Machine learning is one of the hottest domains in technology today. Based on LinkedIn's Jobs on the Rise US Report 20211, Machine Learning Researcher, Machine Learning Engineer and AI Specialist are strong in-demand roles in the India market. A machine learning engineer earns $151,028 per year based on data provided by Indeed2. If you look at LinkedIn, there are over 57k active machine learning jobs in the India today.
But what is machine learning?
If I put it simply, machine learning is about harnessing the power of computer science and statistics and applying them to data. You don't need to be a pro programmer or a math wizard to learn machine learning but must have good knowledge about programming, maths, mainly statistics.
How is this related to AI?
Machine learning is a subset of AI. With the application of machine learning, algorithms use a set of data to allow computers to perform tasks they have not been systematically programmed to perform.
If you want to become a machine learning expert, read our brochure that has a practical approach to getting started and thriving in the india as a machine learning beginner!
The Basics You Need to Get Started as a Machine Learning Beginner in the India
1. Understand the Basics of Python
Python is the first choice of professionals and tech companies as it is predefined for machine learning. Whenever end-to-end integration is required to develop analytics-based applications, the use of analytics-friendly Python libraries comes to the rescue. As a machine learner, you should focus on developing an understanding of the fundamentals of the language, libraries and data structures.
2. Have a good understanding of statistics
Your concepts should be clear, especially on the topic of Bayesian probability, as it is important for implementing machine learning algorithms. As a machine learner, and to learn the basics of statistics, it is important to have a thorough understanding of descriptive and inferential statistics. Descriptive statistics are used to describe data from a chart or graph. Estimated statistics allow you to use estimates from that data.
Try these courses for free:
Statistical Methods for Decision Making
Statistics for Machine Learning
statistics for data science
3. Build Your Foundation of Machine Learning
As a beginner to machine learning, you need to understand the concept of machine learning from the ground up. This includes taking a deep dive into Machine Learning and its terminology, algorithms and techniques. Some of the important topics include Supervised Learning, Feature Engineering, Linear Regression with Python, Reinforcement Learning with Python, Model Selection and Tuning etc.
Try these courses for free:
machine learning foundation
linear regression with python
reinforcement learning with python
You can then learn more advanced techniques such as deep learning and ensemble techniques. But as a machine learning beginner, the courses and information given above will be of great help to you.
Start preparing and get certified
As you learn the concepts mentioned above, you will find that there is much more to this area. And to explore this, it is advisable to adopt a structured and guided approach to machine learning.
For this you can do Post Graduate Program in Artificial Intelligence and Machine Learning: Business Applications (PGP-AIML). This online program is ranked among the top AI programs in the India by the McCombs School of Business at the University of Texas at Austin; Delivered in association with Great Learning, one of America's top universities. The program offers you the opportunity to work on practical projects such as personal loan sales campaigns, Street View house number recognition, e-commerce recommendation systems, bank customer segmentation, and more.
A 6-month online program with a structured learning approach, the program features a unique mentored learning model, a comprehensive curriculum covering both Machine Learning and AI, Program Manager Support, 8+ hands-on projects and much more. You will also receive a certificate of completion from the renowned University of Texas at Austin.
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