AI Engineer Bootcamp - Curriculum Index
Welcome to the AI Engineer Bootcamp! This repository contains a comprehensive, day-by-day blog series guiding you from Python fundamentals to state-of-the-art Deep Learning and Transformer architectures.
Below is the complete index of all the micro-tutorials generated for the curriculum so far.
Part 1: Foundations of AI Engineering
Week 1: Python Programming Basics
- Day 1: Introduction to Python and Development Setup
- Day 2: Control Flow - Giving Your Code a Brain
- Day 3: Functions and Modules - Building Reusable Code
- Day 4: Data Structures - The Backbone of Data Science
- Day 5: Working with Strings - Prepping Data for NLP
- Day 6: File Handling - Making Data Persistent
- Day 7: Pythonic Code and Project Work
Week 2: Data Science Essentials
- Day 1: Introduction to NumPy for Numerical Computing
- Day 2: Advanced NumPy Operations - Broadcasting and Filtering
- Day 3: Introduction to Pandas for Data Manipulation
- Day 4: Data Cleaning and Preparation with Pandas
- Day 5: Data Aggregation and Grouping in Pandas
- Day 6: Data Visualization with Matplotlib and Seaborn
- Day 7: Exploratory Data Analysis (EDA) Project
Week 3: Mathematics for Machine Learning
- Day 1: Linear Algebra Fundamentals
- Day 2: Advanced Linear Algebra Concepts
- Day 3: Calculus for Machine Learning - Derivatives
- Day 4: Calculus for Machine Learning - Integrals and Optimization
- Day 5: Probability Theory and Distributions
- Day 6: Statistics Fundamentals for AI Engineers
- Day 7: Mini-Project - Linear Regression from Scratch!
Week 4: Probability and Statistics for Machine Learning
- Day 1: Probability Theory and Random Variables
- Day 2: Probability Distributions in Machine Learning
- Day 3: Statistical Inference - Estimation and Confidence Intervals
- Day 4: Hypothesis Testing and P-Values
- Day 5: Types of Hypothesis Tests
- Day 6: Correlation and Regression Analysis
- Day 7: Statistical Analysis Project - Analyzing Real-World Data
Week 5: Introduction to Machine Learning
- Day 1: Machine Learning Basics and Terminology
- Day 2: Supervised Learning and Regression Models
- Day 3: Advanced Regression Models - Polynomials and Regularization
- Day 4: Introduction to Classification and Logistic Regression
- Day 5: Model Evaluation and Cross-Validation
- Day 6: k-Nearest Neighbors (k-NN) Algorithm
- Day 7: Supervised Learning Mini Project
Part 2: Machine Learning Algorithms
Week 6: Feature Engineering and Model Evaluation
- Day 1: Introduction to Feature Engineering
- Day 2: Data Scaling and Normalization
- Day 3: Encoding Categorical Variables
- Day 4: Feature Selection Techniques
- Day 5: Creating and Transforming Features
- Day 6: Model Evaluation Techniques
- Day 7: Cross Validation and Hyperparameter Tuning
Week 7: Advanced Machine Learning Algorithms
- Day 1: Introduction to Ensemble Learning
- Day 2: Bagging and Random Forests
- Day 3: Boosting and Gradient Boosting
- Day 4: Introduction to XGBoost
- Day 5: LightGBM and CatBoost
- Day 6: Handling Imbalanced Data
- Day 7: Ensemble Learning Project
Week 8: Model Tuning and Optimization
- Day 1: Introduction to Hyperparameter Tuning
- Day 2: Grid Search and Random Search
- Day 3: Advanced Hyperparameter Tuning with Bayesian Optimization
- Day 4: Regularization Techniques for Model Optimization
- Day 5: Cross-Validation and Model Evaluation Techniques
- Day 6: Automated Hyperparameter Tuning
- Day 7: Optimization Capstone Project
Part 3: Deep Learning and Modern AI
Week 9: Neural Networks and Deep Learning Fundamentals
- Day 1: Introduction to Deep Learning and Neural Networks
- Day 2: Forward Propagation and Activation Functions
- Day 3: Loss Functions and Backpropagation
- Day 4: Gradient Descent and Optimization Techniques
- Day 5: Building Neural Networks in TensorFlow
- Day 6: Building Neural Networks with PyTorch
- Day 7: The Neural Network Capstone
Week 10: Convolutional Neural Networks (CNNs)
- Day 1: Introduction to Convolutional Neural Networks
- Day 2: Convolutional Layers and Filters
- Day 3: Pooling Layers and Dimensionality Reduction
- Day 4: Building CNN Architectures with Keras and TensorFlow
- Day 5: Building CNN Architectures with PyTorch
- Day 6: Regularization and Data Augmentation
- Day 7: CNN Image Classification Capstone
Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling
- Day 1: Introduction to Sequence Modeling and RNNs
- Day 2: Understanding RNN Architecture and BPTT
- Day 3: Long Short-Term Memory (LSTM) Networks
- Day 4: Gated Recurrent Units (GRUs)
- Day 5: Text Preprocessing and Word Embeddings
- Day 6: Sequence-to-Sequence (Seq2Seq) Models
- Day 7: RNN Project & Sentiment Analysis Capstone
Week 12: Transformers and Attention Mechanisms
- Day 1: Introduction to Attention Mechanisms
- Day 2: Introduction to Transformers Architecture
- Day 3: Self-Attention and Multi-Head Attention
- Day 4: Positional Encoding and Feed-Forward Networks
- Day 5: Hands-On with Pre-Trained Transformers
- Day 6: Advanced Transformers - Fine-Tuning BERT Variants
- Day 7: Transformer Project - Text Summarization Capstone