Week 4: Descriptive Statistics & Probability

• Day 22: Measures of Central Tendency & Dispersion
• Day 23: Probability Theory Fundamentals
• Day 24: Probability Distributions – Normal, Binomial, Poisson
• Day 25: Random Variables & Expected Values
• Day 26: Covariance, Correlation & Dependence
• Day 27: Statistical Sampling Methods
• Day 28: Exploratory Data Analysis (EDA) Techniques

Week 5: Inferential Statistics

• Day 29: Confidence Intervals & Margin of Error
• Day 30: Hypothesis Testing – Z-tests, T-tests
• Day 31: ANOVA & Chi-Square Tests
• Day 32: Non-parametric Statistical Tests
• Day 33: Bayesian Statistics Introduction
• Day 34: A/B Testing for Business Decisions
• Day 35: Statistical Fallacies & Pitfalls

Week 6: Mathematics for Machine Learning

• Day 36: Linear Algebra Fundamentals – Vectors, Matrices
• Day 37: Matrix Operations & Transformations
• Day 38: Eigenvalues & Eigenvectors
• Day 39: Calculus Essentials – Derivatives, Gradients
• Day 40: Multivariable Calculus & Partial Derivatives
• Day 41: Optimization Algorithms & Techniques
• Day 42: Information Theory & Entropy

Week 7: Supervised Learning – Regression

• Day 43: Introduction to Machine Learning & Scikit-Learn
• Day 44: Linear Regression – Theory & Implementation
• Day 45: Polynomial Regression & Regularization Techniques
• Day 46: Decision Trees for Regression
• Day 47: Ensemble Methods – Random Forests for Regression
• Day 48: Support Vector Regression
• Day 49: Model Evaluation Metrics for Regression

Week 8: Supervised Learning – Classification

• Day 50: Logistic Regression – Theory & Implementation
• Day 51: Decision Trees for Classification
• Day 52: Ensemble Methods – Random Forests, Gradient Boosting
• Day 53: Support Vector Machines for Classification
• Day 54: Naive Bayes Classifiers
• Day 55: Multi-class Classification Strategies
• Day 56: Imbalanced Dataset Handling Techniques

Week 9: Unsupervised Learning & Model Evaluation

• Day 57: Introduction to Unsupervised Learning
• Day 58: K-means Clustering
• Day 59: Hierarchical Clustering
• Day 60: DBSCAN & Density-based Clustering
• Day 61: Principal Component Analysis (PCA) for Dimensionality Reduction
• Day 62: Model Evaluation Metrics for Classification & Clustering
• Day 63: Cross-Validation Techniques

Week 10: Introduction to Deep Learning

• Day 64: Introduction to Neural Networks & Deep Learning
• Day 65: Perceptrons & Artificial Neurons
• Day 66: Feedforward Neural Networks
• Day 67: Activation Functions – ReLU, Sigmoid, Tanh
• Day 68: Backpropagation & Gradient Descent
• Day 69: Overfitting & Regularization Techniques
• Day 70: Model Tuning & Hyperparameter Optimization

Week 11: Convolutional Neural Networks (CNNs)

• Day 71: Introduction to CNNs & Applications
• Day 72: Convolution Layers & Filters
• Day 73: Pooling Layers & Flattening
• Day 74: CNN Architecture – LeNet, AlexNet, VGG
• Day 75: Transfer Learning with Pre-trained Models
• Day 76: Fine-tuning CNNs for Image Classification
• Day 77: Evaluation of CNN Models

Week 12: Recurrent Neural Networks (RNNs) & Final Project

• Day 78: Introduction to RNNs & Time Series Data
• Day 79: Long Short-Term Memory (LSTM) Networks
• Day 80: Gated Recurrent Units (GRUs)
• Day 81: RNNs for Natural Language Processing (NLP)
• Day 82: Final Project Planning & Architecture
• Day 83: Final Project Development – Model Implementation
• Day 84: Final Project Evaluation & Presentation