Learn Machine Learning

Do you want to build your career in data science field?
Do you want to learn the fundamentals of machine learning concepts?
Do you want to implement machine-learning algorithms with R, Python or Scala?
Do you want to learn the practical and real world implementations of a machine learning technique?
If you are interested in any of above-mentioned topics, the Machine Learning path is what to you need in your data science journey.

With the growing importance of data science and its uses across all types of business, it is the perfect time to learn machine learning. So, what is the big deal about machine learning? Briefly, various companies and organizations collect a lot of raw data today, without getting knowledge from it. Machine learning techniques can help them get the inherent knowledge or pattern in their available data set.

In this Learning Path, we provide you with everything you need to transform data into action. You will learn each piece of this puzzle with a lot of practical projects.

We start with basic techniques and move on to coding our own machine learning algorithms. In general, we learn different techniques like classification, optimization, clustering, dimension reduction and text mining. We recommend you follow the steps in sequential order.  Only skip a step, if you know the subject matter mentioned in that step already.

After completing this path you should be able to apply your machine learning knowledge to various applications and domains to solve real-world problems such as Spam detection, sentiment prediction, web document classification, market prediction, fraud detection, product recommendation systems, potential customer identification, web personalization, weather prediction, character recognition, games, medical diagnosis, visualization, etc.

For this path we assume you are familiar with the basic data science languages and tools such as python, R, Scala, SQL, and Git.
For most of these courses we use Data Scientist Workbench to clean data and write programs in one of the mentioned programming languages.

Featured Courses

Machine Learning - Classification and Prediction

Coming Soon!! This course is under development.

Machine Learning - Dimensionality Reduction

Welcome to this machine learning course on Dimensionality Reduction.

Dimensionality Reduction is a category of unsupervised machine learning techniques used to reduce the number of features in a dataset. Dimension reduction can also be used to group similar variables together.

In this course, you will learn the theory behind dimension reduction, and get some hands-on practice using Principal Components Analysis (PCA) and Exploratory Factor Analysis (EFA) on survey data.

The code used in this course is prepared for you in R.

Machine Learning - Regression Analysis

Coming Soon!! This course is under development.

Machine Learning - Neural Networks

Coming Soon!! This course is being developed.

Advanced Classification and Prediction

This is an introduction course to Machine learning for classification

This course cover several popular classification methods and some validation methods.

You will learn the theory behind k nearest neighbors, Logistic Regression, Support Vector Machines, Trees and Random Forests. Then learn how to apply and test these models on python using scikit-learn.

Machine Learning - Regression Analysis

Coming Soon!! This course is under development.

Machine Learning - Dimensionality Reduction

Welcome to this machine learning course on Dimensionality Reduction.

Dimensionality Reduction is a category of unsupervised machine learning techniques used to reduce the number of features in a dataset. Dimension reduction can also be used to group similar variables together.

In this course, you will learn the theory behind dimension reduction, and get some hands-on practice using Principal Components Analysis (PCA) and Exploratory Factor Analysis (EFA) on survey data.

The code used in this course is prepared for you in R.

Advanced Classification and Prediction

This is an introduction course to Machine learning for classification

This course cover several popular classification methods and some validation methods.

You will learn the theory behind k nearest neighbors, Logistic Regression, Support Vector Machines, Trees and Random Forests. Then learn how to apply and test these models on python using scikit-learn.

Machine Learning - Neural Networks

Coming Soon!! This course is being developed.

What is Big Data University?

An IBM community initiative, Big Data University is the world’s best education on big data. Learn about big data, data science and analytic technologies from experts using hands-on exercises and interactive videos. Best of all, it’s completely free.