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|
|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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