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Machine Learning: Practical labs with Math’s Core Foundation

By Green Edtech Categories: IT
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About Course

This Machine Learning course provides basic and advanced concepts of machine learning. Our course is designed for students and working professionals. Machine learning is a growing technology that enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information.

Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more. This machine learning tutorial gives you an introduction to machine learning along with a wide range of machine learning techniques such as Supervised, Unsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, hidden Markov models, and various sequential models. This course will be useful for graduates, postgraduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The reader can be a beginner or an advanced learner. This tutorial has been prepared for the students as well as professionals to ramp up quickly. This tutorial is a stepping stone to your Machine Learning journey. Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. And as a bonus, this course includes both Python, R and MATLAB code templates which you can download and use on your own projects.

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Course Content

Section 1: Introduction

  • Introduction to Machine Learning

Section 2: Machine Learning Fundamentals

Section 3: Tools for Machine Learning

Section 4: Practical Interaction

Section 5: Calculus for Machine Learning

Section 6: Linear Algebra for Machine Learning

Section 08: Machine Learning Tutorial R And Python

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