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Advanced implementation of regression model and essential tasks to be performed like feature selection in TensorFlow 2.x

In this course, you will learn advanced linear regression technique process and with this you can able to build any regression problem. Starting from

TensorFlow 2.x

Linear Regression

Gradient Descent Algorithm

With this intuition we will work on project: Customer Revenue Prediction.

Problem Statement: A large child education toy company which sells educational tablets and gaming systems both online and in retail stores wanted to analyse the customer data. The goal of the problem is determine the following objective as shown below.

Data Analysis & Preprocessing: Analyze customer data and draw the insights w.r.t revenue and based on the insights we will do data preprocessing. In this module you will learn the following.

Necessary Data Analysis

Multi-colinearity

Factor Analysis

Feature Engineering:

Lasso Regression

Identify optimal penalty factor

Feature Selection

Pipeline Model

Evaluation

We will start with basic of tensorflow 2.x to advanced techniques in it. Then we drive into intuition behind linear regression and optimization function like gradient descent.

What you’ll learn

- TensorFlow 2.0
- Gradient Descent Algorithm
- Create Pipeline regression model in TensorFlow
- Lasso Regression
- Feature Selection with lasso
- Programming in TensorFlow 2.0
- Selection of Penalty factor lambda
- Visualizing graph in TensorBoard
- Neuron or Perceptron Model Architecture
- Loss or Cost Function
- TensorFlow Keras API
- Linear Regression
- Create customized model in TensorFlow
- Exploratory Data Analysis
- Data Preprocessing
- Multiple Linear Regression in TensorFlow

**+ Table of Contents**

**Introduction**

1 Walk through the Course

**TensorFlow Essentials**

2 Introduction

3 Getting Started to Google Colab

4 Tensor Data Structure

5 TensorFlow Convert List to Tensors

6 TensorFlow Convert Numpy Array to Tensors

7 TensorFlow Constant

8 TensorFlow 1.x vs TensorFlow 2.x

9 Operators

10 TensorFlow Operators

11 Data Flow Graph

12 Google Colab Integrating to Google Drive

13 TensorBoard – Data Flow Graph

14 Second Graph

15 Dense Network Part-1

16 Dense Network Part-2

17 Assignment – 2 Question

18 Assignment -2 Solution

**Fitting Linear Model (Linear Regression)**

19 What you will learn

20 Linear Regression Intuition

21 Gradient Descent Algorithm

22 Linear Model Architecture – Perceptron (Neuron)

23 TensorFlow – Linear Regression, Part-1

24 TensorFlow – Linear Regression, Part-2

25 TensorFlow – Loss Function

26 TensorFlow – Gradient Descent

27 TensorFlow – Fitting Model

28 TensorFlow – Keras – Linear Regression

**Project Overview**

29 Project Overview

**Data Analysis**

30 Data and Distribution

31 Distribution part-2

32 Multicollinearity

33 Factor Analysis

34 Conclusion of Data Analysis

35 Data Preprocessing

**Feature Engineering**

36 Multiple Linear Regression

37 TensorFlow – Multiple Linear Regression

38 Lasso Regression – L1 Regularization

39 TensorFlow – Lasso Regression and Penalty Factor Slection

40 Feature Selection

**Final Pipeline Model**

41 Split data into Train and Test frames

42 Input Pipelines

43 Feature Columns

44 Training Pipeline Model

45 Save and Restore

46 Model Evaluation

**BONUS**

47 Bonus Lecture

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