Do you want to know How to Become a Machine Learning Engineer?… If yes, this article is for you. In this article, you will get a step-by-step complete roadmap to becoming a Machine Learning Engineer. Along with that, you will also find some best resources to learn Machine Learning Concepts.
Now, without any further ado, let’s get started-
How to Become a Machine Learning Engineer?
- Learning TimeFrame for Machine Learning Engineer
- What is Machine Learning Engineer?
- What does Machine Learning Engineer do?
- Roles and Responsibilities of Machine Learning Engineer
- Steps to Become a Machine Learning Engineer
- My Suggestion-
- How long Does it take to Become a Machine Learning Engineer?
- Machine Learning Engineer Salary
- How to Become a Machine Learning Engineer without a Degree?
- How to Become a Machine Learning Engineer at Google?
- What Qualifications do I need to be a Machine Learning Engineer?
- Is ML a Good Career?
Learning TimeFrame for Machine Learning Engineer
|– Learn Python basics.
|– Simple data analysis with Python.
|– Refresh math skills (algebra, probability).
|– Create basic programs to solve math problems.
|– Use Python libraries (NumPy, Pandas) for data.
|– Explore and visualize a dataset of interest.
|– Understand basic machine learning concepts.
|– Implement a simple machine learning model (e.g., linear regression).
|– Explore more complex machine learning ideas.
|– Work on a classification project (e.g., predicting customer churn).
|– Work with real-world data on small projects.
|– Build a recommendation system for movies or products.
|– Learn about deep learning.
|– Implement a basic neural network for image classification.
|– Try building simple neural networks.
|– Develop a project using a pre-trained deep learning model (transfer learning).
|– Choose a focus area (e.g., computer vision).
|– Create a computer vision project (e.g., object detection).
|– Work on projects within your chosen area.
|– Build a natural language processing (NLP) application, like sentiment analysis.
|– Dive into advanced topics and stay updated.
|– Contribute to an open-source machine learning project.
|– Build a network by connecting with professionals.
|– Apply for jobs or internships, and be ready for interviews.
|– Apply for jobs or internships, be ready for interviews.
|– Collaborate on a team project or participate in a Kaggle competition.
|– Keep learning, contribute to projects, and enjoy the journey.
|– Work on a real-world problem that aligns with your interests and expertise.
Before starting the detailed learning process, I would like to discuss- Who is Machine Learning Engineer? What does Machine Learning Engineer do? and Roles and Responsibilities of Machine Learning Engineer.
What is Machine Learning Engineer?
A Machine Learning Engineer is a programmer who builds machines and systems that can learn and react similarly to human beings. The goal of a Machine Learning Engineer is to achieve Artificial Intelligence.
You will understand more about Machine Learning Engineer in the next section, “What does Machine Learning Engineer do?
What does Machine Learning Engineer do?
Machine Learning work with the following steps-
- Data Collection.
- Data Preprocessing.
- Choose a Machine Learning Algorithm.
- Training the Model.
- Testing the Model.
- Tuning the Model.
So, as a machine learning engineer, you have to perform all these steps.
Machine Learning Engineers create a Machine Learning model that can work properly with the best performance. Machine Learning Engineers have to choose the right algorithms as per model compatibility and requirement.
They have to extract ideas from the data science team, choose appropriate tools and ecosystems, Use machine Learning frameworks, and stay up to date with the latest development.
Now, let’s see the Roles and Responsibilities of Machine Learning Engineers-
Roles and Responsibilities of Machine Learning Engineer
- Study and convert Data Science Prototypes.
- Build Machine Learning models.
- Research and apply appropriate Machine Learning tools and algorithms.
- Build a Machine Learning application based on industry requirements.
- Choose correct datasets and data visualization methods.
- Conduct Machine Learning tests and experiments.
- Execute Statistical Analysis and fine-tuning with the help of test results. (Statistical Analysis is a small part of ML Engineers whereas it’s a major job part of Data Analyst).
- Train and Retrain the model based on model accuracy.
- Stay updated with the latest development in the field.
So, these are the Roles and responsibilities of the Machine Learning Engineer. Now, you have a better understanding of the Machine Learning Engineer.
Now, let’s see how you can become a Machine Learning Engineer. For your convenience, I have divided the learning process into different steps.
