Learn Machine Learning in 21 Days

Learn Machine Learning in 21 Days, available at $44.99, has an average rating of 4.29, with 21 lectures, based on 677 reviews, and has 108312 subscribers.

You will learn about Master Machine Learning on Python Make accurate predictions Make robust Machine Learning models Use Machine Learning for personal purpose Have a great intuition of many Machine Learning models Know which Machine Learning model to choose for each type of problem Use SciKit-Learn for Machine Learning Tasks Make predictions using linear regression, polynomial regression, and multiple regression Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, etc. This course is ideal for individuals who are Anyone interested in Machine Learning. or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science. or Any people who want to create added value to their business by using powerful Machine Learning tools. It is particularly useful for Anyone interested in Machine Learning. or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science. or Any people who want to create added value to their business by using powerful Machine Learning tools.

Enroll now: Learn Machine Learning in 21 Days

Summary

Title: Learn Machine Learning in 21 Days

Price: $44.99

Average Rating: 4.29

Number of Lectures: 21

Number of Published Lectures: 21

Number of Curriculum Items: 21

Number of Published Curriculum Objects: 21

Original Price: ?1,199

Quality Status: approved

Status: Live

What You Will Learn

Master Machine Learning on Python

Make accurate predictions

Make robust Machine Learning models

Use Machine Learning for personal purpose

Have a great intuition of many Machine Learning models

Know which Machine Learning model to choose for each type of problem

Use SciKit-Learn for Machine Learning Tasks

Make predictions using linear regression, polynomial regression, and multiple regression

Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, etc.

Who Should Attend

Anyone interested in Machine Learning.

Students who have at least high school knowledge in math and who want to start learning Machine Learning.

Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

Any students in college who want to start a career in Data Science.

Any people who want to create added value to their business by using powerful Machine Learning tools.

Target Audiences

Anyone interested in Machine Learning.

Students who have at least high school knowledge in math and who want to start learning Machine Learning.

Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

Any students in college who want to start a career in Data Science.

Any people who want to create added value to their business by using powerful Machine Learning tools.

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

You can do a lot in 21 Days. Actually, it’s the perfect number of days required to adopt a new habit!

What you’ll learn:-

1.Machine Learning Overview

2.Regression Algorithms on the real-time dataset

3.Regression Miniproject

4.Classification Algorithms on the real-time dataset

5.Classification Miniproject

6.Model Fine-Tuning

7.Deployment of the ML model

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is ML? Application & Types of ML

Chapter 2: Data Preprocessing Techniques

Lecture 1: What is NumPy?

Lecture 2: Data Manipulation with Pandas

Chapter 3: Regression

Lecture 1: Simple Linear Regression

Lecture 2: Multiple Linear Regression

Lecture 3: Polynomial Regression

Lecture 4: Support Vector Regression(SVR)

Lecture 5: Decision Tree Regression

Lecture 6: Random Forest Regression

Lecture 7: Regression Mini Project

Chapter 4: Classification

Lecture 1: Logistic Regression

Lecture 2: K-Nearest Neighbour

Lecture 3: Support Vector Machine (SVM)

Lecture 4: Kernel SVM

Lecture 5: Naive Bayes Classification

Lecture 6: Decision Tree Classification

Lecture 7: Random Forest Classification

Lecture 8: Classification Mini Project

Chapter 5: Problems With ML

Lecture 1: Underfitting and Overfitting

Chapter 6: Model Selection

Lecture 1: Cross Validation And Grid Search

Chapter 7: Model Deployment

Lecture 1: ML Model With Deployment

Instructors

Code Warriors
The best place to learn, code and conquer – Once you have it

Mayank Bajaj
Founder at Code Warriors

Gaurav Sharma
AI/ML Instructor at Code Warriors

Anup Mor
Software Engineer at Curio Ventures, Instructor at Udemy

Rating Distribution

1 stars: 36 votes

2 stars: 43 votes

3 stars: 129 votes

4 stars: 228 votes

5 stars: 241 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!