Section 1 : Introduction
Lecture 1 Welcome to the course 00:02:14 Duration
Lecture 2 Introduction to Neural Networks and Course flow 00:04:39 Duration
Lecture 3 Course Resources

Section 2 : Setting up Python and Jupyter Notebook
Lecture 1 Installing Python and Anaconda 00:03:04 Duration
Lecture 2 Opening Jupyter Notebook 00:09:06 Duration
Lecture 3 Introduction to Jupyter 00:13:27 Duration
Lecture 4 Arithmetic operators in Python Python Basics 00:04:28 Duration
Lecture 5 Strings in Python Python Basics 00:19:07 Duration
Lecture 6 Lists, Tuples and Directories Python Basics 00:18:41 Duration
Lecture 7 Working with Numpy Library of Python 00:11:55 Duration
Lecture 8 Working with Pandas Library of Python 00:09:15 Duration
Lecture 9 Working with Seaborn Library of Python 00:08:58 Duration

Section 3 : Single Cells - Perceptron and Sigmoid Neuron
Lecture 1 Perceptron 00:09:47 Duration
Lecture 2 Activation Functions 00:07:31 Duration
Lecture 3 Python - Creating Perceptron model 00:14:11 Duration

Section 4 : Neural Networks - Stacking cells to create network
Lecture 1 Basic Terminologies 00:09:47 Duration
Lecture 2 Gradient Descent
Lecture 3 Back Propagation 00:22:27 Duration

Section 5 : Important concepts Common Interview questions
Lecture 1 Some Important Concepts 00:12:44 Duration

Section 6 : Standard Model Parameters
Lecture 1 Hyperparameters 00:08:19 Duration

Section 7 : Practice Test
Lecture 1 About Proctor Testing

Section 8 : Tensorflow and Keras
Lecture 1 Keras and Tensorflow
Lecture 2 Installing Tensorflow and Keras 00:04:04 Duration

Section 9 : Python - Dataset for classification problem
Lecture 1 Dataset for classification 00:07:20 Duration
Lecture 2 Normalization and Test-Train split 00:06:00 Duration
Lecture 3 More about test-train split

Section 10 : Python - Building and training the Model
Lecture 1 Different ways to create ANN using Keras 00:01:59 Duration
Lecture 2 Building the Neural Network using Keras 00:12:19 Duration
Lecture 3 Compiling and Training the Neural Network model 00:10:34 Duration
Lecture 4 Evaluating performance and Predicting using Keras 00:09:21 Duration

Section 11 : Python - Solving a Regression problem using ANN
Lecture 1 Building Neural Network for Regression Problem 00:22:11 Duration

Section 12 : Complex ANN Architectures using Functional API
Lecture 1 Using Functional API for complex architectures 00:12:41 Duration

Section 13 : Saving and Restoring Models
Lecture 1 Saving - Restoring Models and Using Callbacks 00:19:49 Duration

Section 14 : Hyperparameter Tuning
Lecture 1 Hyperparameter Tuning 00:09:06 Duration

Section 15 : Add-on 1 Data Preprocessing
Lecture 1 Gathering Business Knowledge 00:03:20 Duration
Lecture 2 Data Exploration 00:03:19 Duration
Lecture 3 The Dataset and the Data Dictionary 00:07:31 Duration
Lecture 4 Add-on Resources
Lecture 5 Importing Data in Python 00:06:04 Duration
Lecture 6 Univariate analysis and EDD 00:03:34 Duration
Lecture 7 EDD in Python 00:12:11 Duration
Lecture 8 Outlier Treatment 00:04:16 Duration
Lecture 9 Outlier Treatment in Python 00:14:18 Duration
Lecture 10 Missing Value Imputation
Lecture 11 Missing Value Imputation in Python 00:04:57 Duration
Lecture 12 Seasonality in Data 00:03:35 Duration
Lecture 13 Bi-variate analysis and Variable transformation
Lecture 14 Variable transformation and deletion in Python 00:09:21 Duration
Lecture 15 Non-usable variables 00:04:44 Duration
Lecture 16 Dummy variable creation Handling qualitative data 00:04:50 Duration
Lecture 17 Dummy variable creation in Python 00:05:46 Duration
Lecture 18 Correlation Analysis 00:10:05 Duration
Lecture 19 Correlation Analysis in Python 00:07:07 Duration

Section 16 : Add-on 2 Classic ML models - Linear Regression
Lecture 1 The Problem Statement 00:01:26 Duration
Lecture 2 Basic Equations and Ordinary Least Squares (OLS) method 00:08:13 Duration
Lecture 3 Assessing accuracy of predicted coefficients 00:14:40 Duration
Lecture 4 Assessing Model Accuracy RSE and R squared 00:07:20 Duration
Lecture 5 Simple Linear Regression in Python
Lecture 6 Multiple Linear Regression 00:04:58 Duration
Lecture 7 The F - statistic 00:08:22 Duration
Lecture 8 Interpreting results of Categorical variables 00:05:04 Duration
Lecture 9 Multiple Linear Regression in Python 00:14:13 Duration
Lecture 10 Test-train split 00:09:32 Duration
Lecture 11 Bias Variance trade-off 00:06:02 Duration
Lecture 12 Test train split in Python 00:10:19 Duration