How to Learn Machine Learning: A Beginner’s Guide to the Best Courses
Many companies today rely heavily on the ability of machines they use to drive their business, creating a reliance on people who know how to use those machines. If you want to know how to learn machine learning, then you have come to the right place.
While there are many ways of learning more about the topic, we have summarised the process into five simple steps below. We will also highlight some of the machine learning resources available online, in books, tutorials, and articles, and introduce you to an online community to expand your knowledge base and grasp fundamental concepts.
As a machine learning beginner, you will need to conduct thorough research before signing up for any of the classes available. There are plenty of websites offering opportunities to learn, but you should also be aware of scammers.
This guide will walk you through what machine learning is, available machine learning training, and its technical application. With the growth of big data, the importance of mastering machine learning is on the rise.
What Is Machine Learning?
Machine learning is the process of applying artificial intelligence to help systems automatically improve experiences without being explicitly programmed. The algorithm uses statistics to find correlations in large amounts of data. The information drawn from the data allows the machine to make better decisions in the future.
The main purpose of machine learning is to help computers learn patterns automatically without manual assistance. The experienced-based system improves experience each time through training. By exposing more data to the system, the better the results will be in the future.
Machine learning is divided into two main areas: supervised machine learning and unsupervised machine learning. For supervised machine learning, the data is tagged with answers. For unsupervised machine learning, the training algorithm does not display any labels.
Through machine learning, companies like Netflix, Google Spotify, and YouTube can continuously improve their user experience. The process involves collecting data such as which songs you click on, the status you react to, the type of movie you keep watching, and the videos you keep replaying.
What Is Machine Learning Used For?
Machine learning is used for a wide range of applications. The process involves computers learning from previous computations and producing reliable and effective results repetitively. The machine can use complex mathematical equations on big data faster to achieve desirable results. Below are some of the industries that use machine learning.
The system is vital in industries that deal with big data, such as banks and financial institutions. It is critical in preventing fraud and providing key insights in seeking investors.
The system is essential to help identify potential problem areas before they arise. Through machine learning, companies can improve their transportation services significantly over a short period.
Doctors can easily use the process to siphon through a patient’s medical history, it makes it a much easier process to evaluate the patient in real-time as opposed to the traditional methods. In addition, it’s a great platform to improve medication and treatment plans.
The system helps businesses that have an online presence to market their products. By using machine learning, consumers are repeatedly recommended products to buy based on their previous purchases.
The public agencies that deal with multiple sources of data can use machine learning to establish key trends. It provides a great strategy to optimize services and minimize fraud or theft.
Learn Machine Learning: Step-by-Step
The most daunting part of learning any new skill or information is getting started. While most students want to augment their skills, they often lack the will to attend a class or read a book. Understanding the initial challenges of learning any new concept, we have devised a way to understand machine learning easily.
The step-by-step process below will help you acquire the relevant knowledge and skills you will need.
1. Select a Tool
There are plenty of machine learning tools available for you to utilize. Your choices include Weka Workbench, Python Ecosystem, or R platform, depending on your expertise level.
2. Watch Tutorials and Online Videos
Tutorials are essential in helping you further your skill. There are many experts on machine learning that upload videos on the topic. They can also run blogs and offer classes as well. You can use tutorials to gain more knowledge and experience and find out new developments in the field.
3. Read a Book
You might be surprised by the amount of information you can find in a machine learning book. Don’t overlook traditional book learning, books serve as a great source of insights for best practices.
4. Attend a Class
There are different types of classes available for you to learn more about the topic. You can sign up for any of the classes mentioned later in this article and gain credible insights.
The more you practice on different projects with versatile data, the better you become. You cannot afford to sit back and relax after acquiring a substantial amount of knowledge. You can easily further develop your skills and become an expert.
The Best Machine Learning Courses
The best machine learning courses available are those that are available to you at your own convenience. The following courses are led by instructors who are experts in their field, and available on platforms that have recorded numerous applications with positive reviews.
Kellogg School of Management: Start Building Your AI Strategy
This is an excellent course to learn how to incorporate AI into your business. This is one of the best courses available that is tailored to cut across multiple industries. You will learn AI strategies in finance, the auto industry, and healthcare.
The two-month-long course will be suitable for functional leaders, mid-career professionals, senior managers, c-level executives, and investors. You will learn from real-world examples such as Netflix, Airbnb, Danske Bank, Teradata, Oracle, and Microsoft.
Udemy: Machine Learning, Data Science and Deep Learning with Python
In this course, you will learn how to build a deep neural network, make predictions using linear regression, implement machine learning on a massive scale and classify data using support vector machine, decision trees, Naive Bayes K-Means clustering.
