What is Machine Learning?

Can we train a machine to distinguish a cat from a dog? We will start with an overview of machine learning and its applications, then we will look at the various machine learning algorithms which are broadly classified as supervised, unsupervised and deep learning (neural network) algorithms.

We will start with a short theory and jump into practical examples and applications for linear regression, logistic regression, Clustering, Support Vector Machines, K Nearest Neighbors. For each section, we will have project examples and work with real data sets. By using various libraries like Scikit-Learn, Pandas, Seaborn, Matplotlib, etc.. to do data analysis and visualization.

My suggestion is that you have a basic understanding of Python. If not you can watch my full Python Programming Course Part 1.

Full Python Course Part 1: https://youtu.be/nyrjl97WZhA

Subscribe: https://www.youtube.com/channel/UCo-3_hRV1Zk2dyvcIReeyKg?sub_confirmation=1

Data sets can be found here: https://github.com/sshumiye/Notes

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