Best Free Resources For Programming, Software Engineering, and Data Science
Do you know that you can take the courses from MIT, Stanford, and Harvard for free? Lots of their undergraduate and graduate-level course materials are for the students around the globe to use for free. I am going to talk about some of the resources here. I know there are so many very bright and talented students in the different parts of the world who cannot go to all those great schools. But they have the potential to learn. In the tech industry, you can get a job even in Google, without a tech degree. Only the knowledge matters. If you are interested to put the time and effort, you can become a tech giant even if you cannot go to a big school. Here are the resources.
MIT
You will be amazed to know that you almost can get the course materials of all the undergraduate and graduate-level computer science courses free. You will find courses from all the areas of computer science including:
introduction to computer science and electrical engineering,
engineering problem solving,
Dynamic programming, software construction,
mathematical programming,
data science,
artificial intelligence,
robotics,
the mathematics of computer science,
software development, and web applications,
and much more.
Here is the link for their free courses page.
edx
This is a huge piece of gold if you are looking to learn online in any tech field and also other fields. I am only focusing on tech learning. You can take high-quality courses from big-name universities for free.
Learn whatever you want:
computer science,
software engineering,
web development,
mathematics,
statistics,
data analytics and visualization,
robotics,
artificial intelligence,
data science,
machine learning,
or any other engineering.
Search for your interest area in the search bar. You can audit the courses or pay to get a certificate. If you audit the courses, you will be able to learn but won’t get a certificate. If you go to the course from the catalog page, a lot of courses will say an amount per month or audit for free.
To find the audit option, click on the ‘Take Course’ button. On this page, there is the audit option.
Coursera
Another huge resource. There is a big pool of courses from all the well-known universities and well-known professors. You can learn almost all the areas I mentioned in the edx section. You just have choices. Try both edx and Coursera. Choose which works best for you.
In Coursera also, you can pay to get a certificate or audit the course to learn only. But finding the audit option is a bit trickier here. I am explaining here.
In the course description page, you will see the ‘Enroll’ option. But do not enroll from there. It will give only 7 days for free. Scroll down to the bottom of the page. You will find another ‘Enroll’ option. Click on that. A window will pop up. At the bottom of that window, you will see a small audit option. That’s what you are looking for.
Hackr.io
You will find great quality free courses of any trending programming languages such as:
python,
javascript,
Ruby,
C++,
PHP,
Java and more.
All the trending frameworks such as:
Node.js,
Angular,
React,
Django,
Laravel,
Ionic and more
SQL, and No SQL databases, and much more.
Codeacademy
Code academy also has a very good set of courses. It is especially good for beginners. It has an inbuilt IDE where you can write your code and feedback right away. So in the beginning you even do not need to set up your own IDE. Courses are free. But you can get a subscription for $20 a month to get help from their mentors.
Harvard
Harvard also gives a lot of their courses for free. This is the course catalog:
On this page, you will find some courses marked as free and some other paid courses. You can audit the free courses, in the same way, I explained in the edx section above. All the Harvard free courses are available in edx.
More Resources
There are a lot of other free resources as well. I am mentioning a few here.
Udacity
Udacity has a lot of nano degrees that are paid. Nt they have some good free courses as well. Those free courses are not expert level courses. They usually beginner level courses. Here is their course catalog. Search for free courses in the search bar.
Udemy
Udemy also has a lot of free courses. But not necessarily they are beginner level. Sometimes you may find good courses for free. They also keep giving discounts year-round.
Conclusion
I am sure, you agree after seeing all the free resources that it is not necessary to pay to learn programming or computer science now. The only problem is, it can be hard to focus and stay motivated to keep learning alone at home. If you can find a friend who wants to learn with you that is best. Otherwise, just accept the challenge and go ahead!
Here are some free resources to start with
I am a python user. So I can only give ideas about machine learning in Python. If you are a complete beginner and do not know python that well, practice that one to get better first. Here is a specialization for Python. It will teach you all the python syntax and structures with a lot of practice:
After that practice python to get better. There are several great platforms to provide us with practice problems. I use leetcode and checkio to practice programming. In these platforms, you can see other people’s solutions to get better. There are so many other platforms to practice programming as well: code wars, CodeChef are two more platforms I hear about a lot.
After learning to program well, it is a good idea to learn some computation, data manipulation, and visualization libraries of python. They are essential to learning before you dive into machine learning.
