A comprehensive guide for software engineers transitioning into AI/ML engineering. Covers Python for ML, data preprocessing, supervised and unsupervised learning, deep learning with TensorFlow and PyTorch, NLP, computer vision, MLOps, model deployment, and career strategies.
Chapters
Python Ecosystem for Machine Learning
20 min read
Mathematics Fundamentals: Linear Algebra and Calculus Refresher
25 min read
Statistics and Probability for Data-Driven Decisions
20 min read
Data Wrangling and Exploratory Data Analysis
25 min read
Feature Engineering and Data Preprocessing Pipelines
20 min read
Supervised Learning: Regression and Classification Basics
25 min read
Advanced Supervised Learning Algorithms
20 min read
Unsupervised Learning: Clustering and Dimensionality Reduction
20 min read
Evaluating Model Performance and Avoiding Overfitting
20 min read
Neural Networks: From Perceptrons to Backpropagation
25 min read
Deep Learning Frameworks: Getting Started with TensorFlow
25 min read
Deep Learning Frameworks: Getting Started with PyTorch
25 min read
Computer Vision Architectures and Techniques
25 min read
Natural Language Processing: Text Embeddings and Transformers
25 min read
MLOps: Managing the Machine Learning Lifecycle
20 min read
Model Deployment: Serving Predictions at Scale
20 min read
Monitoring, Logging, and Versioning ML Models
20 min read
Ethics, Bias, and Fairness in AI Systems
15 min read
Career Roadmap: Building a Portfolio and Landing the Role
20 min read