From Novice to Pro: A Roadmap for Your Machine Learning Career

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The rate at which machine learning (ML) is transforming industries has significantly increased in the recent past. It redefines the way we solve business problems and provides the delight that every customer looks for. Some of the top reasons why learning algorithms have become popular are scalability and learning from historical events using data captured through various systems and processes within an organization.

It is this data that algorithms like decision trees, support vector machines, and neural networks process and learn patterns from to make predictions.

Our goal is not to generate just predictions, but good quality and actionable ones. For this, you need to choose the right evaluation metrics to assess the goodness of your model. From here starts the journey of continuously monitoring and maintaining model outcomes by iterating based on real-world feedback.

Let’s take a look at a concise roadmap to building a lasting and effective machine learning career.

Focus on Foundations

 
With this overview, we are ready to start with the skills required to build good quality and actionable ML models. At the core of ML are mathematical concepts such as linear algebra, statistics, probability, and calculus.

In parallel, focus on learning a programming language, a popular one being Python which offers powerful libraries, such as TensorFlow, PyTorch, and Scikit-learn.

You’d also need to learn SQL to query and manage data from databases.

Reaching these goals, you have both of the baseline skills: a knack for data and programming language to work on it.

Data preparation is an extensive process — you need to learn how to handle missing, outlier, inconsistent, or for that matter incorrect data. Learn how to transform data to make it suitable for analysis and for models to learn from. Spend generous time exploring data patterns and gaining insights about the problem you are trying to solve.

Going Beyond Novice

 
Now is the time to start mastering Python and learning about core ML algorithms, asking algorithm-specific questions such as:

• How does Linear Regression work?
• What are its assumptions?
• In which scenarios, does it work well vs. where it is not a right fit?
• How does it differ from Logistic Regression?

These questions will help you to differentiate between regression and classification problems. You are on the right track, so keep expanding upon the foundations by learning about decision trees, random forests, logistic regression, and more. Practice implementing these algorithms on datasets from sources like Kaggle, UCI Machine Learning Repository, or publicly available datasets on GitHub.

Level up your knowledge by extending to unsupervised learning and then other sub-fields of ML, like neural networks, specifically, transformers and Reinforcement Learning.

Libraries like TensorFlow and PyTorch are commonly used for building and training deep learning models.

Going Over and Above

 
You are all set to implement your knowledge by practicing hands-on. This helps you internalize the concepts much better and prepares you to showcase your expertise.

Here are some ideas to get things off the ground:

While you are at it, try contributing to open-source projects or collaborate with others in hackathons to enhance your portfolio.

In the end, your understanding of building a complete ML pipeline, from data preprocessing to model deployment, prepares you to become a successful ML practitioner.

Now that you are on your journey to become a pro, you need to stay updated with the latest trends and techniques by following blogs, reading research papers, and participating in ML communities.
 
 

Vidhi Chugh is an AI strategist and a digital transformation leader working at the intersection of product, sciences, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, an author, and an international speaker. She is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.

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