Hello there, If you want to learn Machine Learning and thinking whether Udemy's
Machine Learning A-Z - Hands-On Python and R in Data Science
an online course is worth it, nor then you have come to the right place. In this article, I have reviewed Udemy's Machine Learning A-Z - Hands-On Python and R in Data Science by Kirill Eremenko and Hadelin De Ponteves. Honestly, this
is one of the best Machine Learning courses you can get at an affordable price and is suitable for both beginners and intermediate programmers and people who want to pursue Machine Learning. If you are in a hurry, I suggest you join the
course, but if you have some time, then stay and read the full review to make an informed decision.
This industry, known as machine learning, has grown to be one of the most demanding fields in the IT industry because there are endless options and industries that machine learning can be applied such as chatbots, self-driving
cars, fake news detection, and much more.
Machine learning is the concept of making computers learn using data instead of being programmed, and Glassdoor has estimated the salary for a machine learning engineer is around $114,121 a year the good thing is that you don’t actually need a college degree to start a career in this industry because many online courses can teach you these concepts.
While I’m browsing the internet for some online courses to learn machine learning on platforms like Coursera, Educative, and Pluralsight, I have landed on a Udemy course that promises to teach you all of what you need to start a career in this industry, and you are now reading a review of that course.
Machine learning is the concept of making computers learn using data instead of being programmed, and Glassdoor has estimated the salary for a machine learning engineer is around $114,121 a year the good thing is that you don’t actually need a college degree to start a career in this industry because many online courses can teach you these concepts.
While I’m browsing the internet for some online courses to learn machine learning on platforms like Coursera, Educative, and Pluralsight, I have landed on a Udemy course that promises to teach you all of what you need to start a career in this industry, and you are now reading a review of that course.
Machine Learning A-Z - Hands-On Python and R in Data Science [Udemy Course Review]
Without wasting any more of your time, here is my review of
Udemy's Machine Learning A-Z - Hands-On Python and R in Data Science
. I have divided reviews into multiple sections taking into account the Instructor's reputation, content structure, what is covered in this course, and overall course material and delivery.
Kirill Eremenko: is a data science consultant with years of experience in the industry as well as a Udemy instructor of over 114 courses in different fields such as machine learning, tableau, deep learning, data science, python language, and much more with over 1.6 million students enrollments.
Hadelin de Ponteves: is also a Udemy instructor with over 80 online courses in different fields such as blockchain, deep learning, computer vision, artificial intelligence, and many more fields. He is also an entrepreneur and the founder of BlueLife AI.
1. The Instructor Reputation
Before start exploring the content let’s first introduce the two main instructors for this machine learning course:Kirill Eremenko: is a data science consultant with years of experience in the industry as well as a Udemy instructor of over 114 courses in different fields such as machine learning, tableau, deep learning, data science, python language, and much more with over 1.6 million students enrollments.
Hadelin de Ponteves: is also a Udemy instructor with over 80 online courses in different fields such as blockchain, deep learning, computer vision, artificial intelligence, and many more fields. He is also an entrepreneur and the founder of BlueLife AI.
And, here is the course image, looks stunning to me :
2.1. Data Preprocessing
The first step before applying machine learning algorithms to your data is preprocessing that data into the right format and this section is all about. You will see how to preprocess data using the two languages python and R.
2.2. Regression
The next step after preprocessing your data is applying some machine learning algorithms on that data and this section shows you six different algorithms such as simple linear regression, support vector machine, random forest, and much more as well as evaluating these algorithm's performance.
2.3. Classification
Machine learning regression algorithms used to predict continuous data but what about predicting categories? Well, this is what you will learn in this section using some algorithms such as logistic regression, kernel SVM, and much more with the pros and cons of every algorithm in detail.
2.4. Clustering
This section will teach you to use some clustering algorithms such as K-means clustering to perform grouping on some datasets based on some parameters and some of these algorithms are not good for big datasets.
2.5. Association Rule Learning
This section will teach you a technique you use to find the relationship between various items what’s knows as association rule learning and it used usually in the recommendation systems many more.
2.6. Reinforcement Learning
In an abbreviation, reinforcement learning is a subset of machine learning where the computer can make a sequence of decisions. This section as well shows you how to perform this using Python and R.
2.7. Natural Language Processing
NLP is a subset of machine learning where computers can work with text such as translation, speech recognition, and more. This section will introduce you to the NLP libraries and how to use it with python and R.