If you follow these steps, you can easily achieve your goal to become a Machine Learning Engineer. So, let’s get started-
Steps to Become a Machine Learning Engineer
Step 1- Learn Programming Language
To build a machine learning model, you should be familiar with programming languages. If you are a beginner in programming, then start with Python.
But if you already have Python knowledge, then you are one step closer to Machine Learning.
I started my Machine Learning journey with Python.
Because Machine Learning is all about implementation. And if you don’t have programming knowledge, you can’t implement anything.
At this step, only learn Python Basics, so that you can code in Python. As a beginner in python, you can refer to any Free Python Tutorial available online. I have already written an article for Best Free+Paid Resources to learn Python Online. You can refer to this article.
For your convenience, I am again mentioning some best resources to learn Python-
Resources for Learning Python-
- The Python Tutorial (PYTHON.ORG)– This Python Tutorial is official Python documentation. This tutorial will provide you so many Python basic concepts.
- Python Tutorial- MLTUT– This is a small contribution from my side. I tried to make this tutorial easy to understand. This is not a very advanced level tutorial, but yes, you will get a basic knowledge of Python.
- Python for Everybody (Coursera)– This specialization program will teach you fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language.
- Python for Absolute Beginners! (Udemy)– It’s a good free online course with 2.5 hours of content.
- Python 3 Tutorial (SOLOLEARN)– This is another great tutorial to learn Python. This tutorial will clear your basic concepts of Python.
Step 2- Brush-Up Your Math skills
Knowledge of mathematics is essential to understand, how machine learning and its algorithms work. You should know the basics of these math topics for machine learning-
- Probability and Statistics
- Linear Algebra
Knowledge of statistics will give you the ability to decide which algorithm is good for a certain problem.
Statistics knowledge includes statistical tests, distributions, and maximum likelihood estimators. All are essential in machine learning.
Knowledge of Statistics helps you to count well, normalize well, obtain distributions, find out the mean of your input feature, and its standard deviation.
Mathematics helps you to identify under-fitting and over-fitting by understanding the Bias-Variance tradeoff.
I have written an article on Best Math Courses for Machine Learning. You can check if you want some more interesting courses in Math.
Resources for Learning Math for Machine Learning-
- Mathematics for Machine Learning Specialization by Imperial College London– This is one of the best specialization programs that covers all mathematical topics required for Machine Learning. The aim of this specialization program is to fill the gap and build an intuitive understanding of mathematics.
- Mathematics for Data Science Specialization by National Research University Higher School of Economics– This is another mathematics specialization program, that covers all required math topics for Machine Learning and Data Science. In this specialization, you will learn Discrete Mathematics, Calculus, Linear Algebra, and Probability.
- Probability and Statistics by the University of London-This course is especially dedicated to Probability and Statistics. In this course, you will learn many useful tools to deal with uncertainty.
Step 3- Learn Machine Learning Concepts
Now, you have gained Python and Math skills. It’s time to learn Machine Learning concepts. In this step, you need to learn the basics of Machine Learning like- Types of Machine Learning algorithms( Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning), then the detail of each Machine Learning algorithm, and other concepts.
At this step, you can enroll yourself in any Machine Learning Online Course. I personally loved Andrew Ng Machine Learning Course. This course is beginner-friendly and gives you a strong knowledge of Machine Learning.
I have collected some best online courses and summarized them in an article. You can refer to this article for more ML Courses- Best Online Courses On Machine Learning You Must Know.
But if you are in hurry, then this is the summary of some best Machine Learning Courses-
Resources for Learning Machine Learning-
- Machine Learning (Coursera) by Andrew Ng– This Course provides you a broad introduction to machine learning, data-mining, and statistical pattern recognition.
- Intro to Machine Learning with TensorFlow (Udacity)– The best part of this program is that at each step, you will get practical experience by applying your skills to code exercises and projects.
- Machine Learning with Python by IBM– This course starts with the basics of Machine Learning. Python is used in this course to implement Machine Learning algorithms.
- Get started with Machine Learning (Codecademy)– This is another Beginner-friendly course for Machine Learning from Codecademy.
Step 4- Learn Data Science Tools
After gaining Python and Machine Learning knowledge, it’s time to practice. And for that, you need to use Data Science tools like Jupyter and Anaconda. Spend your few hours and play with these tools. Understand what they’re for and why you should use them.
