Section 1 : Introduction

 		Lecture 1 Welcome to the course 2:14
Lecture 2 Introduction to Neural Networks and Course flow 4:39
Lecture 3 Course Resources Text

Section 2 : Setting up Python and Jupyter Notebook

 		Lecture 5 Installing Python and Anaconda 3:4
Lecture 6 Opening Jupyter Notebook 9:6
Lecture 7 Introduction to Jupyter 13:27
Lecture 8 Arithmetic operators in Python Python Basics 4:28
Lecture 9 Strings in Python Python Basics 19:7
Lecture 10 Lists, Tuples and Directories Python Basics 18:41
Lecture 11 Working with Numpy Library of Python 11:55
Lecture 12 Working with Pandas Library of Python 9:15
Lecture 13 Working with Seaborn Library of Python 8:58

Section 3 : Single Cells - Perceptron and Sigmoid Neuron

 		Lecture 14 Perceptron 9:47
Lecture 15 Activation Functions 7:31
Lecture 16 Python - Creating Perceptron model 14:11

Section 4 : Neural Networks - Stacking cells to create network

 		Lecture 17 Basic Terminologies 9:47
Lecture 18 Gradient Descent
Lecture 19 Back Propagation 22:27

Section 5 : Important concepts Common Interview questions

 		Lecture 20 Some Important Concepts 12:44

Section 6 : Standard Model Parameters

 		Lecture 21 Hyperparameters 8:19

Section 7 : Practice Test

 		Lecture 22 About Proctor Testing Pdf

Section 8 : Tensorflow and Keras

 		Lecture 23 Keras and Tensorflow
Lecture 24 Installing Tensorflow and Keras 4:4

Section 9 : Python - Dataset for classification problem

 		Lecture 25 Dataset for classification 7:20
Lecture 26 Normalization and Test-Train split 6:0
Lecture 27 More about test-train split Text

Section 10 : Python - Building and training the Model

 		Lecture 28 Different ways to create ANN using Keras 1:59
Lecture 29 Building the Neural Network using Keras 12:19
Lecture 30 Compiling and Training the Neural Network model 10:34
Lecture 31 Evaluating performance and Predicting using Keras 9:21

Section 11 : Python - Solving a Regression problem using ANN

 		Lecture 32 Building Neural Network for Regression Problem 22:11

Section 12 : Complex ANN Architectures using Functional API

 		Lecture 33 Using Functional API for complex architectures 12:41

Section 13 : Saving and Restoring Models

 		Lecture 34 Saving - Restoring Models and Using Callbacks 19:49

Section 14 : Hyperparameter Tuning

 		Lecture 35 Hyperparameter Tuning 9:6

Section 15 : Add-on 1 Data Preprocessing

 		Lecture 36 Gathering Business Knowledge 3:20
Lecture 37 Data Exploration 3:19
Lecture 38 The Dataset and the Data Dictionary 7:31
Lecture 39 Add-on Resources
Lecture 40 Importing Data in Python 6:4
Lecture 41 Univariate analysis and EDD 3:34
Lecture 42 EDD in Python 12:11
Lecture 43 Outlier Treatment 4:16
Lecture 44 Outlier Treatment in Python 14:18
Lecture 45 Missing Value Imputation
Lecture 46 Missing Value Imputation in Python 4:57
Lecture 47 Seasonality in Data 3:35
Lecture 48 Bi-variate analysis and Variable transformation
Lecture 49 Variable transformation and deletion in Python 9:21
Lecture 50 Non-usable variables 4:44
Lecture 51 Dummy variable creation Handling qualitative data 4:50
Lecture 52 Dummy variable creation in Python 5:46
Lecture 53 Correlation Analysis 10:5
Lecture 54 Correlation Analysis in Python 7:7

Section 16 : Add-on 2 Classic ML models - Linear Regression

 		Lecture 55 The Problem Statement 1:26
Lecture 56 Basic Equations and Ordinary Least Squares (OLS) method 8:13
Lecture 57 Assessing accuracy of predicted coefficients 14:40
Lecture 58 Assessing Model Accuracy RSE and R squared 7:20
Lecture 59 Simple Linear Regression in Python
Lecture 60 Multiple Linear Regression 4:58
Lecture 61 The F - statistic 8:22
Lecture 62 Interpreting results of Categorical variables 5:4
Lecture 63 Multiple Linear Regression in Python 14:13
Lecture 64 Test-train split 9:32
Lecture 65 Bias Variance trade-off 6:2
Lecture 66 Test train split in Python 10:19