Getting Started with Machine Learning for Developers [Video]

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Getting Started with Machine Learning for Developers [Video]



Learn the basics of Machine Learning to make high performing day-to-day apps

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Video Details

ISBN 139781789131543
Course Length1 hour

Video Description

Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the Globe is, "How do I get started in Machine Learning?" One reason could be the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This systematic guide will teach you various Machine Learning techniques.

You start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The course introduces you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement regression, clustering, classification, and more, all with fun examples.

Style and Approach

This course gives you a glimpse into Machine Learning models and the application of models at scale using clustering, classification, regression, and reinforcement learning, all with fun examples. Hands-on examples will be presented to understand the power of problem-solving with Machine Learning and advanced architectures, software installation, and configuration.

Table of Contents

Machine Learning and Statistical Science
The Course Overview
What is Machine Learning?
Statistics
Probability and Random Variables
Differential Calculus Elements
The Learning Process
Dataset Retrieval
Feature Engineering
Dataset Preprocessing
Models
Clustering
K-Means Algorithm
K-NN Algorithm
Linear and Logistic Regression
Regression Analysis
Linear Regression
Data Exploration and Linear Regression in Practice
Linear Regression with Gradient Descent
Logistic Regression

What You Will Learn

  • Learn the math and mechanics of Machine Learning 
  • Understand the steps in the workflow of a Machine Learning process
  • Learn K-means clustering
  • Learn K-NN clustering
  • Explore supervised learning algorithms

Authors

Table of Contents

Machine Learning and Statistical Science
The Course Overview
What is Machine Learning?
Statistics
Probability and Random Variables
Differential Calculus Elements
The Learning Process
Dataset Retrieval
Feature Engineering
Dataset Preprocessing
Models
Clustering
K-Means Algorithm
K-NN Algorithm
Linear and Logistic Regression
Regression Analysis
Linear Regression
Data Exploration and Linear Regression in Practice
Linear Regression with Gradient Descent
Logistic Regression

Video Details

ISBN 139781789131543
Course Length1 hour
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