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Course Detail

MACHINE LEARNING

About the Tutorial

Today’s Artificial Intelligence (AI) has far surpassed the hype of blockchain and quantum computing. The developers now take advantage of this in creating new Machine Learning models and to re-train the existing models for better performance and results. This tutorial will give an introduction to machine learning and its implementation in Artificial Intelligence.

Audience

This tutorial has been prepared for professionals aspiring to learn the complete picture of machine learning and artificial intelligence. This tutorial caters the learning needs of both the novice learners and experts, to help them understand the concepts and implementation of artificial intelligence.

Prerequisites

The learners of this tutorial are expected to know the basics of Python programming. Besides, they need to have a solid understanding of computer programing and fundamentals.
If you are new to this arena, we suggest you pick up tutorials based on these concepts first, before you embark on with Machine Learning.

Requirements
Just some high school mathematics level.

Description
Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, 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 includes:
  • 44 hours on-demand video
  • 73 articles
  • 38 downloadable resources
  • Full lifetime access
  • Access on mobiles
  • Certificate of completion
curriculum: Machine Learning Course Syllabus: Certifications
  • Module 1 – Introduction to Machine Learning
  • Module 2 – Supervised Learning and Linear Regression
  • Module 3 – Classification and Logistic Regression
  • Module 4 – Decision Tree and Random Forest
  • Module 5 – Naïve Bayes and Support Vector Machine
  • Module 6 – Unsupervised Learning
  • Module 7 – Natural Language Processing and Text Mining
  • Module 8 – Introduction to Deep Learning
  • Module 9 – Time Series Analysis
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