Machine Learning : Linear Regression using TensorFlow Python

Machine Learning : Linear Regression using TensorFlow Python, available at $54.99, has an average rating of 4.6, with 19 lectures, 3 quizzes, based on 157 reviews, and has 1453 subscribers.

You will learn about Machine Learning – Linear Regression in TensorFlow with Python TensorFlow model for Linear Regression This course is ideal for individuals who are Anybody who wants to develop Machine Learning skill or Those who want to get a job as a Machine Learning Developer It is particularly useful for Anybody who wants to develop Machine Learning skill or Those who want to get a job as a Machine Learning Developer.

Enroll now: Machine Learning : Linear Regression using TensorFlow Python

Summary

Title: Machine Learning : Linear Regression using TensorFlow Python

Price: $54.99

Average Rating: 4.6

Number of Lectures: 19

Number of Quizzes: 3

Number of Published Lectures: 19

Number of Published Quizzes: 3

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: About this course

Lecture 1: Course Introduction

Chapter 2: Introduction to Artificial Intelligence and Machine Learning

Lecture 1: Introduction to Artificial Intelligence

Lecture 2: Introduction to Machine Learning

Chapter 3: The Building Blocks for Developing Program

Lecture 1: Introduction to Google Colaboratory

Lecture 2: Introduction to TensorFlow

Chapter 4: Linear Regression Model

Lecture 1: Introduction to Linear Regression Models

Chapter 5: Data Preparation and Normalization

Lecture 1: Training and Test Data Preparation

Lecture 2: Python Data Visualization – Tutorial

Lecture 3: Data Visualization

Lecture 4: Data Normalization

Chapter 6: Linear Regression

Lecture 1: Linear Regression Model Creation

Lecture 2: Training the Model

Lecture 3: Testing and Using the Model

Chapter 7: Managing Data Files and of Pandas Dataframes

Lecture 1: Loading Datafile in Colaboratory Workspace

Lecture 2: Introduction to Python Pandas

Lecture 3: Linear Regression using the Datafile upload

Chapter 8: Linear Regression Model with More variables

Lecture 1: Linear Regression Model with 3 variables

Lecture 2: Python Program for the Linear Regression Model with 3 variables

Chapter 9: Training and Testing Error in Machinle Learning Models

Lecture 1: Training and Testing Error in Machinle Learning Models

Instructors

Xavier Chelladurai
Professor

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 12 votes
4 stars: 61 votes
5 stars: 82 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu