Machine Learning Career Path [2023] Step-by-Step Guide

Machine Learning today is one of the most sought-after career options in the market. A lot of Software Engineers are picking up ML, simply because it is a highly paid skill and it’s a future tech.

Do you want to follow Machine Learning Career Path and therefore you want to know how to make your career in Machine Learning?

You have come to the right place. After reading this article, you have a clear view of the Machine Learning Career Path. You will find some useful information regarding Machine Learning which you should know to make a career in machine learning.

After reading this article I assure you, you will get to know where and how to start.

So let’s start our journey with the most common question.

What is Machine Learning?

Machine learning is the domain of Computer Science that gives the ability to electronics machines to learn without being programmed.

I want to clarify the meaning of “Not Being Programmed”. The electronic machine is not being programmed to do the task, it learns from scratch on its own. This type of electronics machine called to be as Artificial Intelligence.

In my article Artificial Intelligence Career Path (AI) Step-by-Step Guide, I have explained how to break into AI and become an AI Engineer. Must read this article.

In other words, Machine Learning provides data to Artificial Intelligence so it can learn things.

For example, we humans learn from our past experiences; likewise, machines are also able to learn from experience. But for the machines data work as an experience.

Types of Machine Learning

Machine Learning is typically divided into three categories. A machine can learn various kinds of new things in three ways: (1) Supervised Learning (2) Unsupervised Learning (3) Reinforcement Learning. Let’s understand each of the learning methods one by one.

1. Supervised Learning

You have to train your machine with every possible input with the corresponding output. The training process continues until the machine achieves the desired level of accuracy on the output.

After a sufficient amount of training with data, the machine can generate an output according to any new input provided. This method of learning is called supervised machine learning.

An example of Supervised Learning is student-teacher relations. In other words, the teacher in the classroom teaches students every possible solution to the questions.

2. Unsupervised Learning

In this learning process, we give only some sets of inputs to the machine.

After some sort of time machine is able to generate its own logic and structure between different inputs to solve the problems. That is called unsupervised machine learning.

An example of Unsupervised Learning is a friend-to-friend relationship. Suppose one of your friends gives you lots of different solutions to solve a problem. But you can’t apply all the given solutions, it’s up to you which advice you will apply to solve your problem.

3. Reinforcement Learning

In this learning method, you only have to provide the task to the machine.

Machine tries to solve a given task on its own based on previous experience. After achieving the task output, we give feedback to the machine that how good the decision was.

Machine stores that feedback on its memory, whether it is good or bad. The process of learning through rewards and recognition is known as reinforcement machine learning.

The best Example of Reinforcement Learning is the parent’s child relation. Whenever a kid faces a new problem, the kid tries to make a decision on his own using past experiences. As parents, we appreciate the kid and tell him about whether the decision was right or wrong.

Education Required to Become a Machine Learning Engineer

  • Education 1 (Mandatory): You must have to complete your high school diploma with subjects- Maths and Computer Science
  • Education 2 (Mandatory): Bachelor’s Degree in Computer Science or Mathematics or any engineering stream.
  • Education 3 (Optional): Masters + PhD

Like most machine learning aspirants, you can join master’s and Ph.D. programs in Data Analysis related subjects.

However, a master’s or Ph.D. is not necessary to get into machine learning. But candidates having master’s/Ph.D. get more career opportunities as compared to candidates having an undergraduate degree.

Things You Should Learn Before Machine Learning

Most of the time people generally ask this question; where to start and how to start to break into machine learning.

If you want to make your career in machine learning then these are some prerequisites you must fulfill and things you must know. You can think of it as a building block of ML.

1. The first prerequisite to learning ML is to have a good understanding of maths. We need maths to understand machine learning algorithms. You don’t need all the maths but only some specific topics.

  • Linear algebra
  • Probability theory
  • Optimization
  • Calculus
  • Statistics

2. I think you have guessed the second requirement of learning ML. Yes, you are right, you also need some programming background to begin. Or, it might not be wrong to say that you need to learn Python before learning ML as it has a large number of libraries and is easy to learn. And many more reasons to choose, which you will get to know further in this article.

You don’t need to be a professional mathematician or rockstar programmer to learn machine learning, you just need to know some basic kinds of stuff.

Once you fulfill the above two mentioned prerequisites, the rest will be fairly easy.

Top 5 Programming Languages for Machine Learning

There are many programming languages in the market which are used in machine learning and to learn them all is quite tedious. One of the easiest ways to pick the best programming language to learn is by listening to what the market says. So here we have named only the five most popular and demanding programming languages for machine learning.

  1. Python
  2. R
  3. C++
  4. JavaScript
  5. Java

In the above-mentioned list, you can pick any of the languages based on your interest.

R is the second most used language for machine learning and AI followed by Python. However, the gap between numbers 1 and 2 is quite big.

Most beginners choose to go with Python. And why not, it is one of the easiest to learn and has easy syntax relatively.

