Skip to main content

5 steps to successfully learn machine learning by yourself

How to learn Machine learning? Learning machine learning is not a big deal but if you want to be an expert in any field you need someone as a mentor. So try to follow some professional machine learning blogs like Shout Future, Kdnuggets, Analytics Vidhya etc., and if you have any doubts, clarify it through forums or directly ask in comments.

Learning machine learning, how to learn machine learning, teach yourself machine learning, machine learning
Machine Learning 

I realised the growth and development of machine learning in future will be incredible, so started to learn Machine learning back in 2013. I started from scratch and I confused a lot. Because I don't know what to do and where to start.
I think you are also like me! Here I mentioned step by step learning process to become a professional machine learning engineer yourself.

1.Getting Started:

  • Find out what is machine learning.
  • Skills to become machine learning engineer.
  • Attend conferences and workshops.
  • Interact with experienced people directly or through social media.

2. Learn Basics of Mathematics and statistics:

3. Choose your tool: Learn R or Python:

Learn R:
Learn Python:

4. Basic and Advanced machine learning tools:


5. Build your Profile:

That's it. With these skills you can enter into the sexiest job in the world now called "Data Scientist". Plan well and follow this steps very well. 
You have to travel very long to become an expert in this field. So start your journey from today onwards and separate yourself from the crowd. 
Please Comment your ideas and opinions. 


Comments

  1. Great, Thank you for sharing with us. Very nice article & have great information.

    ReplyDelete

Post a Comment

Popular posts from this blog

Handy Practical Guide to Machine Learning Algorithms for Beginners

Broadly, there are 3 types of Machine Learning Algorithms.. 1. Supervised LearningHow it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression,Decision Tree, Random Forest, KNN, Logistic Regression etc. 2. Unsupervised LearningHow it works:In this algorithm, we do not have any target or outcome variable to predict / estimate.  It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning: Apriori algorithm, K-means.
3. Reinforcement Learning:How it works:  Using this algorithm, the machine is trained to make specific de…

AI Careers: Skills to Get Artificial Intelligence Jobs

As we can see from the history of artificial intelligence the rate of improvement in this field is just unbelievable. So the job opportunity in artificial intelligence is constantly growing. If you have desired skill sets, you can start your journey in the world of exciting Artificial Intelligence.

Now Artificial Intelligence is playing a crucial part in almost all industries. According to a survey AI market is estimated to grow to $5.05 billion by 2020 at a CAGR of 53.65% percent from 2015 to 2020.
AI is a technology that leads us to a new industrial revolution. Our generation can clearly see the positive impacts of AI in almost all the important fields like Healthcare, Finance, Education, Manufacturing etc.
With the help of AI we are entering into the new world of automation. The future of Artificial Intelligence is giving a confidence to make the world in better place. At the same time, some of the important scientists like Stephen Hawking alarmed about the danger (to Human and for…

A Complete Report On Data Scientist Salary

Executive Summary O’Reilly Data Science Salary Survey, we’ve analyzed input from 983 respondents working in the data space, across a variety of industries— representing 45 countries and 45 US states. Through the results of our 64-question survey, we’ve explored which tools data scientists, analysts, and engineers use, which tasks they engage in, and of course—how much they make. Key findings include: Python and Spark are among the tools that contribute most to salary.Among those who code, the highest earners are the ones who code the most.SQL, Excel, R and Python are the most commonly used tools.Those who attend more meetings, earn more.Women make less than men, for doing the same thing.Country and US state GDP serves as a decent proxy for geographic salary variation (not as a directestimate, but as an additional input for a model).The most salient division between tool and tasks usage is between those who mostly use Excel, SQL, and a small number of closed source tools—and those who …