Machine Learning From Basic to Advanced
Machine Learning From Basic to Advanced, available at $34.99, has an average rating of 4.12, with 16 lectures, based on 881 reviews, and has 139922 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: Machine Learning From Basic to Advanced
Summary
Title: Machine Learning From Basic to Advanced
Price: $34.99
Average Rating: 4.12
Number of Lectures: 16
Number of Published Lectures: 15
Number of Curriculum Items: 16
Number of Published Curriculum Objects: 15
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.
Are you ready to start your path to becoming a Machine Learning Engineer!
This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!
This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Machine Learning as well as Data Scientist!
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:
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Part 1 – Data Preprocessing
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Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.
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Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
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Part 4 – Clustering: K-Means, Hierarchical Clustering.
And as a bonus, this course includes Python code templates which you can download and use on your own projects.
Course Curriculum
Chapter 1: Data Preprocessing
Lecture 1: Data Preprocessing
Chapter 2: Classification
Lecture 1: K-Nearest Neighbour
Lecture 2: Support Vector Machine (SVM)
Lecture 3: Kernel SVM
Chapter 3: Regresssion
Lecture 1: Decision Tree Classification
Lecture 2: Random Forest Classification
Lecture 3: Simple Linear Regression
Lecture 4: Multiple Linear Regression
Lecture 5: Polynomial Linear Regression
Lecture 6: Decision Tree Regression
Lecture 7: Random Forest Regression
Lecture 8: Logistic Regression
Chapter 4: Bonus Material
Lecture 1: Support Vector Regression(SVR)
Chapter 5: Clustering
Lecture 1: K-Means Clustering
Lecture 2: Hierarchical Clustering
Instructors
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Code Warriors
The best place to learn, code and conquer – Once you have it -
Anup Mor
Software Engineer at Curio Ventures, Instructor at Udemy -
Gaurav Sharma
AI/ML Instructor at Code Warriors -
Mayank Bajaj
Founder at Code Warriors
Rating Distribution
- 1 stars: 48 votes
- 2 stars: 55 votes
- 3 stars: 190 votes
- 4 stars: 276 votes
- 5 stars: 312 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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