Tuesday, April 5, 2022

Is Introduction to Machine Learning Specialization by IBM on Coursera worth it? [Review]

Hello folks, if you are interested in Machine learning and looking for the best Machine learning course online, you have come to the right place. Earlier, I have shared the best Machine Learning and Data Science courses, and in this article, I will review IBM's popular Introduction to Machine Learning Specialization in Coursera. IBM doesn't need an introduction, and this is a great course for anyone who wants to learn about Machine Learning, its importance, and its impact.  This course is carefully built by IBM's Machine Learning experts to teach you Machine learning through real-life examples and use cases; that's also what makes it different from other courses. 

Machine learning is an important part of many of the daily software that we use, like spam filtering in your email and detecting fraudulent transactions when someone sends you money, and even when tesla, the self-driving car, takes care of the wheel the example is endless. 

This science ranked number 1 in the united states a couple of years ago with growth of 344$ in just three years between 2015 and 2018 with an average salary of $128k a year.

Machine learning uses data to learn, make decisions, or predict based on the data learned. There are many algorithms in machine learning to use according to your data. 

The problem you are trying to solve, like labeled data, will use supervised learning algorithms, and unlabeled data will use unsupervised learning algorithms such as clustering.

Becoming a specialist in this field doesn’t need a degree or going to college anymore since many universities make this accessible from your home using a computer and an internet connection. Still, I recommend taking the IBM Introduction to Machine Learning Specialization on the Coursera platform with many courses to learn.

Review - IBM's  Introduction to Machine Learning Specialization on Coursera

Review of IBM's Introduction to Machine Learning Course on Coursera - Is it worth it in 2022?

Now that you know what this course is and who should join this course, let's review this Machine learning specialization in detail. We will review the course on three important parameters: instructor reputation and teaching style, Courser structure, content quality, what other people are saying about his course, and people's opinions.  This is my tried and trusted formula to review courses, and it has helped a lot. 

1. The Instructors Review

This specialization was created by two experts working in the IBM company. One of the instructor's names Mark J Grover, who was a full-time professor in computer technology and has experience in computer security, networking, and working in Cisco company, and the other instructor is Miguel Maldonado, who is also a  machine learning developer at IBM.

Both have awesome ratings and excellent teaching styles suitable for even beginners who don't know anything about the subject like Machine Learning

2. Course Content

This Coursera specialization is nothing but a collection of courses structured in a progressive way to teach you a skill. This specialization contains four courses or modules teaching you from Machine learning basics to important machine learning algorithms like Supervised and Unsupervised Machine Learning. 

2.1. Exploratory Data Analysis for Machine Learning

Before jumping into this specialization, you need to have prior experience with the python programming language and understand math like algebra and statistics. Starting the course by understanding artificial intelligence and machine learning, the history of artificial intelligence, and its application.

Next, you will learn how to get the data from different sources and clean it before feeding them to the machine learning algorithms to give a better result. 

Finally, you will learn about inferential statistics and hypothesis-testing that will help you measure the quality of your data before feeding them to the machine learning algorithms.

IBM Machine Learning Course review on Coursera

2.2. Supervised Machine Learning: Regression

Supervised learning is the most common and used type of machine learning, and it uses labeled data to train the algorithm and make a prediction. This section will learn more about supervised learning, the other types of machine learning, and the differences between regression and classification. 

Next, you will learn best practices to avoid overfitting in your training phase, which means the machine learning model is too attuned to the data, and you can avoid them by using the test split method and many other techniques you will learn.

Finally, Learn about regression with regularization, which will discourage learning a more sophisticated model to avoid overfitting. There are many techniques for this, such as ridge regression, LASSO regression, and elastic net.

Best Coursera course to learn Supervised Machine Learning

2.3. Supervised Machine Learning: Classification

The previous course talked about regression and its different types. Still, now we will move to classification that categorizes a set of data into classes and predict things like a cat, human, cars…etc.

Start by understanding more about classification problems and then learn its algorithms, such as logistic regression and its common error metrics. 

 Next, move to another classification algorithm known as K Nearest Neighbors, which is widely used and fast, and learn about the support vector machine algorithm.

Later, you will learn about the decision trees algorithm that enables you to make decisions based on some conditions and is widely used in machine learning classification tasks and learn about modeling unbalanced classes.

Best Coursera course to learn Machine Learning

2.4. Unsupervised Machine Learning

Supervised learning is another type of machine learning like supervised learning, but it uses non-labeled data to learn from the data and categorize the data based on some standards. You will learn more about this technique and how to use the K means algorithm to perform unsupervised learning.

Next, You will see how to select the right clustering algorithm suitable for your data. Finally, dimensionality reduction is a powerful technique to deal with big data and pre-processing data.

Best Coursera course to learn UnSupervised Machine Learning

3. People's Review

This is a rather new course from IBM on the Coursera platform. While IBM has many popular courses on Data Science and Artificial Intelligence, like IBM Data Science Professional Certificate and IBM AI Engineering Professional Certificate, this course targets beginners and other technical people who want to learn about Machine Learning through real-world use cases. 

More than 3K students have joined this specialization, and more than 22K people have joined various courses under this program. The cours also comes with financial aid if you cannot pay the fee to join this course. 

All these courses have, on average, 4.8 ratings which are just phenomenal, but it's no surprise, given IBM is behind these courses. They are designed for industry by one of the pioneers and oldest industry leaders. 


That's all about reviewing IBM's latest Introduction to Machine Learning specialization. If you want to learn Machine Learning or are interested in a career in AI, then this Coursera program is for you. Machine learning is a must skill to have in many job fields like data science and AI engineering. This course is a good introduction to learning the fundamentals of this field and understanding how software that uses machine learning works.

Other Coursera Resource ARticles you may like

Thanks for reading this article. If you like this review of Coursera and IBM's Machine Learning Specialization, then please share them with your friends and colleagues. If you have any questions or feedback, then please drop a note.

P. S. - And, If you are looking for the best Udemy online courses to learn With data science on Python, you can also check out the Machine Learning A-Z course by Kirill Eremenko and his teamThey have the best Machine learning with Python and R programming online courses on Udemy. 

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