Machine Learning From Basic to Advanced
3.8 (144 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
22,087 students enrolled

Machine Learning From Basic to Advanced

Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.
New
3.8 (144 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
22,087 students enrolled
Last updated 7/2020
English
English [Auto]
Current price: $13.99 Original price: $19.99 Discount: 30% off
23 hours left at this price!
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This course includes
  • 3 hours on-demand video
  • 30 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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.
Requirements
  • Some basic python programming experience.
  • Basic understanding of python libraries like numpy, pasdas and matplotlib.
  • 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:

  • Part 1 - Data Preprocessing

  • Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.

  • Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

  • 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.

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.
Course content
Expand all 15 lectures 02:59:27
+ Classification
5 lectures 30:42
K-Nearest Neighbour
06:26
Support Vector Machine (SVM)
12:06
Kernel SVM
06:37
Decision Tree Classification
02:39
Random Forest Classification
02:54
+ Regresssion
7 lectures 01:41:57
Simple Linear Regression
13:43
Multiple Linear Regression
32:26
Polynomial Linear Regression
11:34
Support Vector Regression(SVR)
17:30
Decision Tree Regression
08:45
Random Forest Regression
03:20
Logistic Regression
14:39
+ Clustering
2 lectures 33:42
K-Means Clustering
15:16
Hierarchical Clustering
18:26