2.8. Deep learning
Machine learning designed to work with small to medium data but what about large data like Big data and more. Here comes the power of deep learning where you can create neural networks to deal with and process this large data.
2.9. Dimensionality Reduction
This technique is used to reduce or transform your data from a high-dimension to a low-dimension space because less variable makes it easy to be plotted and better for comparisons.
2.10. Model Selection & Boosting
Now after you have learned all of the machine learning and deep learning techniques and algorithms you probably confused about which one I need to use for my problem or project. Well, this section will teach you what algorithms and techniques you should use for your model or data.
You can clearly see that 54% of students have given five stars for the course and they are very satisfied with the course content as well as they recommend it to everyone who wants to start a career in this industry.
That's all on this review of Machine Learning A-Z: Hands-On Python and R in Data Science course on Udemy by Kirill Eremenko and his SuperDataScience team. This course teaches you really amazing things that will make you professional in this industry and I couldn’t even mention all things you will explore in this course so this is your chance if you want to become a machine learning engineer.
2. The Course Content
The nice things are that the instructors started with an introduction about what is machine learning and some other concepts before deep dive into the practical section of the course.2.1. Data Preprocessing
The first step before applying machine learning algorithms to your data is preprocessing that data into the right format and this section is all about. You will see how to preprocess data using the two languages python and R.
2.2. Regression
The next step after preprocessing your data is applying some machine learning algorithms on that data and this section shows you six different algorithms such as simple linear regression, support vector machine, random forest, and much more as well as evaluating these algorithm's performance.
2.3. Classification
Machine learning regression algorithms used to predict continuous data but what about predicting categories? Well, this is what you will learn in this section using some algorithms such as logistic regression, kernel SVM, and much more with the pros and cons of every algorithm in detail.
2.4. Clustering
This section will teach you to use some clustering algorithms such as K-means clustering to perform grouping on some datasets based on some parameters and some of these algorithms are not good for big datasets.
2.5. Association Rule Learning
This section will teach you a technique you use to find the relationship between various items what’s knows as association rule learning and it used usually in the recommendation systems many more.
2.6. Reinforcement Learning
In an abbreviation, reinforcement learning is a subset of machine learning where the computer can make a sequence of decisions. This section as well shows you how to perform this using Python and R.
2.7. Natural Language Processing
NLP is a subset of machine learning where computers can work with text such as translation, speech recognition, and more. This section will introduce you to the NLP libraries and how to use it with python and R.
2.8. Deep learning
Machine learning designed to work with small to medium data but what about large data like Big data and more. Here comes the power of deep learning where you can create neural networks to deal with and process this large data.
2.9. Dimensionality Reduction
This technique is used to reduce or transform your data from a high-dimension to a low-dimension space because less variable makes it easy to be plotted and better for comparisons.
2.10. Model Selection & Boosting
Now after you have learned all of the machine learning and deep learning techniques and algorithms you probably confused about which one I need to use for my problem or project. Well, this section will teach you what algorithms and techniques you should use for your model or data.
3. People Review
The course has got more than 700k student enrollment which is insane and few courses in the Udemy platform of all industries have got this number of enrollment and that proves the success and quality of this program.You can clearly see that 54% of students have given five stars for the course and they are very satisfied with the course content as well as they recommend it to everyone who wants to start a career in this industry.
Here is the link to join this course -
Machine Learning A-Z - Hands-On Python and R in Data Science
That's all on this review of Machine Learning A-Z: Hands-On Python and R in Data Science course on Udemy by Kirill Eremenko and his SuperDataScience team. This course teaches you really amazing things that will make you professional in this industry and I couldn’t even mention all things you will explore in this course so this is your chance if you want to become a machine learning engineer.
If you are serious about learning Python and Machine Learning in-depth, here are some more free and paid resources for Further Learning
Thanks for reading this article so far. If you like this Machine Learning A-Z - Hands-On Python and R in the Data Science course review, please share this article with your friends and colleagues. If you have any questions or feedback, please drop a note, and if you have a Machine Learning course or book that I should join or read, feel free to share it with us.
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Thanks for reading this article so far. If you like this Machine Learning A-Z - Hands-On Python and R in the Data Science course review, please share this article with your friends and colleagues. If you have any questions or feedback, please drop a note, and if you have a Machine Learning course or book that I should join or read, feel free to share it with us.
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