The instructor will also teach you how to build a movie recommender system, design and evaluate A/B tests, classify images, and data. He will also expound on data visualization, train K-fold cross-validation, and reinforcement learning.
To qualify for this course, you will need to have extensive experience in coding or scripting. A degree in computer science and proficiency in mathematics will suffice.
Deeplearning.ai: Deep Learning Course
This program is a five-week-long course, it is perfect for learning the foundations of machine learning. You will learn how to build neural networks and complete machine learning projects. In addition, the instructor will teach on RNNS, LSTM, BatchNorm, and convolution networks.
The course will also feature a session where deep learning experts will share their personal experiences and offer you free career advice. The case studies featuring the topic will cut across different healthcare industries, natural language processing, music generation, and autonomous driving.
MIT Professional Education: Machine Learning – From Data to Decisions
These lessons are useful in helping people understand more about the tools and techniques of machine learning. While a basic understanding of statistics will be helpful, the course does not require you to have any prerequisite knowledge of the topic.
The online program examines the practical nature of machine learning and is recommended for professionals who want to develop a competitive edge. Once you finish the lessons, you will receive a certificate of completion.
Udemy: Machine Learning Course A-Z™
This is one of the best programs to help you learn about machine learning in Python and R. You will learn how to make accurate predictions, build great intuition for models and handle tools such as reinforcement learning, deep learning, and NLP.
The 40-hour course will expose you to relevant information on the topic delivered by an expert Data Scientist. You will sit through plenty of quizzes and exercises to ensure you master the whole concept of the topic seamlessly. You will also receive detailed instructions on downloading and installing the software.
Stanford University: Machine Learning Course
The 55-hour course is one of the most popular programs on machine learning. In this class, you will learn about the best machine learning algorithms and techniques. You will learn how to implement and get them to work.
The instructor will give a broad introduction to the topic. He will also expand on supervised learning, unsupervised learning, AI, machine learning, and best practices. The course is based on real-life case scenarios and applications.
Fast.ai: Practical Deep Learning for Coders
This is a great course to learn about AI for free. The course will help you train models in computer vision, natural language processing, and tabular data. You will also learn how to turn models into web applications, why and how deep learning models work, and how to improve accuracy.
The instructor also touches on different techniques such as Affine functions and nonlinearities, random initialization and transfer learning, random forests and gradient boosting, convolutions, data augmentation, and batch normalization.
Columbia University: Machine Learning – Artificial Intelligence Course
The four-course program will teach you about the foundation and the guiding principles of machine learning. You will also learn how to apply that knowledge in real-life scenarios. In addition, the instructor will expand on how to design neural networks and use them to solve relevant problems.
You will be able to apply the information you gain in vision and physical simulation seamlessly. The one year course features plenty of exercises and assignments to help develop your skills further.
Fast.ai: Part 2: Deep Learning from the Foundations
This program covers topics using Python, fast.ai library, and PyTorch. You will learn how to build deep learning models from scratch, based on the aforementioned tools. The course will cover matrix multiplication, loss functions, optimizers, open-source code, data block API, machine learning models, and Swift basics for deep learning.
Columbia Engineering Executive Education: Applied Machine Learning
This program is exceptional, especially if you have a background in Python. It is a great course to expand your knowledge of supervised and unsupervised machine learning techniques using Python as a programming language.
The lessons are essential in learning the practical aspects of machine learning across different industries such as software development, healthcare, and retail. You will briefly cover data science concepts such as data manipulation and analysis, data visualization, and writing functions.
The five-month course will also teach you different regression models such as regularization, least squares, and linear regression. You will also gain a good grasp of Kernel methods and nearest neighbors. At the end of the course, you will earn a certificate in applied machine learning.
Is Learning Machine Learning Right for You?
There have been tremendous technological advancements over the past few decades and companies that have not adopted new methodologies risk being phased out. Machine learning techniques can easily provide benefits for businesses across different industries.
Machine learning is already well integrated into many parts of our life, both knowingly and unknowingly. Machine learning techniques have become highly reliable in face detection, product recommendation, surging prices, and feed content.
You can easily take your career to a new level by learning these techniques. And according to Forbes, by the end of 2024, machine learning will have grown into a $20 billion industry. The New York Times also points out there are very few people who are skilled in the area, meaning there are plenty of job opportunities for certified people.
Not to mention machine learning engineers earn an average of $146,000 per year. This is the highest paying job compared to other technology opportunities available. Learning machine learning will set you up for a great salary, as well as many other benefits.