Machine Learning Linear Regression Using Tensorflow Python

Tags: Regression Analysis

Design, Develop and Train the model

Last updated 2022-01-10 | 4.4

- Machine Learning - Linear Regression in TensorFlow with Python
- TensorFlow model for Linear Regression

What you'll learn

Machine Learning - Linear Regression in TensorFlow with Python

TensorFlow model for Linear Regression

* Requirements

* Programming Fundamentals
* Be able to un derstand the code

Description

In this course, we provide the step-by-step approach for building a Linear Regression model using TensorFlow with Python. In the beginning, we give a high-level introduction to Artificial Intelligence and Machine Learning. We develop the entire system in Google Colaboratory using TensorFlow. So, we have a lecture each on Introduction to Google Colaboratory and Introduction to TensorFlow. We develop the model to predict the price of the house from the size. We have the data for 100 houses with two attributes, house size, and house price. We first teach Python code to create the data, load it and check if the data are correctly loaded. We divide the data into Training and Testing data at a ratio of 80:20. We also introduce the importance of Data Normalization. After normalizing the data, we begin the process of building the model. We use the TensorFlow Gradient Descent method and train the model. We select the number of iterations to make the training error and testing error significantly low. After training the model we use the model for a new set of data. That is, we find the price of a new house whose size is given. We then extend the program for a problem with multiple variables. In this problem, we predict the price of the house from three attributes, plinth area, land area, and furnish-area. In the last lecture, elaborate more on training and test data and compute the same.

Who this course is for:

Anybody who wants to develop Machine Learning skill
Those who want to get a job as a Machine Learning Developer

Course content

9 sections • 22 lectures

About this course 1 lectures

Course Introduction Preview 05:23

Introduction to Artificial Intelligence and Machine Learning 3 lectures

Introduction to Artificial Intelligence Preview 08:48

We introduce the concept of Artificial Intelligence. We introduce Artificial Intelligence technologies and some popular applications

Introduction to Machine Learning Preview 08:29

In this lecture, we introduce the concept of Machine Learning. We introduce the difference between instruction-based learning and experience-based learning. We also introduce the important Machine Learning applications.

Introductio to AI and ML

Answer all the questions

The Building Blocks for Developing Program 3 lectures

Introduction to Google Colaboratory Preview 14:29

Introduction to Google Colaboratory

Introduction to TensorFlow Preview 06:58

Introduction to TensorFlow. Here we introduce the open-source library TensorFlow and explain how it can be used for building Machine Learning systems.

Colab and TensorFlow

Colaboratory and TensorFlow

Linear Regression Model 1 lectures

Introduction to Linear Regression Models Preview 13:11

In this lecture, we present the Linear Regression Models using very simple real-life examples. We explain how the training and test data can be created.

Data Preparation and Normalization 4 lectures

Training and Test Data Preparation Preview 09:21

In this lecture we introduce the concept of Training Data and Test Data.

Python Data Visualization - Tutorial Preview 10:25

In this video, we demonstrate how Data visualization is possible using matplotlib library.

Data Visualization Preview 08:22

Data Visualization is very important in Data analytics. In this lecture we present the program on how we can visualize the data.

Data Normalization Preview 09:55

Data Normalization is an important concept in Machine Learning. In this lecture, we explain the importance of Data Normalization and Write Python code for the same

Linear Regression 3 lectures

Linear Regression Model Creation Preview 08:07

We write the Python Code to build the Linear Regression Model

Training the Model Preview 14:31

This lecture is on training the model using the training data. We write the Python code.

Testing and Using the Model Preview 08:41

Here we write the code to use the model and predict for a new set of data.

Managing Data Files and of Pandas Dataframes 4 lectures

Loading Datafile in Colaboratory Workspace Preview 05:36

In this video, we learn how to upload a data file into Google Colaboratory.

Introduction to Python Pandas Preview 16:30

In Python, Pandas is a very important library to process data files. In this video, we present a tutorial on Pandas. We upload a data file called diabetics.csv and learn how to do various processing.

Linear Regression using the Datafile upload Preview 09:44

In the programs written so far, the data was a part of the program. That is not the right way of programming. We must give data in a separate data file. In this program, we upload the data file to the Colab workspace and write the program that reads data from the data file.

Quiz on Datafiles and Pandas

Linear Regression Model with More variables 2 lectures

Linear Regression Model with 3 variables Preview 10:49

In this lecture we describe how we can build a model with more variables

Python Program for the Linear Regression Model with 3 variables Preview 14:35

In this lecture we present the complete program to build the Linear Regression model with 3 variables and use it for a new set of data.

Training and Testing Error in Machinle Learning Models 1 lectures

Training and Testing Error in Machinle Learning Models Preview 13:54

In this lecture, we introduce the concept of Training Error and Testing Error. We illustrate the importance of data using an analogy. We write a detailed Python Code to compute the training and testing error.