Machine Learning is one of the hottest domains in tech today. Based on LinkedIn’s Jobs on the Rise US report of 20211, Machine Learning Researcher, Machine Learning Engineer, and AI specialist are roles with strong demand in the US market. A machine learning engineer makes $151,028 per year based on the data provided by Indeed2. If you look at LinkedIn, there are more than 57k active machine learning jobs in the US today.
But what is machine learning all about?
If I’ve to put it simply, machine learning is all about using the power of computer science and statistics and applying them to data. You need not be a pro programmer or a math wizard to learn machine learning but should definitely have good knowledge about programming, math, mainly statistics.
How is it related to AI?
Machine learning is a subset of AI. With the application of machine learning, algorithms make use of a set of data to enable computers to do tasks they are not organically programmed to do.
If you want to become a machine learning expert, read our brochure that contains a pragmatic approach for you to commence as a machine learning beginner in the US and thrive in it!
Basics Required to Start as a Machine Learning Beginner in the US
1. Understand the basics of Python
Python is the first choice of professionals and tech companies as it is predisposed to machine learning. Whenever an end-to-end integration is needed, analytics-based applications have to be developed, the use of analytics-friendly Python libraries comes to the rescue. As a machine learning beginner, you should focus on developing an understanding of the basics, libraries, and data structure of the language.
Try this course for free: Python for Machine Learning
2. Have a sound understanding of statistics
Your concepts should be clear especially on the topic of Bayesian probability, as it is important for executing machine learning algorithms. As a machine learning beginner, 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 a graph. Inferential statistics allow you to use conclusions from that data.
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3. Build your foundations of machine learning
As a machine learning beginner, you need to understand the concept of machine learning from the grassroots level. This involves diving deep into machine learning and its terminologies, algorithms, and techniques. Some of the important topics include Supervised Learning, Unsupervised Learning, Feature Engineering, Linear Regression with Python, Reinforcement Learning with Python, Model Selection and Tuning, etc.
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You can then learn more advanced techniques like deep learning and ensemble techniques. But as a machine learning beginner, the courses and information provided above will be of great use to you.
Start Preparing and Get Certified
As you’ll learn the concepts mentioned above, you’ll realize that there is a lot more to this domain. And to explore the same, it is advisable to take a structured and guided approach to learn machine learning.
For this, you can pursue The Post Graduate Program in Artificial Intelligence & Machine learning: Business Applications (PGP-AIML). Ranked among the top AI programs in the US, this online program by the McCombs School of Business at The University of Texas at Austin; one of the top universities in the US, delivered in collaboration with Great Learning. The program offers you the chance to work on hands-on projects like A campaign to sell personal loans, Identify Street View House Numbers, e-Commerce Recommendation System, Bank Customer Segmentation, and a lot more.
A 6-month online program with a structured learning approach, this program has a unique mentored learning model, a comprehensive curriculum covering both Machine Learning and AI, program manager support, 8+ hands-on projects, and a lot more. You’ll also get a certificate of completion from the renowned University of Texas at Austin.
Get started with your journey of growing from a machine learning beginner in the US to becoming an expert with a thriving career in this field.