How to Learn Python Machine Learning

Behind many of the most famous websites in the world are machine learning algorithms. Netflix uses algorithms to determine what movies to recommend based on what you have seen. Amazon uses data to recommend items you may want to purchase from their website.

Python is one of the most popular languages used for machine learning. In this guide, we will walk you through a few resources you can use to build and refine your Python machine learning knowledge.

What You Need to Know About Python Machine Learning

Machine learning is a cutting-edge field that is constantly evolving. As such, new techniques and technologies are created every year. If you want to write machine learning programs with Python, there are a few basic concepts that you should familiarize yourself with:

• Machine Learning Theory: What is machine learning? When is machine learning used? What are the main types of machine learning? What is regression? These are all questions you should be able to answer before you start writing machine learning code.
• Data Analysis Packages: Once you understand the basic theory behind machine learning, you are ready to start working with some data analysis packages. Look at NumPy, Pandas, and Matplotlib and see which ones you should learn. These packages will set you up for success in machine learning.
• Using Jupyter Notebook: A lot of Python machine learning exercises involve using Jupyter Notebook, a tool for data analysis, machine learning, and expressing your findings from a dataset. You should become familiar with this tool.
• Mastering Scikit: After you are familiar with some data analysis packages, you are ready to start using scikit-learn, a library used for machine learning. You should spend time learning about topics like regression, building models, data structures, and the other concepts associated with scikit-learn.

This is a high-level overview of what you need to know. Within these four bullet points, you will find months’ worth of work. You may spend a few days learning the basics of NumPy alone, nevermind the other libraries we mentioned. 

In terms of specific machine learning concepts, these are some of the topics you will need to learn:

• Data preprocessing
• Regression, classification, and reinforcement learning
• Clustering
• Deep learning
• Selecting models
• Regression
• Supervised and unsupervised learning
• K-means algorithms
• Variance vs. bias
• Recommender systems
• Designing hypothesis and building models

That is one thing you have to remember about machine learning with Python: there is a lot to learn and no easy shortcuts. You need to stay dedicated and focused on your goal of learning how to write machine learning programs.

Skills Needed to Learn Python Machine Learning

To learn Python machine learning, you will need a working understanding of the Python programming language. You should feel comfortable writing more advanced Python programs and know exactly how the code you write works.

Ideally, you should have a good understanding of data analysis too, as well as Python data science packages. As we mentioned earlier, data science packages come up quite a bit in machine learning, so understanding them is key.

Mathematical knowledge is useful because machine learning involves a lot of numbers, calculations, and statistics. You do not need to have advanced mathematical knowledge to learn the basics of machine learning but you will need to be willing to refine your mathematical abilities as you learn.

Why You Should Learn Python Machine Learning

This is a growing field and businesses are still exploring exactly how they can leverage machine learning. As a result, there are plenty of opportunities to work on machine learning projects, and more opportunities will open as we discover new machine learning applications.

Today, machine learning is used by search engines, streaming websites, e-commerce platforms, and social networks. If you are a competent machine learning engineer, you will have no trouble finding employment at a technology company. Fortunately, salaries are commensurate with the amount of work involved in learning machine learning, as we will see later in this guide.

How Long Does it Take to Learn Python Machine Learning?

Learning to write machine learning code in Python takes a long time. Assuming you dedicate an hour a day to studying, expect to spend about six months to a year on the basics. If you want to become a professional, you will need to dedicate more hours every day or study over a longer period of time.

You will spend a while learning to write machine learning code in Python because of the sheer number of tasks and topics involved. You will learn a lot about data analysis. Likewise, you will become familiar with scikit-learn. You are also likely to find yourself researching mathematical concepts that you encounter as you learn. All of this adds to the amount of time you will spend studying.

Learning Python Machine Learning: A Study Guide

Machine learning is still a relatively new field, but there are plenty of courses and resources you can use to build your skills. Below, we have listed some top machine learning courses you can leverage to advance your knowledge. 

Machine Learning by Stanford on Coursera

• Resource Type: Course
• Price: Free
• Audience: Beginners to machine learning

Over the course of 11 weeks, this class will introduce you to a range of machine learning concepts, from foundational knowledge to advanced topics like recommended systems. You will not only learn about machine learning theory but also gain the knowledge necessary to apply what you learn to the real world.

This course is taught by Andrew Ng, the founder of DeepLearning.AI and the co-founder of Coursera. Ng is an expert on the topic of machine learning.

Become a Machine Learning Engineer Nanodegree by Udacity

• Resource Type: Course
• Price: $399 per month
• Audience: Beginners to machine learning (intermediate Python required).

