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Learn Machine Learning in 21 Days
Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.
Created by Code Warriors, offered on Udemy
bestcourses score™
Student feedback
7/10To make sure that we score courses properly, we pay a lot of attention to the reviews students leave on courses and how many students are taking a course in the first place. This course has a total of 66169 students which left 350 reviews at an average rating of 3.88. Impressive!
Course length
9/10We analyze course length to see if courses cover all important aspects of a topic, taking into account how long the course is compared to the category average. This course has a length of 4 hours 37 minutes, which is pretty short. This might not be a bad thing, but we've found that longer courses are often more detailed & comprehensive. The average course length for this entire category is 4 hours 29 minutes.
Overall score
7.2/10This course currently has a bestcourses score of 7.2/10, which makes it a great course to learn from. On our entire platform, only 15% of courses achieve this rating!
Description
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
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.
Requirements
- Some basic python programming experience.
- Basic understanding of python libraries like numpy, pasdas and matplotlib.(Optional)
- Some high school mathematics.
