Microsoft Giving Free Machine Learning Course: Enroll Now

Microsoft offers many courses for free on its website. There are more than 4000 courses and all are available free of cost. Today, I will show you a Free Machine learning cum data science course where you will learn Machine learning with data science from scratch. Let’s start.

Microsoft Free Machine Learning course overview

Name	Foundations of data science for machine learning
Duration	12 Hours 45 mins
Number of Modules	14
Difficulty level	Beginner
Offered By	Microsoft
Rating	4.8/5
Number of ratings	22,000
How to enroll?	Click here to enroll now

Modules of Microsoft Free Machine Learning course

Module 1. Introduction to machine learning

In this module, you will be introduced to all the basics of Machine Learning to set the foundations for further modules. Basically, you will be learning about models, inputs and outputs, and how to visualize inputs and outputs.

Module 2. Build classical machine learning models with supervised learning

In this module, you will be introduced to supervised machine learning and how to create models with supervised learning. Supervised learning is a type of Machine Learning where an algorithm learns from the examples of data given to the algorithm.

Module 3. Introduction to data for machine learning

Data is very important in Machine Learning because ML algorithms work on training and testing data. Before they start to work in the real world, ML models are trained and tested on huge data to output more accurate results. Data is always in raw form and we need to make it error-free and clean the data such that it can be used for training ML models.

Module 4. Explore and analyze data with Python

In this module, you will learn how to use Python libraries like NumPy and Pandas to explore and manipulate data for ML models. You will also learn how to visualize data in Python with Matplotlib.

Module 5. Train and understand regression models in machine learning

Regression models are one of the most famous and used techniques in Machine Learning. It is widely used for scientific discoveries, business planning, and stock market analytics.

Module 6. Refine and test machine learning models

Module 7. Train and evaluate regression models

Module 8. Create and understand classification models in machine learning

Module 9. Select and customize architectures and hyperparameters using random forest

Module 10. Confusion matrix and data imbalances

The confusion matrix is an ML technique used to check the accuracy of classification models. So, in this model, you will be learning this important table of ML and other related important formulas used with this table.

Module 11. Measure and optimize model performance with ROC and AUC

ROC stands for Receiver operator characteristic and AUC stands for Area Under Curve. These both are used to measure the performance of a model in ML.

Module 12. Train and evaluate classification models

Module 13. Train and evaluate clustering models

Module 14. Train and evaluate deep learning models

Deep learning is a more advanced study in Machine learning. Deep learning focuses more on neural network concepts rather than just data given to ML models during training. Deep Learning is more complex than ML because deep learning tries to mimic the human brain. You will have a very basic overview of Deep Learning in this last module of the Free Machine Learning course by Microsoft.

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