1 - Introduction (18.41 MB)
37 - Python Dataset for classification problem (75.73 MB)
38 - Python Normalization and TestTrain split (65.45 MB)
39 - R Dataset Normalization and TestTrain set (103.5 MB)
41 - Different ways to create ANN using Keras (9.31 MB)
42 - Building the Neural Network using Keras (118.1 MB)
43 - Compiling and Training the Neural Network model (116.66 MB)
44 - Evaluating performance and Predicting using Keras (98.49 MB)
45 - BuildingCompiling and Training (105.98 MB)
46 - Evaluating and Predicting (73.78 MB)
47 - Building Neural Network for Regression Problem (279.96 MB)
48 - Using Functional API for complex architectures (130.58 MB)
49 - Building Regression Model with Functional AP (97.06 MB)
50 - Complex Architectures using Functional API (125.97 MB)
51 - Saving Restoring Models and Using Callbacks (251.89 MB)
52 - Saving Restoring Models and Using Callbacks (287.59 MB)
53 - Hyperparameter Tuning (56.16 MB)
54 - Hyperparameter Tuning (56.13 MB)
55 - Testtrain split (29.85 MB)
56 - Bias Variance tradeoff (18.35 MB)
57 - Test train split in Python (55.84 MB)
58 - Test train split in R (108.52 MB)
59 - The final milestone (8.36 MB)
10 - Lists Part 1 (11.23 MB)
11 - Lists Part 2 (13.29 MB)
12 - Tuples and Directories (12.88 MB)
13 - Working with Numpy Library of Python (52.79 MB)
14 - Working with Pandas Library of Python (55.76 MB)
15 - Working with Seaborn Library of Python (45.77 MB)
3 - Installing Python and Anaconda (15.35 MB)
4 - This is a milestone (26.14 MB)
5 - Opening Jupyter Notebook (54.65 MB)
6 - Introduction to Jupyter part 1 (21.66 MB)
7 - Introduction to Jupyter part 2 (8.42 MB)
8 - Arithmetic operators in Python Python Basics (10.41 MB)
9 - Strings in Python Python Basics (81.08 MB)
17 - Integrating ChatGPT with Jupyter notebook (52.42 MB)
18 - Installing R and R studio (62.41 MB)
19 - Basics of R and R studio (33.12 MB)
20 - Packages in R (120.74 MB)
21 - Inputting data part 1 Inbuilt datasets of R (37.55 MB)
22 - Inputting data part 2 Manual data entry (25.45 MB)
23 - Inputting data part 3 Importing from CSV or Text files (108.48 MB)
24 - Creating Barplots in R (141.02 MB)
25 - Creating Histograms in R (37.07 MB)
26 - Perceptron (39.4 MB)
27 - Activation Functions (24.54 MB)
28 - Python Creating Perceptron model (129.03 MB)
29 - Basic Terminologies (28.61 MB)
30 - Gradient Descent (44.32 MB)
31 - Back Propagation (84.48 MB)
32 - Some Important Concepts (52.78 MB)
33 - Hyperparameters (33.06 MB)
34 - Keras and Tensorflow (11.05 MB)
35 - Installing Tensorflow and Keras in Python (29.51 MB)
36 - Installing TensorFlow and Keras in R (14.93 MB)