Building Machine Learning Models in Python with scikit-learn

This course course will help engineers and data scientists learn how to build machine learning models using scikit-learn, one of the most popular ML libraries in Python. No prior experience with ML needed, only basic Python programming knowledge.

The Python scikit-learn library is extremely popular for building traditional ML models i.e. those models that do not rely on neural networks. In this course, Building Machine Learning Models in Python with scikit-learn, you will see how to work with scikit-learn, and how it can be used to build a variety of machine learning models. First, you will learn how to use libraries for working with continuous, categorical, text as well as image data. Next, you will get to go beyond ordinary regression models, seeing how to implement specialized regression models such as Lasso and Ridge regression using the scikit-learn libraries. Finally, in addition to supervised learning techniques, you will also understand and implement unsupervised models such as clustering using the mean-shift algorithm and dimensionality reduction using principal components analysis. At the end of this course, you will have a good understanding of the pros and cons of the various regression, classification, and unsupervised learning models covered and you will be extremely comfortable using the Python scikit-learn library to build and train your models. Software required: scikit-learn, Python 3.x.

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