How To Become A Machine Learning Engineer — Learning Path

1. Introduction to Machine Learning -

Machine Learning is a subset of artificial intelligence that focuses mainly on machine learning from their experience without being explicitly programmed and making predictions based on their experience. So instead of you writing the code, what you do is you feed data to the generic algorithm, and the machine builds the logic based on the given data.

Types of Machine Learning are

• Supervised Learning — train me
• Unsupervised Learning — I am self-sufficient in learning
• Reinforcement Learning — hit and trial

2. Purpose of Machine Learning -

Machine learning is needed for tasks that are too complex for humans to code directly. Some tasks are so complex that it is impractical, if not impossible, for humans to work out all of the nuances and code for them explicitly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.

3. Is Machine learning hard to learn or is it difficult?

Machine Learning is in itself huge learning. The term machine learning is self-explanatory. Machines learn to perform tasks that aren’t specifically programmed to do. Machine Learning being a vast field, knowing just python is not well enough. There are lot many other things you should know to be a machine learning engineer. Continuous effort and hard work will make you better in this field.

4. Who can learn Machine Learning -

There is an increasing demand for skilled machine learning engineers across all industries. Machine Learning course for the following professionals in particular -

• People who have an interest in learning
• Developers aspiring to be a data scientist or machine learning engineer
• Analytics managers who are leading a team of analysts
• Business analysts who want to understand data science techniques
• Information architects who want to gain expertise in machine learning algorithms
• Analytics professionals who want to work in machine learning or artificial intelligence
• Graduates looking to build a career in data science and machine learning

• Experienced professionals who would like to harness machine learning in their fields to get more insights

5.Prerequisites to start learning Machine learning

Machine Learning Crash Course does not presume or require any prior knowledge in machine learning. However, to understand the concepts presented and complete the exercises, we recommend that students meet the following prerequisites -

• Linear Algebra
• Trigonometry
• Statistics
• Calculus
• Probability
• Graph Theory
• Differential Equations
• Python (or R)

6. How to start with Machine Learning

Getting started in machine learning is broken down into a 5-step process -

• Adjust Mindset — Believe you can practice and apply machine learning.
• Pick a Process — Use a systemic process to work through problems.
• Pick a Tool — Select a tool for your level and map it onto your process.
• Practice on Datasets — Select datasets to work on and practice the process.
• Build a Portfolio — Gather results and demonstrate your skills.

7. Websites and Blogs to learn Machine Learning for free

With the increased usage of Machine Learning applications in various sectors, it has become of utmost importance for a learner to be familiar with the various concepts involved in ML algorithms and ML models.

8. Ways You Can Succeed in A Machine Learning Career

• Understand what machine learning is — Having experience and understanding of what machine learning is, understanding the basic maths behind it, understanding the alternative technology.
• Be curious — Machine learning is a modern thing that will only continue to evolve in the future, so having a healthy sense of curiosity and love of learning is essential to keep learning new technologies and what goes with them.
• Learn Python and how to use machine learning libraries — Programming exercises in Machine Learning Crash Course are coded in Python using TensorFlow. No prior experience with TensorFlow is required, but you should feel comfortable reading and writing Python code that contains basic programming constructs, such as function definitions/invocations, lists and dicts, loops, and conditional expressions.
• Be a team player — Today, when you are working in machine learning, you are most likely working as part of a team, and this team would comprise people who have direct interaction with the business. So it means if you want to be successful as a machine learning practitioner today, you must be ready and able to interact with the business and be a team player.

• Gain knowledge of the industry you want to work in — Machine learning, much like any data-driven job, doesn’t exist in a vacuum. Every industry and company has unique goals and needs. That being the case, the more you can learn about your desired industry, the better off you’ll be.

9. Career choices in Machine Learning

The jobs available are more specific -

• Machine Learning Researcher
• AI Engineer
• Data mining & analysis
• Machine Learning Engineer
• Data Scientist
• BI(Business Intelligence) Developer

10. Future of ML

Google says “Machine Learning is the future,” and the future of Machine Learning is going to be very bright. As humans become more addicted to machines, we’re witnesses to a new revolution that’s taking over the world, and that is going to be the future of Machine Learning.

With the innovation we will see in the coming years, we can’t even imagine what will develop, but we do know we already have a shortage of trained AI and machine learning professionals, and that gap will only grow until we get people trained and placed in the millions of AI job.