
32.Immutability (3:39)
33.Built in Function and Methods (6:31)
34.Boolean Data Type (2:54)
35.Exercise (3:52)
36.Data Structor and Lists (8:24)
37.Lists continued (10:04)
38.Matrix from Lists (3:40)
39.List Methods (8:19)
40.Lists Methods 2 (9:01)
Assessment Exercise-06
Section 7: Python the Most Powerful Language Part 04
41.creating Lists Programatically (5:03)
42.Dictionary (8:44)
43.Dic key is Un Changeable (5:11)
44.Most Used Methods on Dictionaries (7:40)
45.Tuple Data Types (9:11)
46.Sets data Types (8:19)
47.Intro to Process of Coding Conditionals (1:56)
48.if else Statement (11:02)
49.AND OR keywords (6:11)
50.Boolean result of Different values (4:54)
Assessment Exercise-07
Section 8: Python the Most Powerful Language Part 05
51.Logical Operators (7:53)
52.Identity Operator (4:56)
53.for loop and Iterables (11:20)
54.Nested For loop (5:39)
55.Exercise for loop (3:45)
56.Range Function (7:54)
57.While Loop (7:15)
58.Continue Break Pass Keywords (6:25)
59.Exercise Draw a Shape (3:34)
Assessment Exercise-08
Section 9: Python Part-2
60.Functions (5:49)
61.Why of Functions (4:51)
62.Parameter vs Argument (6:57)
63. Default Parameters (7:09)
64.Return Keyword (5:51)
65.Doc String (7:28)
66.Good Programming Practices (6:22)
67.args and kwargs (6:44)
68.Exercise (10:54)
69.Scope of a Function (5:41)
70.Scope Rules 1 (8:21)
Assessment Exercise-09
Section 10: Python Part-3
71.Scope Rules 2 (3:18)
72.GLobal vs nonlocal Keywords (5:43)
73.Programming Best Practices 2 (7:07)
74.Special Functions map (6:52)
75.Special Functions Filter (6:55)
76.Special Functions Zip (4:08)
77.Special Functions reduce (7:54)
78.List Comprehension Case 12 and 3 (10:33)
79.Sets and Dictionary Comprehension (3:27)
80.Python Modules (9:35)
81.Python packages (8:04)
Assessment Exercise-10
Section 11: Environment Setup for Machine Learning Projects
82. Who is Mr. Conda (3:03)
83.Tools for Data Science Environment (6:53)
84.Setting Up Machine Learning Project (5:51)
85. Blue Print of Machine Learning Project (3:50)
86.Installing conda (5:51)
87.Installing tools (3:44)
88.Starting Jupyter Notebook (6:36)
89.Installing for MacOS and Linux (1:43)
90.Walkthrough of Jupyter notebook 1 (8:10)
91.Walkthrough of Jupyternotebook 2 (6:47)
92.Loading and Visualizing Data (11:15)
93.Summing it Up (4:06)
Assessment Exercise-11
Section 12: Pandas for Data Analysis
94.Tools needed (5:39)
95.Pandas and What we Will cover (3:08)
96. Data Frames (10:11)
97.How to Import Data (11:10)
98. Describing Data (7:07)
99.Data Selection (14:03)
100.Data Selection 2 (14:15)
101.Changing Data (11:30)
102.Add Remove Data (12:33)
103._Manipulating_Data (10:10)
Assessment Exercise-12
Section 13: NumPy
104.What and Why of Numpy (7:02)
105. Numpy Array (14:25)
106.Shape of Array (8:29)
107.Important Functions on Arrays (8:51)
108.Creating Numpy array (13:00)
109.random seed (3:28)
110.Accessing Elements (12:29)
111.Array Manipulation (9:27)
112.Aggregations (8:44)
Assessment Exercise-13
Section 14: NumPy Part 02
113.mean variance and std (6:35)
114.Dot Product vs Matrix Manipulation (4:32)
115.Dot Product (14:22)
116. Reshape and Transpose (4:52)
117. Exercise (14:46)
118.Comparison Operators (3:37)
119.Sorting Arrays (9:22)
120. Reading Images (9:26)
Assessment Exercise-14
Section 15: Matplotlib
121. matplotlib Into (5:05)
122.First Plot with matplotlib (7:12)
123.Methods to Plot (8:14)
124.settingup Features (3:56)
125.One Figure Many Plots (9:41)
126.Most Used Plots Bar plot (8:52)
127.Histogram (7:31)
128.Four plot one figure (3:34)
129.Pandas Data Frame (8:26)
130.Plotting from Pandas Data Frame (11:19)
Assessment Exercise-15
Section 16: Matplotlib Part 02
131. Plotting from Pandas Data Frame (11:19)
132. Bar plot from Pandas Data Frame (9:15)
133. pyplot vs OO methods (10:18)
134. Life Cycle of OO method (10:20)
135. Life Cycle of OO method Advanced (11:52)
136. Customization Part-2 (2:30)
137. Customization Part-3 (3:57)
138. Figure Styling (4:20)
139. Naming Entire Figure (6:17)
Assessment Exercise-16
Section 17: Scikit-Learn
139.What Actually ML Model is (6:49)
140.Intro to Sklearn (6:36)
141.Step 1 Getting Data Ready Split Data (8:08)
142.Step 2 Choosing ML model (5:02)
143.Step 3 Fit Model (3:34)
144.Step 4 Evaluate Model (5:35)
