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Learn Machine Learning in 21 Days
Rating: 4.0 out of 5(709 ratings)
108,326 students

Learn Machine Learning in 21 Days

Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.
Created byCode Warriors, Mayank Bajaj, Gaurav Sharma, Anup Mor
Last updated 4/2021
English

What you'll 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.

Course content

7 sections • 21 lectures • 4h 36m total length

  • What is ML? Application & Types of ML12:27

    Discover what machine learning is and its applications, from image recognition to self-driving cars. Learn supervised, unsupervised, and reinforcement learning, and the roles of training data and models.

Requirements

  • Some basic python programming experience.
  • Basic understanding of python libraries like numpy, pasdas and matplotlib.(Optional)
  • Some high school mathematics.

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

Who this course is for:

  • 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.

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