Roadmap to Become Machine Learning Engineer

Deepshika

Are you looking to become a machine learning engineer?

Letโ€™s go through this roadmap to become machine and explore what you need to know to become an expert machine-learning engineer:

Math & Statistics

Just like most other data roles, machine learning engineering starts with strong foundations from math, especially in linear algebra, probability, and statistics. Hereโ€™s what you need to focus on:

โ€ข Basic probability concepts ๐ŸŽฒ

โ€ข Inferential statistics ๐Ÿ“Š

โ€ข Regression analysis ๐Ÿ“ˆ

โ€ข Experimental design & A/B testing ๐Ÿ”

โ€ข Bayesian statistics ๐Ÿ”ข

โ€ข Calculus ๐Ÿงฎ

โ€ข Linear algebra ๐Ÿ” 

Python

You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.

โ€ข Variables, data types, and basic operations โœ๏ธ

โ€ข Control flow statements (e.g., if-else, loops) ๐Ÿ”„

โ€ข Functions and modules ๐Ÿ”ง

โ€ข Error handling and exceptions โŒ

โ€ข Basic data structures (e.g., lists, dictionaries, tuples) ๐Ÿ—‚๏ธ

โ€ข Object-oriented programming concepts ๐Ÿงฑ

โ€ข Basic work with APIs ๐ŸŒ

โ€ข Detailed data structures and algorithmic thinking ๐Ÿง 

Machine Learning Prerequisites

โ€ข Exploratory Data Analysis (EDA) with NumPy and Pandas ๐Ÿ”

โ€ข Data visualization techniques to visualize variables ๐Ÿ“‰

โ€ข Feature extraction & engineering ๐Ÿ› ๏ธ

โ€ข Encoding data (different types) ๐Ÿ”

Machine Learning Fundamentals

Use the scikit-learn library along with other Python libraries for:

โ€ข Supervised Learning: Linear Regression, K-Nearest Neighbors, Decision Trees ๐Ÿ“Š

โ€ข Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Hierarchical Clustering ๐Ÿง 

โ€ข Reinforcement Learning: Q-Learning, Deep Q Network, Policy Gradients ๐Ÿ•น๏ธ

Solve two types of problems:

โ€ข Regression ๐Ÿ“ˆ

โ€ข Classification ๐Ÿงฉ

Neural Networks

Neural networks are like computer brains that learn from examples ๐Ÿง , made up of layers of “neurons” that handle data. They learn without explicit instructions.

Types of Neural Networks:

โ€ข Feedforward Neural Networks: Simplest form, with straight connections and no loops ๐Ÿ”„

โ€ข Convolutional Neural Networks (CNNs): Great for images, learning visual patterns ๐Ÿ–ผ๏ธ

โ€ข Recurrent Neural Networks (RNNs): Good for sequences like text or time series ๐Ÿ“š

In Python, use TensorFlow and Keras, as well as PyTorch for more complex neural network systems.

Deep Learning

Deep learning is a subset of machine learning that can learn unsupervised from data that is unstructured or unlabeled.

โ€ข CNNs ๐Ÿ–ผ๏ธ

โ€ข RNNs ๐Ÿ“

โ€ข LSTMs โณ

Machine Learning Project Deployment

Machine learning engineers should dive into MLOps and project deployment.

Here are the must-have skills:

โ€ข Version Control for Data and Models ๐Ÿ—ƒ๏ธ

โ€ข Automated Testing and Continuous Integration (CI) ๐Ÿ”„

โ€ข Continuous Delivery and Deployment (CD) ๐Ÿšš

โ€ข Monitoring and Logging ๐Ÿ–ฅ๏ธ

โ€ข Experiment Tracking and Management ๐Ÿงช

โ€ข Feature Stores ๐Ÿ—‚๏ธ

โ€ข Data Pipeline and Workflow Orchestration ๐Ÿ› ๏ธ

โ€ข Infrastructure as Code (IaC) ๐Ÿ—๏ธ

โ€ข Model Serving and APIs ๐ŸŒ