Welcome to the future..! In this article, we will be dealing with how to learn Machine Learning.
We know that humans can learn a lot from their past experiences and that machines follow instructions provided by humans. But what if humans can train the machines to learn from their past data and perform human activities at a faster pace.
Well, that’s Machine Learning. It’s not just about learning, but understanding and reasoning too.
In this article, I’ll recommend some of the best courses to learn machine learning according to my research. The links to these courses are also provided.
Before we take a look at that, let’s discuss a few basic things about machine learning that you must know. If you’re not interested in learning these, you can skip these and move directly to the recommended courses.
Machine Learning- An Overview
Before we move onto the deeper side of Machine Learning, I would like to efface the confusion between the words Artificial Intelligence (AI) and Machine Learning, since they are being interchangeably used by many.
AI is the broad science of mimicking human abilities, whereas Machine Learning is a subset of AI that trains a machine on how to learn.
Machine Learning is not just explicit coding like in AI; Here examples(a large amount of data) are fed, and the machine learns from those examples.
It’s a huge difference because it’s much easier for us to provide examples than hectic coding.
Take a look at the so-far best outcome of Machine Learning- the heavily hyped, self-driving car from Google:
It has got lasers on the top that provides information to the car in terms of it’s surrounding area and radars in the front that keeps informing the car about the speed and motion of all the vehicles and obstacles around.
The car uses all these data not only to figure out how to drive but also to predict what the drivers around are potentially about to do. So it’s almost a GB of data per sec that the car is processing.
Some other applications of Machine Learning include detecting spam emails, recognizing handwritten digits, fraud detection in transactions, face app predictions, etc.
Check out some of the fundamental sections you need to cover in machine learning.
Machine learning is very much related to Statistics, and the base of machine learning is set on certain mathematical principals like probability and statistics, linear algebra, and calculus.
So it’s appropriate to know the fundamentals of Statistics and probability theory, descriptive statistics, sampling, hypothesis testing, regression and decision analysis, basic matrix operations, and the basics of differential and integrational calculus.
A little bit of programming skill, along with an understanding of Data structures, algorithms, and OOPs concepts is required.
Python is the heavily used language in machine learning due to its libraries like numpy, pandas, sci-kit learn, tensorflow, keras, etc. Other languages like R, Java, and C++ also come up with machine learning, but it all depends on the amount of data set you put to use.
Machine Learning Frameworks
Machine Learning Framework is a library or tool that helps us in creating machine learning models easily without being aware of the underlying algorithms. Some of the widely used frameworks include Scikit learn, TensorFlow, Azure, Caffe, Scipy, Pandas, Numpy, etc.
Mastering any one of these frameworks is very much essential at the implementation level of Machine Learning.
Ability to work with a huge amount of data( big data), data processing, knowledge of SQL and non SQL, ETL(Extract Transform Load) operations, data analysis and visualization skills are also some of the stepping stones in the journey of machine learning.
An idea of some of the important machine learning algorithms like linear regression, logistical regression, decision trees, random forest, clustering, reinforcement learning will also be helpful in better machine learning.
You might be thinking that this is a big ask. But do not worry. You might not need all these at the beginning. I’ll recommend some courses that you can follow easily to build your career in machine learning.
Best Courses To Learn Machine Learning
Some of the best online resources for learning machine learning and their links are provided below. Dive into each and choose the one that fits your comfort.
Here are the best ML courses available on Coursera.
Machine Learning with Python (offered by IBM)
Here all the fundamental concepts of Machine Learning are covered along with a final project. Click here to know more about it.
Applied Machine Learning with Python (by University of Michigan)
It has a different approach since they introduce the learner to applied machine learning, focusing more on the techniques and methods than on the theory or statistics behind these methods. They also introduce scikit learn through a tutorial. Click here to check the syllabus and more details.
Machine Learning Foundations: A Case Study Approach (by the University of Washington)
This is a highly recommended course where you will get hands-on experience with machine learning from a series of practical case-studies, which will be helpful in applying machine learning methods in a wide range of domains.
Click here to check out the course.
Introduction to TensorFlow (by Google Cloud)
Google and TensorFlow is a deadly combination. Check out the course details by clicking here.
Applied Data Science with Python specialization (by University of Michigan)
With a rating of 4.5/5, it is one of the most enrolled courses online for Machine learning. Click here to learn more details about it.
EdX is yet another great platform for online learning. Check out its course for machine learning.
Machine Learning Fundamentals (by University of California, Santiago)
In this free course, you can learn a variety of supervised and unsupervised algorithms and also the theory behind those algorithms. The course is provided in Python language. It is one of the highly chosen courses. Click here to check more details about the course.
Microsoft Professional Program in Artificial Intelligence
It’s a vast program with 9 courses and a final project with 8-16 hours per course. So we can make sure that each topic will be explained with ample time. Though it’s a bit costly, I am very much sure it will be worth it. Click here to learn more about the course.
Udemy needs no introduction as it is a popular online course platform. Check out the best Udemy courses for machine learning.
- Machine Learning A-Z: Hands-on Python & R In Data Science
- Python for Data Science and Machine learning Bootcamp
- Machine learning, Data Science and Deep Learning with Python
- Data Analysis with Python and Pandas
One of the advantages of these Udemy courses is that they dive into the coding section earlier rather than spending too much time in associated theory portions. They focus more on the experimental part of machine learning. I would say that these Udemy courses are very helpful.
Udacity offers two nano degree programs related to machine learning.
These 3-month courses are intended for students with prior knowledge of machine learning algorithms and software engineering fundamentals.
Data School course: Introduction to Machine Learning in Python with scikit learn (video series)
This course covers all aspects of Machine learning from basics to advanced level. Click here to check out the course.
Imarticus Machine learning and Deep learning Pro Degree in collaboration with IBM
It is one of the first platforms to provide a 145+ hour certification course providing in-depth exposure to data science, big data, machine learning and deep learning. The program includes seven industry projects, numerous case studies, and periodic interaction with industry leaders.
Click here to check out the course.
These are some of the prominent courses you can find online. Go through them and pick your favorite one. Constant practice and determination can aid you in becoming a better Machine Learning expert.
Invest yourself today in Machine Learning, for it will truly be a game-changer in the near future.
I hope this article was helpful. If so, do share it so that others can also find it.