For installing and getting the basics of these tools, you can use these tutorials-
Resources for Learning Jupyter and Anaconda–
- Anaconda Tutorial by Corey Schafer– This YouTube Tutorial will help you to install Anaconda.
- Jupyter Notebook for Beginners Tutorial by Dataquest– This is a complete tutorial that will guide you to install Jupyter Notebook and provide basics.
Step 5- Familiar with pandas, NumPy, and Matplotlib.
Now, it’s time to know how to deal with data. Why? because in order to build a machine learning model, the first requirement is data. And for that, you need to have knowledge of data manipulation, analysis, and visualization.
pandas is an open-source data analysis and manipulation tool. With the help of pandas, you can work with data frames. Dataframes are nothing but similar to Excel files.
NumPy will help you to perform numerical operations on data. With the help of NumPy, you can convert any kind of data into numbers. Sometimes data is not in a numeric form, so we need to use NumPy to convert data into numbers.
Matplotlib allows us to draw a graph and charts of our findings. Sometimes it’s difficult to understand the result in tabular form. That’s why converting the results into a graph is important. And for that, Matplotlib will help us.
Resources for Learning pandas, NumPy, and Matplotlib–
- Applied Data Science with Python Specialization by the University of Michigan– You will get a strong introduction to data science Python libraries, like matplotlib, pandas, nltk, scikit-learn, and networkx.
- Exploratory Data Analysis With Python and Pandas (Guided Project)- In this 2-hour long project-based course, you will use external Python packages such as Pandas, Numpy, Matplotlib, Seaborn, etc. to conduct univariate analysis, bivariate analysis, correlation analysis and identify and handle duplicate/missing data.
- NumPy Tutorial by freeCodeCamp– NumPy tutorial in YouTube video.
Step 6- Start Practicing with Real- World Projects
Now, you have enough skills to build your first Machine Learning Model. It’s time to work on some machine learning projects. Projects are essential to get a job as a Machine Learning Engineer.
The more projects you will do, the more in-depth your understanding of ML you will grasp. Projects will also provide more privilege to your Resume.
If you are not clear where to start with Machine Learning Projects, you can go to Kaggle and choose any dataset. Once you have a dataset, you will get an idea of what you can do with it. You can choose projects in DataCamp.
Once you do some projects, look up common problems on the internet and solve them by acquiring relevant datasets.
Step 7- Gain Deep Learning Skills
Now, you have gained enough Machine Learning skills, but knowledge of deep learning is also important.
Because Machine Learning works perfectly fine with small datasets. But, when you have large datasets, then Machine learning Algorithms fail. So for that Deep Learning is used. Deep Learning gives perfect results for large datasets.
One more advantage of deep learning is- In Machine Learning, you need to feed all features manually to train the model. But Deep Learning automatically extracts all the features. This makes Deep learning much powerful than Machine Learning. Because manual feeding is a time-consuming process, especially if you have a large dataset.
Resources for Learning Deep Learning-
- Deep Learning Specialization (deeplearning.ai)– This course is taught by Andrew Ng. This Deep Learning Specialization is an advanced course series for those who want to learn Deep Learning and Neural Network. You will get a chance to work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing.
- Advanced Machine Learning Specialization by National Research University Higher School of Economics– This specialization program will also teach you deep learning and neural network. You will get a chance to work on a wide variety of real-world problems like image captioning and automatic game playing.
Step 8- Make a Strong Resume
Finally, you are ready to apply for a Machine Learning Engineer Job. Now, it’s time to make a strong Resume. Your Resume is the first impression for any recruiters. No matter how skilled you are, but if your resume is not attractive, sorry you will not get an interview call. That’s why you shouldn’t ignore your Resume.
If you want that your resume will get more privilege than others, then you should keep these things in mind-
- Read the job profile and check what skills they require, then see how many skills you have. Suppose in the job description they mentioned Good Mathematics Skills, and you have Good Mathematics Skills, then definitely write “Good Mathematics Skills” as the first skill. You can repeat the same for other skills too, just compare your skills and the skills written in Job Description. This tip will definitely help you.
- The template of your resume should be classic.
- Avoid templates with so many graphics. It gives a bad impression to the recruiter.
- Don’t hesitate about white spaces. That means don’t try to fill the full page with text. Leave some white space that looks clean.
- Don’t write a long text like a story. It should be precise and simple.
- Mention only the most important Machine Learning Projects. Don’t mention very basic projects.
