How to learn Machine learning

With the growing dependence on IT, every good company is using artificial intelligence and machine learning to make the user experience better just like YouTube is using it to give you suggestions about the videos, Google for making your search list better and many others.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Best Programming Languages for Machine Learning:

It is very important to know the programming languages used in Machine Learning.It is a little different from other programming concepts and algorithms, because of the complex mathematical and graphical algorithms it includes.
So, let’s see which programming language can be best suited:

Java / C++:

Java and C++ are two versatile languages for programming. that can be used for machine learning but the problem with these languages is that they make implementing machine learning very tough because of very less inbuilt functions and classes to implement the mathematical functions. You will have to write code for all the

R Language:

R is a rising language in the field of Big Data and Data analysis. It can be a good platform to implement machine learning if most of the algorithms we are implementing are based upon data science and graphs.

MATLAB/ Octave:

Octave is a language that provides most of the inbuilt classes and functions that makes implementing machine learning a very easy task as compared to Java and C++. It is suitable for the people who are implementing ML for the first time and those who are doing a deeper study on it.
REFER TO: Andrew Ng’s Coursera Machine Learning course.


Python is a language that you can use for almost everything including Machine learning. With its easy syntax, it provides thousands of inbuilt functions that help implement the mathematical functions and graphs easily.
Also, its communication tools are very attractive and it is easy to be connected to connect it to other platforms too.

Resources to Learn Machine learning

Now” is better than ever before to start studying machine learning and artificial intelligence. The field has evolved rapidly and grown tremendously in recent years. Experts have released and polished high quality open source software tools and libraries. New online courses and blog posts emerge every day. Machine learning has driven billions of dollars in revenue across industries, enabling unparalleled resources and enormous job opportunities.

1.Kaggle Competitions and Kaggle Datasets provide a good starting point for both well-defined machine learning problems and raw data sources that’d be suitable for machine learning.

Ben Hamner, Kaggle co-founder and CTO, held a Quora Session answering questions on the future of Kaggle, machine learning and AI, and data science workflows .these are highlight his advice for studying machine learning in eight steps.

  1. Pick a problem you’re interested in
  2. Make a quick, dirty, hacky, end-to-end solution to your problem
  3. Evolve and improve your initial solution
  4. Write up and share your solution
  5. Repeat #1-4 across a diverse set of problems
  6. Seriously compete in a Kaggle competition (if you’ve not already done so)
  7. Apply machine learning professionally
  8. Help teach others about machine learning.You can read all these steps in detail in the Kaggle

2.Machine learning podcasts – Listening to podcasts is one good way to learn a new topic.I came across many podcasts like dataskeptic to learn ML

3.The top 10 algorithms used in data mining . This paper presents the top 10 data mining algorithms – C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

4.Here is the webinars on ML conducted by CareerHunt.

5.Excellent Quora answer explaining ML

Read from more blogs in CareerHunt ML curated resources

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