Review — Is Introduction to Machine Learning Specialization by IBM on Coursera Worth it?
Is Introduction to Machine Learning certification by IBM on Coursera worth it? Should you join this specialization for Machine Learning?
Hello folks, if you are interested in Machine learning and looking for the best Machine learning course online or you are thinking to join Introduction to Machine Learning Specialization by IBM on Coursera but not sure whether it's worth it or not then you have come to the right place.
Earlier, I have shared the best Data Science and Machine Learning courses, and in this article, I will review IBM’s popular Introduction to Machine Learning Specialization in Coursera.
IBM doesn’t need an introduction, it's one of the oldest and most reputed software and technology company and anything coming from IBM should definitely be of top-notch quality and this course is no exception.
It's 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 one thing that 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, and the example is endless.
This science ranked the 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 used data to learn and make decisions or predict based on the data learned from. 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 in Coursera platform with many courses to learn.
Review of IBM’s Introduction to Machine Learning Course on Coursera
Now that you know what this course is and who should join this course let’ 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 and one of the instructors is Mark J Grover, and he was a full-time professor in computer technology and has experience in computer security, networking, and worked in Cisco company and the other instructor is Miguel Maldonado which 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.
Just take a look at some of his Machine learning courses, they are all awesome.
2. Course Content and Structure
This Coursera specialization is nothing but a collection of Machine learning 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.
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 and 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.
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 of this, such as ridge regression, LASSO regression, and elastic net.
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 also learn about 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.
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 also 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 that is suitable for your data. Finally, dimensionality reduction is a powerful technique to deal with big data and pre-processing data.
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 course 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 from one of the pioneers and oldest industry leaders.
Here is the link to join this program — IBM Introduction to Machine Learning Specialization
When it comes to joining this course, you have two options, you can either join this course alone which costs around $39 per month for specialization, you can also join Coursera Plus for $399 per month, a subscription plan from Coursera which gives you unlimited access to their most popular courses, specialization, professional certificate, and guided projects.
Coursera Plus | Unlimited Access to 7,000+ Online Courses
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That’s all about the review of IBM’s latest Introduction to Machine Learning specialization. It’s one of the best Machine Learning Certification programs on Coursera, particularly for beginners? You can learn key machine learning concepts like supervised and unsupervised learning, regression and classifications, in great detail without getting bored.
The structure of the program and the content definitely make it worth money and the certification you receive from IBM And Coursera is like icing on the Cake.
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 engineer.
This course is a good introduction to learn the fundamentals of this field and understand how software that uses machine learning works.
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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 team. They have the best Machine learning with Python and R programming online courses on Udemy.