- After finalizing your resume, you need to check for grammar and spelling mistakes. Because of any grammar or spelling mistakes, your full work will be wasted. So thoroughly check for grammar and spelling before sending it to the company. You can check it on Grammarly.
That’s all!. If you follow these steps and gain these required skills, then no one can stop you to land in Machine Learning Field.
The most important thing is to keep enhancing your skills by working on more and more challenges.
The more you practice, the more knowledge of machine learning you will gain. So after completing these steps, don’t stop, find new challenges and solve them. These projects and challenges will make your portfolio more impressive than others.
Now, it’s time to clear your doubts by answering a few important questions you might have.
How long Does it take to Become a Machine Learning Engineer?
Approximately it will take around 24 months to follow all the steps if you spend at least 5-6 hours of study per day. But the time limit is totally up to you. The more time you invest in learning, the less time you will take to learn the concepts.
Note- Getting a Machine Learning Engineer Job is not included in this time limit. This is the estimated time for learning the concepts and working on projects.
Machine Learning Engineer Salary
According to Indeed, the average salary of a Machine Learning Engineer is- $ 142,858.67
But, this is not the salary of Entry-Level or Mid-level ML Engineers. So, let’s see How much Entry-level and Mid-Level ML Engineers earn?
Entry-Level Machine Learning Engineers salary
Entry-level ML Engineers means someone with 0-4 years of experience, and who is a college pass-out, or someone who switch their job and landed in Machine Learning Field.
So, the average pay of an Entry-Level ML Engineer is $97,090 but after adding bonus and profit-sharing, the pay can become $130,000 or more.
Now, let’s see the average salary of a Mid-level ML Engineer-
Mid-Level Machine Learning Engineers salary
A mid-level Machine Learning Engineer means someone with 5-9 years of experience. So, according to PayScale, the average salary of a mid-level machine learning engineer is $112,095.
I hope, now you have a clear idea about Machine Learning Engineer salaries.
How to Become a Machine Learning Engineer without a Degree?
To get a job as a Machine Learning Engineer without having a degree, you need to follow the following steps-
- Enroll in any Machine Learning Course to gain the required skills.
- Show that you have a lot of experience in machine learning. You can do this by participating in machine learning competitions or you can build your own machine learning projects. You can also contribute to open source projects.
- Attend hackathons. In the programming community, hackathons have actually begun to surpass job fairs in terms of showcasing yourself as a credible job prospect.
- Start going to networking events where people meet up to talk about machine learning and aspects of data science.
- Try to get an internal referral. You can get referrals by going to networking events, going to hackathons, contributing to open source projects, connecting with people on LinkedIn.
- Apply for the Machine Learning Engineer Jobs Online. You can directly Email to recruiters.
So, these are the few steps you need to follow if you want to get a job as a Machine Learning Engineer without a degree.
How to Become a Machine Learning Engineer at Google?
Google preferred qualification for the Machine Learning Engineer role is a Master’s degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or related technical field. Along with that, you must have 2 years of relevant work experience in machine learning software development and architectures for machine learning (with a focus on deep learning).
You should also have experience in building, deploying, and improving Machine Learning models and algorithms in real-world products.
So, if you meet this requirement, you can directly apply for the role.
What Qualifications do I need to be a Machine Learning Engineer?
Most Machine Learning Engineer jobs require a Master’s or Ph.D. in Computer Science, software engineering, and other related fields. Machine Learning Engineer is not a Graduate level job.
It requires years of experience in data science and software engineering, as well as an advanced college degree.
Becoming a Machine Learning Engineer is easy If you are a Software Engineer and want to switch your job or you have completed your Master’s or Ph.D. in a related field.
Is ML a Good Career?
According to the Forbes Article–
- AI and Machine Learning job postings on Indeed rose 29.10% in between May 2018 and May 2019.
- The Indeed analytics team found that the average annual salary for Machine Learning Engineers has grown by $8,409 in just a year, increasing 5.8%. Algorithm engineer’s average annual salary rose to $109,313 this year, an increase of $5,201, or 5%.
So, according to this data, Machine Learning Engineer is definitely a profitable, secure, and most demanding career.
I hope I have covered all the essential details and you got an answer to your question, “How to Become a Machine Learning Engineer?”. If you have any doubts or queries feel free to ask me in the comment section. I am here to help you.
All the Best for your Career!
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Though of the Day…
‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.– Henry Ford