How do you build a career in Machine Learning after completing the ML Foundation Course?



ML/Machine Learning has a promising future. Chatbots, smartphones and most AI platforms essentially use ML. For example, Alexa from Amazon, Google, Facebook, and almost all large platforms point to a growing industry and an all-time high ML jobs demand. Very obviously the need for professionals in ML, AI, and Deep Learning outstrips the demand.

Programmers, graduates in Computer Applications, and even graduates in mathematics, Social Science or Economics can learn and become ML professionals by doing a certified foundation course in Data Analytics/ Data Science course.

The ML professionals essential skill set include

·         Computer programming and CS Fundamentals.

·         Programming languages like R, Python and some more.

·         ML libraries and algorithms.

·         Statistics and Probability.

·         Software design and systems engineering.

Simple ways to get started with Machine Learning:

A. Read ML books and do a machine learning course with a reputed company like Imarticus which can provide you with reinforcement and certification of your practical skills. Data is the beginning and all about applying your machine learning training, programming knowledge, computer science techniques and statistics to data. R and Python are the most commonly preferred languages. While Python scores in leveraging libraries that are analytics-friendly, practical algorithms, the application development and end-to-end integration using sci-learn and Tensorflow APIs, R is preferred for advanced capabilities in data and statistical inferences analysis.

B. Hone your ML skills with ML Courses which provide ML fundamentals and basic algorithms, statistical pattern recognition and data mining. Your knowledge of statistics should include Bayesian probability, inferential and descriptive statistics for which you will find free courses by Udacity.

C. Applying your learning to building algorithms like perception and control for robotics, building smart robots, anti-spam, and web-search text understanding, medical informatics, computer vision, database mining, and audio based applications.

D. Attend hackathons (Kaggle, TechGig, Hackerearth, etc) which give you support, exposure and mentorship in  ML practical ideas.

E. Build your portfolio with 

  1. A project where you collect the data yourself 
  2. A project where you deal with data cleaning, missing data, etc

F. Master areas that you like to work in like Neural Networks, AI, and ML as applied to image segmentation, speech recognition, object recognition and VR.

The Job Scope:

ML can be the most satisfying choice of careers today which include algorithm development and research used for adaptive systems, building predictive methods for product demand and suggestions, and exploring extractable patterns in Big Data.  Companies recruit for positions like 

  • ML Analyst 
  • ML Engineer 
  • Data Scientist NLP 
  • Lead- Data Sciences 
  • ML Scientist.

Expected payouts:

According to a Gartner report, 2.3 million ML jobs in AI are expected by 2020. Entering the ML field now, according to Digital Vidya, is a great option because the ML payouts for the new entrants vary from Rs 699,807- 891,326. With good expertise in algorithms and data analysis the range of reported salaries could be from Rs 9 lakh to Rs 1.8 crore pa.

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