Machine Learning

Introduction

ML engineering overview: what the role entails, how it differs from data science…

• What is an ML Engineer?
• ML Engineer vs AI Engineer
• Skills and Responsibilities

Calculus

Differential calculus concepts essential for understanding gradient descent and …

• Derivatives, Partial Derivatives
• Chain Rule of Derivation
• Gradient, Jacobian, Hessian

Linear Algebra

Matrix math and vector spaces — the language of ML. Critical for understanding t…

• Matrix & Matrix Operations
• Scalars, Vectors, Tensors
• Singular Value Decomposition
• Determinants, Inverse of Matrix
• Eigenvalues, Diagonalization

Probability & Statistics

Statistical foundations for ML: probability theory, distributions, and Bayesian …

• Basics of Probability
• Bayes Theorem
• Random Variables, PDFs
• Types of Distribution

Python

Python mastery — the primary language for ML. From basics to scientific computin…

• Python Basics & OOP
• NumPy & Pandas
• Matplotlib & Seaborn

Data Processing

Raw data to ML-ready datasets: cleaning, transforming, and engineering features.…

• Data Collection & Cleaning
• Feature Engineering
• Exploratory Data Analysis

Supervised Learning

Learning from labeled data: regression for continuous targets, classification fo…

• Linear Regression
• Logistic Regression
• Decision Trees & Random Forest
• Support Vector Machines

Unsupervised Learning

Finding patterns in unlabeled data: clustering, dimensionality reduction, anomal…

• K-Means Clustering
• PCA & Dimensionality Reduction
• Anomaly Detection

Neural Networks

Artificial neural networks: how neurons compute, train, and learn representation…

• Perceptron & Backpropagation
• Activation Functions
• Optimizers (SGD, Adam)

DL Frameworks

Industry-standard deep learning libraries for building, training, and deploying …

• TensorFlow / Keras
• PyTorch

DL Architectures

Specialized neural network architectures for images, sequences, text, and genera…

• CNN – Image Recognition
• RNN / LSTM – Sequences
• Transformers & Attention
• GANs – Generative Models

MLOps & Deployment

Taking ML models from notebooks to production: serving, monitoring, and CI/CD fo…

• Model Serving (Flask, FastAPI)
• Docker for ML
• MLflow / Weights & Biases
• Cloud ML (AWS, GCP)