Python has powerful libraries like Numpy, Pandas, Matplotlib, Seaborn, Scipy, and more for computation, data manipulation, visualization, and statistical analysis. Here is a specialization series in Coursera that has two courses on Numpy, Pandas, Matplotlib, Seaborn, Scipy and the third course in on Applied Machine learning:
The applied machine learning course in this specialization does not teach you to develop the algorithms from the scratch. But it will teach you the concepts and how to use these algorithms from the scikit-learn library in python. This is a good start for a beginner. The University of Michigan offers this specialization. The five courses that are included in this specialization are:
Introduction to Data Science in Python
Applied Plotting, Charting & Data Representation in Python
Applied Machine Learning in Python
Applied Social Network Analysis in Python
This course has some good projects that will add to your portfolio. Also, each week will provide you with a notebook that can be used as a cheatsheet for your future workplace. The material they provide in this course is very good.
This is another specialization. It has four courses.
Machine Learning Foundations: A case study approach
Machine Learning: Classification
Machine Learning: Clustering & Retrieval
The great part about these courses is, these courses will take a project-based approach and each week’s assignment will be a different project. At the end of this, you will have a complete portfolio to show off. The University of Washington offers this course.
CS50’s courses usually very high quality. This course is offered by Harvard University. And you know that you do not expect any less from Harvard. As the title says this is an introductory course. This course will give you some more concepts of machine learning that the previous two courses do not. After taking the previous courses if you take this one, you will learn more models and concepts and also include more projects to your portfolio.
This course will cover graph search algorithms, adversarial search, knowledge representation, logical inference, probability theory, Bayesian networks, Markov models, constraint satisfaction, machine learning, reinforcement learning, neural networks, and natural language processing.
Professor Andrew Ng is a famous professor for his great ability to break down the machine learning concepts. This course is offered by the Stanford Univesity. This course is different than the previous three courses. The three courses above teach you how to use the machine learning algorithms that are built-in python’s libraries.
But Professor Andrew Ng will teach you how to develop the machine learning algorithms from scratch. So, it is lot harder than the previous courses.
But if you can finish it, it will give you a lot of power. It is an eleven weeks long course. But you can audit this course as many times as you want for free. This course will teach you to develop linear regression, logistic regression, neural networks, support vector machine, k mean clustering, principal component analysis, anomaly detection, recommendation system development from scratch.
One thing that may be a bit different about this course that is the assignment instructions are in Matlab. But if you are good at python, you can take the concepts and do them in python. You will find the links to most of the assignments done in python in this page:
I am still working on writing tutorials on the rest of the assignments in python and will be done with them soon.
It looks like a lot! right?
But learning the machine learning libraries will be easier. After you learn to use a couple of algorithms, it will be easier for you to pick up after that. But learning the algorithms from scratch in Andrew Ng’s course will take a lot of time.
These are all the courses I wanted to share for machine learning. Some deep learning courses here.
DeepLearning.AI TensorFlow Developer Professional Certificate
This is also a specialization. Now they upgraded it and made it a professional certification course on Tensorflow. This series will teach you the use of TensorFlow with projects. The course is not that hard. Because it does not teach you how to develop the deep learning algorithm from scratch. it will teach you how to use the TensorFlow library.
Tensorflow is a very powerful tool for deep learning. It will take care of all the hard mathematics behind the scene. You just need to install it, call the library, and use it.
This specialization will teach you to use TensorFlow for numerical prediction, natural language processing, image classification, and time series prediction.
These are the four courses in this specialization:
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Convolutional Neural Networks in TensorFlow
Natural Language Processing in TensorFlow
Sequences, Time Series, and Prediction
Each of these courses takes a project-based approach. So, it is fun to learn!
This is another series of courses from Professor Andrew Ng. It is hard to avoid Professor Ng if you are trying to learn machine learning and deep learning. He is one of the pioneers!
He teaches the concepts very clearly and teaches you to develop the algorithms in detail. This course will be a bit harder because it is about developing the algorithms from scratch and know it from its core. But it will be worth it if you can finish it. It includes these following courses:
Neural Networks and Deep Learning
Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization
Structuring Machine Learning Projects
Conclusion
If you can dedicate your time to these courses, you are a pro in machine learning and deep learning. There are a lot of other libraries and topics out there. Because machine learning is a vast field and it is growing every day. But if you have a strong foundation you will pick up any other new libraries fast.
You have to stay open-minded about that. This is a field where learning will never end. No matter how much you learn, a new thing will come up tomorrow.
One last suggestion is that, do not jump into learning anything new to you. Master a few libraries and algorithms first. That will develop judgment in you. You will understand which one is important for you and what is your interest.
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