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Introduction to Machine Learning in Python

Machine learning is one of the hottest new technologies to emerge into popular consciousness in the last decade, transforming fields from consumer electronics and healthcare to retail. This has led to intense curiosity about this field among many students and working professionals about the field.

If you’re a tech professional such as a software developer, business analyst or even a product manager, you might be curious about how machine learning can change they way you work and take your career to the next level. However, as a busy professional, you’re also looking for a way to get a solid understanding of machine learning that’s not only rigorous and practical, but also concise and fast. This machine learning tutorial will help you achieve your goals.

Why Study Python Machine Learning?

There are many wonderful free online resources to get started on machine learning. However, we’ve curated this learning path with the following aims in mind:

  1. Python-based: Python is one of the most commonly used languages to build machine learning systems. Most of the resources in this learning path are drawn from top-notch Python conferences such as PyData and PyCon, and created by highly regarded data scientists.

  2. Hands-on material: Many of the materials we have included are hands-on tutorials that come with code and real-world data sets, that’ll help you get a practical understanding of the techniques that we’ll cover.

  3. Concise and fast: For someone with a strong technical background, this path should take 20-25 hours to complete. Depending on the amount of time you dedicate, you should be able to complete this in  2-4 weeks, rather than several months for most online machine learning courses.

At the end of this learning path, you’ll have a clear idea of what machine learning is, what the most common techniques in the field are, and through hands-on tutorials, learn how to implement actual machine learning systems in Python. 

What will I learn?

  1. The most common supervised learning and unsupervised learning algorithms, from linear regression to logistic regression to k-means clustering to random forest and other decision tree techniques.

  2. How to use Pandas and Numpy to accomplish various data mining and data wrangling tasks to process your input data into useable training data .

  3. How to use scikit-learn, a powerful tool, to comb over your available data and implement practical machine learning techniques.

  4. How to use computer science techniques to build the foundation of artificial intelligence, big data and predictive models.

  5. How to build basic deep neural networks that represent the cutting-edge when it comes to reinforcement learning and deep learning in machines.

Who is this for?

You’re comfortable programming in at least one language and curious about transitioning to data science. In particular, you want to have a strong understanding of what machine learning is, what are the different techniques in machine learning and what it can actually do. You want to understand how to work with this new technology with a free machine learning tutorial.

If you're looking to break into a data science career, consider checking out our new data science career track bootcamp with personalized mentorship from data science experts and career coaching. 

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