Introduction to Machine Learning

Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks.[1] It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.

Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Glossary

Machine Learning is an extensive area of study and as you continue down this path it is essential you have access to a glossary of terms you will routinely encounter.

Google's Machine Learning Glossary is a great resource to keep at hand.

There are many courses available online to learn Machine Learning.

FastAI

Machine Learning FastAI course.

There are around 24 hours of lessons, and you should plan to spend around 8 hours a week for 12 weeks to complete the material. The course is based on lessons recorded at the University of San Francisco for the Masters of Science in Data Science program. We assume that you have at least one year of coding experience, and either remember what you learned in high school math, or are prepared to do some independent study to refresh your knowledge.

NPTEL

NPTEL Machine Learning Course

India's NPTEL (National Programme on Technology Enhanced Learning), is a joint venture of the IITs and IISc, funded by the Ministry of Education (MoE) Government of India, and was launched in 2003. Initially started as a project to take quality education to all corners of the country, NPTEL now offers close to 600+ courses for certification every semester in about 22 disciplines.

Ebook

An Introduction to Statistical Learning

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

The First Edition topics include:

  • Sparse methods for classification and regression
  • Decision trees
  • Boosting
  • Support vector machines
  • Clustering

The Second Edition adds:

  • Deep learning
  • Survival analysis
  • Multiple testing
  • Naive Bayes and generalized linear models
  • Bayesian additive regression trees
  • Matrix completion

Tips for Beginners

Set concrete goals or deadlines.

Machine learning is a rich field that's expanding every year. It can be easy to go down rabbit holes. Set concrete goals for yourself and keep moving.

Walk before you run.

You might be tempted to jump into some of the newest, cutting edge sub-fields in machine learning such as deep learning or NLP. Try to stay focused on the core concepts at the start. These advanced topics will be much easier to understand once you've mastered the core skills.

Alternate between practice and theory.

Practice and theory go hand-in-hand. You won't be able to master theory without applying it, yet you won't know what to do without the theory.

Write a few algorithms from scratch.

Once you've had some practice applying algorithms from existing packages, you'll want to write a few from scratch. This will take your understanding to the next level and allow you to customize them in the future.

Seek different perspectives.

The way a statistician explains an algorithm will be different from the way a computer scientist explains it. Seek different explanations of the same topic.

Tie each algorithm to value.

For each tool or algorithm you learn, try to think of ways it could be applied in business or technology. This is essential for learning how to "think" like a data scientist.

Don't believe the hype.

Machine learning is not what the movies portray as artificial intelligence. It's a powerful tool, but you should approach problems with rationality and an open mind. ML should just be one tool in your arsenal!

Ignore the show-offs.

Sometimes you'll see people online debating with lots of math and jargon. If you don't understand it, don't be discouraged. What matters is: Can you use ML to add value in some way? And the answer is yes, you absolutely can.

Think "inputs/outputs" and ask "why."

At times, you might find yourself lost in the weeds. When in doubt, take a step back and think about how data inputs and outputs piece together. Ask "why" at each part of the process.

Find fun projects that interest you!

Rome wasn't built in a day, and neither will your machine learning skills be. Pick topics that interest you, take your time, and have fun along the way.

Excerpted from Elite Data Science

Survey of Machine Learning

One of the leaders in the field of Machine Learning, Sebastian Raschka has written a very extensive survey of the state of Python in Machine Learning.

This survey paper is recommended for graduates and professionals who wish to equip themselves quickly with an overview of various aspects of Machine Learning.