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!
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