Learn Machine Learning: The Best Online Courses

Machine learning is an expanding part of many businesses’ software systems. Organizations increasingly use machine learning to automate their systems and solve complex problems. But humans still play a role, and learning machine learning can be a great way to boost your job prospects.

The best part is that it is now possible to forgo a formal degree and instead take machine learning courses online. In this article, we cover the basics of machine learning, walk you through how to learn it, and survey the best courses available.

What Is Machine Learning?

Machine learning is a process whereby a computer or system learns through automated self-improvement. Without being coded by a programmer, the machine improves itself and performs various tasks at high speeds.

Machine learning is a subfield of artificial intelligence (AI). It is the result of years of research into methods for mechanizing human intelligence and consciousness. Eventually, researchers managed to build AI systems that could think and act like a human. Now such systems are capable of learning from data through a process of trial and error.

Today machine learning is used to analyze data. The analysis is then used to make improvements to existing processes and practices. Businesses are heavily reliant on machine learning to study data, notice patterns that humans can’t see, and chart a path forward. 

What Is Machine Learning Used For?

Machine learning allows systems to improve and change based on the data provided by the user. Nowadays, this unsupervised learning method is being applied in almost every industry. Below are just some of the ways that the modern world employs machine learning.

• Image Processing. You can design and work on machine learning algorithms to detect specific features in images and identify who or what the image represents. In facial recognition technology, for example, machine learning uses data to correctly identify your face and unlock your device.
  
  
• Traffic Prediction. Machine learning skills are also used to design applications that use Google Maps to reroute you based on current traffic conditions. Once collected, the traffic data can be used to make traffic predictions in the future.
  
  
• Product Recommendation. Machine learning is also used by various E-commerce and entertainment organizations to recommend products to their users. It first analyzes web activity data to better understand what the customer wants, and then suggests various products based on what it has learned. 
  
  
• Dynamic Pricing. Machine learning is used to set the right prices for goods and services. It is used to help determine car fares, plane ticket prices, and movie ticket prices. The use of machine learning allows for automatic updating of prices in relation to both existing market trends and competitors’ prices.
  
  

Steps to Learn Machine Learning

Learning this technical subject can be difficult. To become an AI and machine learning expert, you will need to be determined, passionate, and innovative. Thus, it is important to have a plan in place to efficiently learn the subject. Below are the steps you can take to learn this subject.

1. Study Math and Programming Prerequisites

Before you start your learning journey, you need to grasp the fundamentals. Prerequisites for learning machine learning include linear algebra, statistics, calculus, and Python.

You can take online classes and enroll in data science bootcamps to master these required background subjects. Without them, understanding machine learning will be almost impossible. 

2. Learn Machine Learning 

After understanding the prerequisites, you can now move on to learning about machine learning. In this step, you will learn the terminology, technical concepts, and types of machine learning.

3. Familiarize Yourself with the Tools  

In this step, you will familiarize yourself with the machine learning tools that data scientists use to solve various problems. You should start by practicing with tools for beginners like Weka. Then move on to intermediate Python tools. Finally, you can try advanced tools like R.

4. Practice with Databases 

Remember, one of the essential parts of machine learning is a database. Machines collect data and store it in a database, which they then use to learn and improve independently. Getting a handle on database management and tools like SQL will therefore help you tackle real-world machine-learning problems.

5. Improve your Portfolio 

This is the final step in this learning process. Use your newfound machine learning skills to start a project portfolio or add to an existing one. By doing this, you can build a network and improve your job prospects. Start small with beginner Python projects and work your way up.

The Best Courses to Learn Machine Learning

Learning this subject is a multi-step affair. However, there are plenty of machine learning courses on platforms like Udemy and Coursera that can guide you through the maze. Below is a list of our favorites.

Machine Learning A-Z: Hands-On Python and R in Data Science

This Udemy course is one of the best machine learning courses out there. It equips you with the necessary knowledge to create machine learning algorithms in R and Python. It is taught by two experts in data science, Kirill Eremenko and Hadelin de Ponteves. 

This course is helpful because it has plenty of code examples that you can use to practice. You will learn how to make powerful and predictive machine learning models. You will also learn how to use advanced techniques such as DiMe, and explore topics such as deep learning and neuro-linguistic programming (NLP). 

Machine Learning by Stanford University 

This course on Coursera is taught by artificial intelligence and machine learning expert Andrew Ng. The course will teach you how to perform logistic regression and use artificial neural networks. 

In 60 hours, you will learn everything about machine learning’s mathematical techniques and how to come up with the best algorithms. You can pay for a certificate upon completion of the course.

Deep Learning A-Z: Hands-On Artificial Neural Networks

Another machine learning course from Udemy, this one helps you create machine learning algorithms using Python. This course is taught by two data science and machine learning pioneers and provides many great real-life examples. 

If you take this Udemy course, you’ll learn a lot about artificial neural networks. By the end, you will be able to apply neural networks in the real world. You will understand the science behind AI, getting a feel for convolutional neural networks and recurrent neural networks along the way.

Overall, this is a great course to learn about AI neural networks.

 

Introduction to Machine Learning

This Udemy course is a great introduction for those wanting to learn machine learning in a concise and breezy manner. You should, however, have background knowledge in Python programming, multivariable calculus, and linear algebra. 

The course covers the basics of machine learning algorithms. You will use Python coding for neural networks, as well as linear and logistic regression. Overall the course lets you gain more programming experience with Python. There is also an option for a paid certification at the end of the course. 

Machine Learning Specialization

This is a seven-month course provided by Coursera. It is hosted by researchers from the University of Washington. In this course, you will understand how to build machine learning applications, particularly those using deep learning.

You will also learn how to classify algorithms and get a better understanding of deep learning, regression analysis, and Python programming. The course will also teach you data clustering algorithms, decision trees, ridge regression, and Lasso statistics. 

This intermediate course is recommended to those who would like to move from the basic machine learning approaches to a more advanced level. You can earn a certificate upon completion. 

Why Learn Machine Learning?

The rapid acceptance of machine learning across many industries has led to the rise of many in-demand jobs. By learning this subject, you can become a part of this disruptive force in remaking the modern world. If you are interested in studying AI, innovating in IT, or becoming a data analyst, then don’t waste any time and start your journey.

Even if you are just interested in data science, having an understanding of machine learning through data science projects is crucial. Modern-day data management and analysis are completely intertwined with machine automation.

 

If that isn’t enough to convince you, the job outlook for a machine learning engineer has skyrocketed. According to Indeed, the job outlook for machine learning experts shot up by a whopping 344 percent between 2015 and 2019. And as a science and a technology, machine learning only continues to develop and evolve. Simply put, learning machine learning puts you in position for one of the best jobs in tech. It is linked to almost every tech-dependent profession, so having skills in this area will significantly boost your portfolio and raise your profile in the tech community.