180-Day AI and Machine Learning Course from Scratch
This exhaustive curriculum provides a full 180-day plan, designed to take a learner from foundational skills to advanced AI topics. Each day's lesson is carefully structured to build upon the last, ensuring a smooth and logical learning progression.
Module 1: Foundational Skills (Days 1-36)
This module is a zero-to-hero primer on the core tools and math necessary for AI.
Week 1: Python Crash Course (Days 1-7)
Day 1: Introduction to Programming and Python Basics
Day 2: Variables, Data Types, and Operators
Day 3: Control Flow (If-Else Statements and Loops)
Day 4: Data Structures: Lists and Tuples
Day 5: Data Structures: Dictionaries and Sets
Day 6: Functions, Modules, and Libraries
Day 7: Project Day: Build a Simple Command-Line Game
Week 2-3: Linear Algebra & Calculus Essentials (Days 8-22)
Day 8: Introduction to Linear Algebra
Day 9: Vectors and Vector Operations
Day 10: Matrices and Matrix Operations
Day 11: Matrix Multiplication and Dot Products
Day 12: Introduction to Calculus
Day 13: Derivatives and Their Applications
Day 14: The Chain Rule and Partial Derivatives
Day 15: Gradients and Gradient Descent
Day 16-22: Break/Review Week: Rework concepts and practice math problems.
Week 4-5: Probability & Statistics for Data Science (Days 23-30)
Day 23: Introduction to Probability
Day 24: Conditional Probability and Bayes' Theorem
Day 25: Random Variables and Probability Distributions
Day 26: Descriptive Statistics (Mean, Median, Mode)
Day 27: Measures of Spread (Variance and Standard Deviation)
Day 28: Correlation and Covariance
Day 29: The Central Limit Theorem
Day 30: Project Day: Analyze a small dataset using descriptive statistics.
Week 6: Python Libraries for Data Science (Days 31-36)
Day 31: Introduction to NumPy
Day 32: NumPy Array Manipulation and Vectorization
Day 33: Introduction to Pandas
Day 34: DataFrames: Indexing, Slicing, and Filtering
Day 35: Data Cleaning and Handling Missing Data
Day 36: Project Day: Exploratory Data Analysis (EDA) with a real-world dataset.
Module 2: Introduction to Machine Learning (Days 37-84)
This module introduces the core concepts and classical machine learning algorithms using Scikit-learn.
Week 7: Core Concepts (Days 37-43)
Day 37: Introduction to AI, ML, and Deep Learning
Day 38: The Machine Learning Workflow
Day 39: Supervised vs. Unsupervised Learning
Day 40: Regression vs. Classification
Day 41: Overfitting and Underfitting
Day 42: Data Splitting (Train/Test/Validation)
Day 43: Model Evaluation Metrics (Accuracy, Precision, Recall)
Week 8-9: Supervised Learning: Regression (Days 44-57)
Day 44: Simple Linear Regression Theory
Day 45: Linear Regression with Scikit-learn
Day 46: Multiple Linear Regression
Day 47: Project Day: Predict Housing Prices
Day 48: Logistic Regression Theory
Day 49: Logistic Regression for Binary Classification
Day 50: Multi-Class Classification with Logistic Regression
Day 51: Project Day: Spam Detection
Day 52-57: Break/Review Week
Week 10-11: Supervised Learning: Classification (Days 58-70)
Day 58: Decision Trees Theory
Day 59: Decision Trees with Scikit-learn
Day 60: Random Forests and Ensemble Methods
Day 61: Project Day: Predict Credit Card Fraud
Day 62: K-Nearest Neighbors (KNN) Theory
Day 63: KNN with Scikit-learn
Day 64: Support Vector Machines (SVMs) Theory
Day 65: SVMs with Scikit-learn
Day 66-70: Review/Practice Days: Reinforce concepts with new datasets.
Week 12: Scikit-learn Hands-on (Days 71-84)
Day 71: The Scikit-learn Ecosystem
Day 72: Data Preprocessing and Feature Scaling
Day 73: Pipelines: Chaining Steps Together
Day 74: Feature Engineering
Day 75: Model Persistence (Saving and Loading Models)
Day 76-84: Project: Build an End-to-End ML Model on a Public Dataset (e.g., Kaggle Titanic).
Module 3: Unsupervised & Reinforcement Learning (Days 85-126)
This module delves into algorithms that find patterns in data and learn from interaction.
Week 13-14: Unsupervised Learning (Days 85-98)
Day 85: Introduction to Unsupervised Learning
Day 86: K-Means Clustering Theory
Day 87: K-Means with Scikit-learn
Day 88: How to Choose the Optimal Number of Clusters
Day 89: Project Day: Customer Segmentation
Day 90: Hierarchical Clustering
Day 91: Principal Component Analysis (PCA) Theory
Day 92: PCA for Dimensionality Reduction
Day 93-98: Break/Review Week.
Week 15-16: Reinforcement Learning & Other Topics (Days 99-112)
Day 99: Introduction to Reinforcement Learning (RL)
Day 100: Agents, Environments, and Rewards
Day 101: Q-Learning Algorithm
Day 102: Project Day: Implement a Simple RL Agent
Day 103: Recommender Systems Theory
Day 104: Collaborative Filtering
Day 105: Content-Based Filtering
Day 106-112: Project: Build a Simple Movie Recommender.
Week 17-18: Advanced ML & Course Review (Days 113-126)
Day 113: Gradient Boosting Machines
Day 114: XGBoost and LightGBM
Day 115: Bias-Variance Tradeoff
Day 116: Hyperparameter Tuning Theory
Day 117-126: Project: Improve a Previous Model with Hyperparameter Tuning.
Module 4: Deep Learning (Days 127-180)
The final module is dedicated to the study of deep learning, covering both theory and practical applications using TensorFlow/Keras and PyTorch.
Week 19-20: Neural Networks from Scratch (Days 127-140)
Day 127: The Perceptron and its Limitations
Day 128: Activation Functions
Day 129: Multi-Layer Perceptrons (MLPs)
Day 130: The Backpropagation Algorithm
Day 131-140: Project: Build a Neural Network to Classify Handwritten Digits (MNIST).
Week 21-22: Deep Learning with TensorFlow & PyTorch (Days 141-154)
Day 141: Introduction to TensorFlow and Keras
Day 142: Building a Sequential Model in Keras
Day 143: Training and Evaluating a Keras Model
Day 144: Introduction to PyTorch
Day 145: Tensors and Automatic Differentiation in PyTorch
Day 146: Building a Simple Neural Network in PyTorch
Day 147-154: Project: Image Classification with TensorFlow.
Week 23-24: Computer Vision (Days 155-168)
Day 155: Introduction to Computer Vision
Day 156: Convolutional Neural Networks (CNNs)
Day 157: Pooling Layers and Flattening
Day 158: Building a CNN for Image Classification
Day 159-168: Project: Build an Image Classifier for a Complex Dataset.
Week 25-26: Natural Language Processing (NLP) (Days 169-180)
Day 169: Introduction to NLP
Day 170: Text Preprocessing and Tokenization
Day 171: Word Embeddings (Word2Vec, GloVe)
Day 172: Recurrent Neural Networks (RNNs) and LSTMs
Day 173-176: Project: Build a Sentiment Analyzer
Day 177-180: Final Project: Build a more complex project of your choice (e.g., a chatbot, a generative AI model, a fraud detection system). Use these days to refine, document, and present your work, consolidating everything you’ve learned.
Are you starting this course fresh from today ? Excited.