Are you looking to become a machine learning engineer?
Let’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. Here’s what you need to focus on:
• Basic probability concepts 🎲
• Inferential statistics 📊
• Regression analysis 📈
• Experimental design & A/B testing 🔍
• Bayesian statistics 🔢
• Calculus 🧮
• Linear algebra 🔠
Python
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
• Variables, data types, and basic operations ✏️
• Control flow statements (e.g., if-else, loops) 🔄
• Functions and modules 🔧
• Error handling and exceptions ❌
• Basic data structures (e.g., lists, dictionaries, tuples) 🗂️
• Object-oriented programming concepts 🧱
• Basic work with APIs 🌐
• Detailed data structures and algorithmic thinking 🧠
Machine Learning Prerequisites
• Exploratory Data Analysis (EDA) with NumPy and Pandas 🔍
• Data visualization techniques to visualize variables 📉
• Feature extraction & engineering 🛠️
• Encoding data (different types) 🔐
Machine Learning Fundamentals
Use the scikit-learn library along with other Python libraries for:
• Supervised Learning: Linear Regression, K-Nearest Neighbors, Decision Trees 📊
• Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Hierarchical Clustering 🧠
• Reinforcement Learning: Q-Learning, Deep Q Network, Policy Gradients 🕹️
Solve two types of problems:
• Regression 📈
• Classification 🧩
Neural Networks
Neural networks are like computer brains that learn from examples 🧠, made up of layers of “neurons” that handle data. They learn without explicit instructions.
Types of Neural Networks:
• Feedforward Neural Networks: Simplest form, with straight connections and no loops 🔄
• Convolutional Neural Networks (CNNs): Great for images, learning visual patterns 🖼️
• Recurrent Neural Networks (RNNs): Good for sequences like text or time series 📚
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.
• CNNs 🖼️
• RNNs 📝
• LSTMs ⏳
Machine Learning Project Deployment
Machine learning engineers should dive into MLOps and project deployment.
Here are the must-have skills:
• Version Control for Data and Models 🗃️
• Automated Testing and Continuous Integration (CI) 🔄
• Continuous Delivery and Deployment (CD) 🚚
• Monitoring and Logging 🖥️
• Experiment Tracking and Management 🧪
• Feature Stores 🗂️
• Data Pipeline and Workflow Orchestration 🛠️
• Infrastructure as Code (IaC) 🏗️
• Model Serving and APIs 🌐
