“How is machinelearningplus university different from other courses?”. Other courses include those you see on Coursera, Udemy, Udacity, Youtube and courses offered by universities.
I think this is important to know, so let me try and answer this question.
First of all, let me clarify that some of these courses are taught by great accomplished people, especially the one by Prof Andrew Ng and they serve as inspiration for the machine learning plus courses.
We simply try to do better and make it more accessible to everyone.
We try to break things down further into smaller steps, take the effort to explain finer details and try to make it more exhaustive, while optimizing the time spent learning.
1. What makes it more accessible?
This is designed for anybody who has an interest in ML/AI.
If you can spend 5-6 months, you can definitely learn and pick it up. That means, there are no prerequisites whatsoever. You don’t need to know anything, including programming.
You will learn Python programming from scratch, then move on to Python for Data Science such as Pandas, Numpy and so on.
Secondly, the teaching is extremely simple. Instead of me speaking about this, it will be far better if you watch some of the videos (Use coupon: PANDASFREE25) and see for yourself.
We take pride in simplifying a lot of dense mathematical concepts without bogging you down.
If you are willing to learn, even an average student who can put 5-12 hours a week, for 3-6 months can surely learn. Of course, different people have different learning paces. So the only prerequisite is the effort and hard work.
2. Theory vs Programming
Another area where ML+ is different from other courses is this: most of the courses out there are either very theoretical in teaching or are more of code implementations and high-level logic.
Courses taught by universities and professors are heavy on theory. They don’t focus much on the programmatic and applied aspects of ML.
On the other hand, courses taught by Data Scientists and practitioners are very programming intensive without much focus on math. They take more of ‘write the code, get the results, understand a bit of the logic and move on’ approach without covering enough theory.
Machinelearningplus courses tries to strike a balance between the two approaches. That is, cover the theory clearly so everyone can understand plus cover the programming and application in industry.
3. Wide Content coverage.. and growing
We cover programming from scratch for mastering it for Data Science, machine learning algorithms, starting with a dedicated course on linear regression, logistic regression, supervised learning, ensemble learning, Time series, Deploying ML models, and special courses such as spaCy, caret package.
We cover 40+ most using machine learning techniques. Besides, for all the concepts you learn, you will learn the variations of it, what problems you could face, you will understand how to workaround in case you are stuck in any specific problem.
More courses in probability, stats, deep learning, advanced Time series courses etc are work-in-progress.
With that the breadth of content is pretty large and growing.
We take extreme pride in our customer service, especially questions raised in the course forums. IF you are stuck anywhere and have any doubt with understanding any part of the course, we are here to answer all your doubts and clear them out very fast.
You can raise as many questions as you want, we are here to clear all of that. You will get notifcations in your email as and when you get a response.
4. Portfolio Projects
A great way to amplify your profile is to have real world problems that you solved. Machinelearningplus has 7+ projects which you can showcase in your profile. This adds a lot of value in hiring decisions, and is important for your profile to get short listed for core Data Science roles.
Again don’t take any of my word for it. Simply sign up to one of the courses: Pandas (Use coupon: PANDASFREE25) or Enroll to the ML+ University for Complete ML mastery and start learning. It’s risk free.
This course is meant to break the Data Science entry barrier, inside our course forums you will see numerous students have successfully done so.
Hope this answers some of the questions.