Is Programming Language Necessary to Learn Machine Learning?

In short, it is not necessary to learn Machine Learning. In fact, you can learn most of the machine learning concepts without knowing a single line of code.

But to implement those concepts you need to learn at least one programming language. And start with Python is the best option, especially for beginners.

What’s the meaning of learning if you can’t implement it.

Tools Used for Machine Learning

These are the following tools a professional uses for machine learning

Also Check: What is Cloud Computing & Why You Should Learn?  

Machine Learning Career Path [Step-by-Step] Guide to Start Your Career in ML

After knowing so many things about Machine Learning through this article. Now we will get to know the step-by-step procedure to break into Machine Learning.

[Step 1] Good Understanding of Maths

First and the most important thing to do is to work on your basics maths. You must have a clear understanding of basic calculus, probability, statistics, and linear algebra.

If you understand these topics very well, you’ll have enough background to understand machine learning algorithms and concepts.

[Step 2] Programming Language (Python)

You are just one step away from start learning Machine Learning. Pick any programming language of your choice. Or I would say pick Python.

Python is being the most used language for machine learning. Because of its larger community, if you get stuck in any problem then the chances of getting the solution are more.

[Step 3] ML Algorithms

Now it’s time to learn machine learning algorithms. They instruct a machine on what to do next in a structured manner. It is essential to learn how does machine learning algorithm works. These are the following algorithms you must know and understand.

  • Linear Regression
  • Logistic Regression
  • Support vector machine (SVM)
  • Random Forest
  • Dimensionality Reduction Algorithms
  • K-Nearest Neighbors (KNN)
  • Decision Trees
  • K-Means
  • Gradient Descent
  • Naive Bayes

[Step 4] Exploratory Data analysis (EDA) / Data Preprocessing

You have to spend most of your time at this stage. In this step, you have to understand data preprocessing. Data Preprocessing is a technique of transforming raw data into an understandable data format. It is necessary to pre-process the data before providing data sets to the machine learning algorithm. And you must know how to pre-process the data before feeding it to the machine.

You can use some powerful and advanced libraries like Numpy, Pandas, and Scipy for data analysis. Matplotlib and Seaborn for visualization of data.

[Step 5] Machine Learning Projects

All right! Now you know a lot about Machine Learning. It’s time to build some real-world projects, it will take your learning to the next level. This is the best way to apply and test your understanding of ML.

This is really a fun part to implement your concepts and knowledge. These projects will help you to understand how machine learning is used in the real world.

Where to find ML Projects? To find the projects to get some hands-on experience you don’t have to struggle hard, simply type “Machine Learning Projects” on the Google search box and you will have plenty of projects to work on.

So these were the 5 steps you have to take to get started in your career in Machine Learning.

Also Check: 5 Best Online Courses to Learn Artificial Intelligence

What are Cheat Sheets & Why it is Used in ML

The cheat sheet is a supportive guide for Machine Learning Engineers because it is not possible to carry all the heavy and thick books all the time.

The cheat sheet is in soft documentation format and contains very few pages. It is easy to find the solution in a cheat sheet as compared to books.

As we know Machine Learning/ Data Science is a very big field and still growing. It is obvious, that we cannot remember all the algorithms, tools, formulas, and functions. That is the reason, we need a cheat sheet.

Download machine learning algorithms cheat sheet

Machine Learning Career Paths

Machine Learning has emerged as the hottest technology and one of the fastest-growing domains in today’s time.

Since you are learning or thinking about making your career in Machine Learning let me tell you there are many career paths in ML that are most in-demand and as well as the highest paying in the industry.

There is an unlimited scope in the machine learning career path. These are some possible job profiles you will get after learning ML and throughout your career.

  1. Machine Learning Engineer
  2. Data Engineer/ Scientist
  3. AI Engineer
  4. Data and Analytics Manager
  5. NLP Engineer
  6. Business Intelligence Developer
  7. Research Scientist
  8. Human-Centered Machine Learning Designer
  9. Big Data Engineer
  10. NLP Scientist

Machine Learning Engineer Job Profile

In an organization, an ML Engineer is responsible to complete many tasks. They have multiple of responsibilities to make the project work. Here I have shared some of the few very common responsibilities that you would also be responsible for.

  • Explore & extract insights from a massive range of structured and unstructured data.
  • Apply data mining and machine learning to improve content understanding.
  • Develop and improve the accuracy of machine learning algorithms.
  • Analyze source data and data flows.
  • Design and implementation of machine learning models.
  • Explore data assets available and identify the right data sets.
  • Quality assurance and testing of analytical routines and data frameworks.

I hope this article has been able to add some value to your knowledge. And provided you with proper guidance about the machine learning career path. If you still have any doubt related to this career option you can ask in the comment section below.

Let me know what you think about Machine Learning(ML). Share your opinions and experiences in the comments below!

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