How do Start to learn Machine Learning?

In this post you will know how should i start to learn Machine Learning from scratch. ML has changed how data-driven business leaders make decisions, gage their businesses, study human behavior, and view predictive analytics. If your organization needs to unleash the benefits of this extraordinary field, you need the right minds—quants and translators.

With breakthroughs such as parallel computation that’s cheap, Big Data, and improved algorithms, utilitarian AI is what the world is moving toward. The increased need to handle huge amounts of data and the number of IoT connected devices that define the world today reinforce the importance of machine learning.

So as to learn machine learning, you should be better than average at math. Here are the maths you should learn keeping in mind the end goal to be prepared.

• Linear algebra-Linear Algebra– MIT 18.06 Linear Algebra by Gilbert Strang
• Probability theory-Probability and Statistics– MIT 6.041 Probabilistic Systems Analysis and Applied Probability by John Tsitsiklis
• Calculus
• Multivariate Calculus
• Graph theory
• Optimization methods
• Any programming language that is widely used for ML such as python, MATLAB or C++.

First you need to be trained with the appropriate basis in mathematics and computer science. In the case of deep learning, you can see part 1 of the MIT Press Deep Learning book (available online for now, eventually MIT Press will have a real paper book) to either brush up on these or see which areas of math and CS are most relevant. Then you need to read on machine learning (there are several good books, such as Chris Bishop's and Kevin Murphy's, online videos such as Andrew Ng's coursera's class and Hugo Larochelle's videos on neural networks, and you can get a summary of many of the basic issues in chapter 5 of the Deep Learning book). Then you need to start practicing, i.e., programming some learning algorithms yourself and playing with them on data, try to compete in some Kaggle competitions, for example.  Try to become an expert at optimizing hyper-parameters and choosing models appropriately. In parallel, continue reading. If you are interested in deep learning, part 2 of my book will give you the basis for the most common algorithms. At that point you should have enough background to start a steady regimen of reading papers that tickle your fancy.

 Take an online course

The main thing I advise somebody who needs to get into machine learning is to take Andrew Ng’s online course.

I believe Ng’s course is especially to-the-point and exceptionally efficient, so it is an extraordinary acquaintance for somebody needing with getting into ML. I am astounded when individuals disclose to me the course is “excessively fundamental” or “excessively shallow”.

On the off chance that they reveal to me that I request that they clarify the contrast between Logistic Regression and Linear Kernel SVM, PCA versus Matrix Factorization, regularization, or gradient descent. I have talked with hopefuls who asserted years of ML encounter that did not know the response to these inquiries. They are for the most part plainly clarified in Ng’s course.