145. Step 5 Improve Model (5:02)
146.Step 6 Save Model (6:24)
Assessment Exercise-17
Section 18: Scikit-Learn Part 02
147.What we are going to Do (8:17)
148.Step 1 Getting Data Split Data (7:16)
149.Step 1 Getting Data Ready Converting Part 1 (6:44)
150.Getting Data Ready Converting Part 2 (8:49)
151.Getting Data Anatomy of Conversion (5:12)
152.Getting Data Second Method of Conversion (4:27)
153.Getting Data Missing Values (7:58)
154.Getting Data Missing Values method 2 (14:45)
155.Choosing Machine Learning Model (7:01)
156.Using map to choose model (14:06)
157.Step 2 How to Choose Better model (6:22)
Assessment Exercise-18
Section 19: Scikit-Learn Part 03
158.Choosing Model for Classification problem (14:42)
159.Fit the Model (4:46)
160.Running Prediction (13:57)
161.Step 3 predict proba method (5:38)
162.Step 3 Running Prediction on Regression Problem (9:11)
163.Step 4 Evaluating Machine Learning Model Default Scoring (12:43)
164.Step 4 WHat is Cross Validation (15:22)
165.Step 4 Accuracy (Classification Model) (4:14)
166.Step 4 Area Under the Curve Part 1 (7:52)
167.Step 4 Area Under the Curve Part 2 (8:20)
Assessment Exercise-19
Section 20: Scikit-Learn Part 04
168.Step 4 Area Under the Curve Part 3 Plotting (8:55)
169.Confusion Matrix Calculate (12:14)
170.Step 4 Confusion Matrix Plot (9:05)
171.Step 4 Classification Report Important concepts (7:55)
172.Step 4 Classification Report Fully Explained (7:45)
173.Step 4 R2 for Regression Problems (6:50)
174.Step 4 Mean Absolute Error for Regression Problems (6:34)
175.Step 4 Mean Square Error for Regression Problems (6:34)
176.Step 4 Scoring parameters for Classification (6:31)
177.Step 4 Scoring parameters for Regression (4:37)
178.Step 4 Evaluation using Functions Classification (10:46)
179.Step 4 Evaluation using Functions Regression (10:55)
Assessment Exercise-20
Section 21: Scikit-Learn Part 05
180.Step 5 Improving Model by Hyper parameters (6:29)
181.Step 5 Improving Model by Hyperparameters manually (8:34)
182. Step 5 Hyperparameters Task 1 (9:20)
183.Step 5 Evaluation Metrics in One Function (8:24)
184.Step 5 Hyperparameters Comparison (4:43)
185.Tunning Hyperparameters using RSCV (14:34)
186.Tunning Hyperparameters using RSCV Part 2 (11:44)
187.Tunning Hyperparameters using GSCV (10:37)
188.Results Comparison (7:57)
Assessment Exercise-21
Section 22: Scikit Learn Part 06
189.Save Load Model with Pickle Method 1 (6:52)
190.Save Load Model with joblib Method 2 (4:28)
191.Building Entire Model using Pipeline Part 1 (5:53)
192.Building Entire Model using Pipeline Part 2 (10:37)
193.Building Entire Model using Pipeline Part 3 (9:04)
194.Building Entire Model using Pipeline Part 4 (12:06)
Assessment Exercise-22
Section 23: Project-1 Part 01
195. Mile Stone Project 1 Intro (3:48)
196. Creating Project Environment (10:36)
197.First 4 Steps (5:36)
198.Data Features Recognition (3:39)
199.Importing Tools and Libraries (6:18)
200. Exploratory Data Analysis Part 1 (5:41)
201.Exploratory Data Analysis Part 2 (7:57)
202.Be Careful with Plot choice (3:08)
203. Scatter Plot to see any Pattren (7:41)
204.Age Distribution (4:00)
205.Chest paint type and Target relation Part 1 (6:53)
206.Chest paint type and Target relation Part 2 (4:26)
207.Correlation Matrix Part 1 (5:37)
Assessment Exercise-23
Section 24: Project-1 Part 02
208.Plotting Correlation Matrix Part 2 (6:53)
209.Modelling Split the data (3:53)
210.Choosing the Right Model (11:57)
211.Improving Model (9:55)
212. Plotting the Improved Model Score (5:18)
213.Hyperparameter Tunning using GSCV (6:04)
214.Hyperparamters for RandomForestClassifier (8:47)
215. Running the model with Hyperparemeters using GSCV (4:32)
216.Score Comparison after tunning (4:48)
Assessment Exercise-24
Section 25: Project-1 Part 03
217.Hyperparameters Tunning Using Grid Search CV (5:18)
218.Summarizing (3:12)
219.What have we learnt (2:52)
220.Area under the curve and Confusion Matrix (8:08)
221.Plot the Classification report (9:43)
222.Lets see if Cross Validation layers help us (8:38)
223.Visualizing Cross Validation Score (4:32)
224. Features Improvement (8:55)
225. Conclusion (2:30)
Assessment Exercise-25
Section 26: Final Exam
Final Exam
189.Save Load Model with Pickle Method 1
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