**Are you looking to become a machine learning engineer? **

Letโs 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. Hereโs what you need to focus on:

โข Basic probability concepts ๐ฒ

โข Inferential statistics ๐

โข Regression analysis ๐

โข Experimental design & A/B testing ๐

โข Bayesian statistics ๐ข

โข Calculus ๐งฎ

โข Linear algebra ๐

**Python**

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

โข Variables, data types, and basic operations โ๏ธ

โข Control flow statements (e.g., if-else, loops) ๐

โข Functions and modules ๐ง

โข Error handling and exceptions โ

โข Basic data structures (e.g., lists, dictionaries, tuples) ๐๏ธ

โข Object-oriented programming concepts ๐งฑ

โข Basic work with APIs ๐

โข Detailed data structures and algorithmic thinking ๐ง

**Machine Learning Prerequisites**

โข Exploratory Data Analysis (EDA) with NumPy and Pandas ๐

โข Data visualization techniques to visualize variables ๐

โข Feature extraction & engineering ๐ ๏ธ

โข Encoding data (different types) ๐

**Machine Learning Fundamentals**

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

โข Supervised Learning: Linear Regression, K-Nearest Neighbors, Decision Trees ๐

โข Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Hierarchical Clustering ๐ง

โข Reinforcement Learning: Q-Learning, Deep Q Network, Policy Gradients ๐น๏ธ

Solve two types of problems:

โข Regression ๐

โข Classification ๐งฉ

**Neural Networks**

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

Types of Neural Networks:

โข Feedforward Neural Networks: Simplest form, with straight connections and no loops ๐

โข Convolutional Neural Networks (CNNs): Great for images, learning visual patterns ๐ผ๏ธ

โข Recurrent Neural Networks (RNNs): Good for sequences like text or time series ๐

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.

โข CNNs ๐ผ๏ธ

โข RNNs ๐

โข LSTMs โณ

**Machine Learning Project Deployment**

Machine learning engineers should dive into MLOps and project deployment.

Here are the must-have skills:

โข Version Control for Data and Models ๐๏ธ

โข Automated Testing and Continuous Integration (CI) ๐

โข Continuous Delivery and Deployment (CD) ๐

โข Monitoring and Logging ๐ฅ๏ธ

โข Experiment Tracking and Management ๐งช

โข Feature Stores ๐๏ธ

โข Data Pipeline and Workflow Orchestration ๐ ๏ธ

โข Infrastructure as Code (IaC) ๐๏ธ

โข Model Serving and APIs ๐