Learning Machine learning Online is more easy on edX

In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms and the theory behind those algorithms.

Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.

Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.

All programming examples and assignments will be in Python, using Jupyter notebooks.

What you'll learn

Classification, regression, and conditional probability estimation
Generative and discriminative models
Linear models and extensions to nonlinearity using kernel methods
Ensemble methods: boosting, bagging, random forests
Representation learning: clustering, dimensionality reduction, autoencoders, deep nets

Machine Learning with Python: A Practical Introduction

Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.

IBM

About this course

This Machine Learning with Python course dives into the basics of machine LearningusingPython, an approachable and well-known programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.

You'll look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed!

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as strain/Test Split, Root Mean Squared Error and Random Forests.

Most importantly, you will transform your theoretical knowledge into practical skills using hands-on labs. Get ready to do more learning than your machine!

What you'll learn

Supervised vs Unsupervised Machine Learning
How Statistical Modeling relates to Machine Learning, and how to do a comparison of each.
Different ways machine learning affects society

Data Science: Machine Learning

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

Harvard University

About this course

Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.

In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

What you'll learn

The basics of machine learning
How to perform cross-validation to avoid overtraining
Several popular machine learning algorithms
How to build a recommendation system
What is regularization and why it is useful?

Machine Learning Fundamentals

Understand machine learning's role in data-driven modeling, prediction, and decision-making.

The University of California, San Diego

About this course

Do you want to build systems that learn from the experience? Or exploit data to create simple predictive models of the world?

In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms and the theory behind those algorithms.

Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.

All programming examples and assignments will be in Python, using Jupyter notebooks.

What you'll learn

Classification, regression, and conditional probability estimation
Generative and discriminative models
Linear models and extensions to nonlinearity using kernel methods
Ensemble methods: boosting, bagging, random forests
Representation learning: clustering, dimensionality reduction, autoencoders, deep nets

Source: HOB

Share This On

Previous Story Next Story

Latest News Feed