Through this Udacity Nanodegree, designed in collaboration with Kaggle and Amazon Web Services, you will acquire the knowledge needed to become a machine learning engineer. The course starts with software engineering fundamentals then moves on to machine learning in production. It culminates in a capstone project where you will have to work on a specific problem.

According to Udacity, assuming you study for ten hours each week, this course takes three months to complete.

Machine Learning A-Z™: Hands-On Python & R In Data Science on Udemy

• Resource Type: Course
• Price: $118.26
• Audience: Beginners to machine learning

Featuring 75 articles, 38 downloadable resources, and 44 hours of video content, this course covers the fundamentals of machine learning in both Python and R.

You will learn data preprocessing, regression, classification, clustering, and other machine learning techniques. The course culminates in a section on model selection and boosting, helping you acquire the knowledge you need to successfully approach a range of machine learning problems.

Machine Learning Mastery Blog – Python Resources

• Resource Type: Website tutorials
• Price: Free
• Audience: Anyone with Python experience

The Machine Learning Mastery website features extensive blog posts and tutorials on machine learning. You will find introductory guides to machine learning, articles on how statistics are used, and more.

On this site, there are also plenty of Python machine learning tutorials that refer to the scikit-learn library. For instance, there is an introduction to using scikit-learn and a walkthrough for your first machine learning project in Python.

Machine Learning Mastery With Python Book

• Resource Type: Book
• Price: $37.99
• Audience: Anyone with Python experience

Jason Brownlee from Machine Learning Mastery has authored a book on writing machine learning programs in the Python programming language. It features 16 lessons and three projects to work on. There are also example programs that will aid your learning throughout the book.

This resource is excellent because it gives you all the information you need to get started with Python machine learning. You will find chapters on topics like feature selection, visualizing data, and model selection.

Communities for People Studying Python Machine Learning

What communities can you turn to for support as you learn Python machine learning? Good question. There are plenty of machine learning communities online where you can ask questions, meet other developers, and learn from experienced coders.

Below, we have curated a list of three top communities for aspiring and existing Python machine learning developers.

Kaggle

Kaggle is perhaps the best-known community for data science and machine learning. It features a range of datasets to practice your skills, public notebooks from which you can learn, and competitions you can join.

You can run all of your machine learning and data science code in Kaggle’s online Jupyter Notebooks environment. You can easily share your notebooks with other people to get feedback, either with developers you know or in the Kaggle community itself.

r/MachineLearning Reddit Community

The r/MachineLearning Reddit community features a wide range of threads about machine learning. This community is a great place to ask machine learning questions, learn about topics you are struggling to understand, and help other people with their questions.

Data Science Stack Exchange

The Data Science Stack Exchange community features thousands of questions and answers related to machine learning, from finding publicly-available data sets to preparing data for an LSTM network. Like on Reddit, you can ask questions related to machine learning, view answers, and ask for clarification if you do not understand a thread.

You may also find a lot of answers to questions on specific tools, like NumPy, on the Stack Overflow programming forum. 

How Hard Is it to Learn Python Machine Learning?

Machine learning is a difficult field to master because there are a lot of concepts you need to learn. You will have to study mathematics, statistics, programming, data analysis, and more. However, if you practice hard enough and spend enough time studying, you will be able to learn machine learning. 

Will Learning Python Machine Learning Help Me Find a Job?

Machine learning is an in-demand skill and Python comes up often in machine learning job descriptions. To help you understand the career prospects for a machine learning engineer, we have put together a few employment statistics:

• Salaries: According to Glassdoor, the average machine learning engineer earns $114,121 per year. This amount is much higher than many other programming jobs, like software engineer or web developer.
• Job Openings: Glassdoor reports there are 19,478 jobs open for machine learning engineers in the US at the time of writing.
• Industry Growth: According to the US Bureau of Labor Statistics, jobs in computer and information research science—a category that includes machine learning engineers—are expected to grow at a rate of 15% between 2019 and 2029.

Taking all of these statistics into account, it becomes clear that machine learning engineering is a lucrative career path if you are willing to put in the work needed to pursue a job in the field.

Conclusion: Should I Learn Python Machine Learning?

If you want to become a machine learning engineer, Python is an excellent language to use. Python has many well-documented packages, like scikit-learn, that were built specifically for machine learning. There are even more packages for data analysis.

Learning to write machine learning code in Python is no easy task. You will need to spend months learning and refining your skills. But as long as you stay focused on your goal, your efforts will be rewarded: there are plenty of job openings in the field and machine learning engineers are paid very handsomely.

To help you evaluate whether you should learn Python machine learning, we have prepared a few questions you should ask yourself:

• Am I willing to dedicate months to learning machine learning?
• Why do I want to learn machine learning? Do I want to become a machine learning engineer or would a field like data science be more interesting?
• What are my long-term career goals?

Whatever learning path you decide to take, we wish you the best!