Day 1: Python Refresh & NumPy
Topic 1: Python Refresher (if needed): Functions, classes, modules.
Topic 2: Introduction to NumPy: Arrays, array operations, broadcasting.
Day 2: Pandas for Data Manipulation
Topic 1: Pandas DataFrames: Creation, selection, indexing.
Topic 2: Data Cleaning with Pandas: Handling missing values, duplicates.
Day 3: Data Visualization with Matplotlib & Seaborn
Topic 1: Matplotlib Basics: Line plots, scatter plots, histograms.
Topic 2: Seaborn for Statistical Plots: Box plots, violin plots, heatmaps.
Day 4: Introduction to Linear Algebra for ML
Topic 1: Vectors and Matrices: Operations, dot product, transpose.
Topic 2: Systems of Linear Equations (conceptual understanding).
Day 5: Introduction to Calculus for ML
Topic 1: Derivatives and Gradients (conceptual understanding).
Topic 2: Gradient Descent intuition (how models learn).
Day 6: Probability & Statistics Basics
Topic 1: Probability distributions (Normal, Bernoulli).
Topic 2: Descriptive Statistics (mean, median, mode, variance, standard deviation).
Day 7: Review & Practice
Review all concepts learned.
Solve small coding challenges using NumPy and Pandas.
Day 1: Advanced EDA Techniques
Topic 1: Correlation analysis.
Topic 2: Outlier detection and treatment.
Day 2: Feature Engineering - Numerical Data
Topic 1: Scaling (Standardization, Normalization).
Topic 2: Binning, polynomial features.
Day 3: Feature Engineering - Categorical Data
Topic 1: One-Hot Encoding.
Topic 2: Label Encoding, Target Encoding (conceptual).
Day 4: Data Splitting & Cross-Validation
Topic 1: Train-Validation-Test Split.
Topic 2: K-Fold Cross-Validation.
Day 5: Introduction to Scikit-learn
Topic 1: Scikit-learn API: Estimators, transformers, pipelines.
Topic 2: Basic data loading and preprocessing with scikit-learn.
Day 6: Building a First ML Pipeline
Topic 1: Create a basic pipeline for data preprocessing and model training.
Topic 2: Implement a simple regression model end-to-end.
Day 7: Review & Mini Project
Consolidate EDA and preprocessing skills.
Mini-Project: Perform EDA and preprocess a simple dataset (e.g., Iris, Boston Housing).
Day 1: Linear Regression Theory
Topic 1: Simple Linear Regression: Hypothesis, cost function (MSE).
Topic 2: Gradient Descent for Linear Regression.
Day 2: Implementing Linear Regression
Topic 1: Coding Linear Regression from scratch (using NumPy).
Topic 2: Linear Regression with scikit-learn.
Day 3: Regression Evaluation Metrics
Topic 1: MAE, MSE, RMSE, R-squared.
Topic 2: Interpreting regression coefficients.
Day 4: Logistic Regression Theory
Topic 1: Logistic Regression: Sigmoid function, cross-entropy loss.
Topic 2: Gradient Descent for Logistic Regression.
Day 5: Implementing Logistic Regression
Topic 1: Coding Logistic Regression from scratch (using NumPy).
Topic 2: Logistic Regression with scikit-learn.
Day 6: Classification Evaluation Metrics
Topic 1: Accuracy, Precision, Recall, F1-score.
Topic 2: Confusion Matrix.
Day 7: Review & Practice
Solve problems involving both regression and classification on small datasets.
Day 1: Overfitting & Underfitting
Topic 1: Bias-Variance Trade-off.
Topic 2: Identifying overfitting and underfitting.
Day 2: Regularization Techniques
Topic 1: L1 (Lasso) Regularization.
Topic 2: L2 (Ridge) Regularization.
Day 3: Hyperparameter Tuning
Topic 1: Grid Search.
Topic 2: Random Search.
Day 4: Model Persistence
Topic 1: Saving and loading models (Joblib, Pickle).
Topic 2: Versioning trained models (conceptual).
Day 5: Project Structuring Introduction
Topic 1: Basic folder structure for an ML project (data/, notebooks/, src/, models/).
Topic 2: README.md and requirements.txt importance.
Day 6: Git & GitHub Basics
Topic 1: Version control with Git: add, commit, push, pull.
Topic 2: Creating a GitHub repository for your project.
Day 7: Mini-Project: Predictive Model with Regularization & Hyperparameter Tuning
Implement a full pipeline: preprocessing, model training with regularization, hyperparameter tuning, evaluation.
This phase introduces more complex algorithms and moves into unsupervised methods.
Day 1: Decision Tree Theory
Topic 1: How Decision Trees work: Splits, impurity (Gini, Entropy).
Topic 2: Overfitting in Decision Trees and Pruning.
Day 2: Implementing Decision Trees
Topic 1: Decision Trees for Classification with scikit-learn.
Topic 2: Decision Trees for Regression with scikit-learn.
Day 3: Ensemble Learning Introduction
Topic 1: Bagging vs. Boosting (conceptual).
Topic 2: Random Forests theory.
Day 4: Random Forests Implementation
Topic 1: Random Forest Classifier with scikit-learn.
Topic 2: Random Forest Regressor with scikit-learn.
Day 5: Feature Importance
Topic 1: Understanding feature importance from tree-based models.
Topic 2: Practical applications of feature importance.
Day 6: Advanced Evaluation Metrics
Topic 1: ROC AUC Curve and PR Curve (for imbalanced data).
Topic 2: Model interpretability beyond coefficients.
Day 7: Review & Practice
Day 1: Gradient Boosting Theory
Topic 1: AdaBoost (conceptual).
Topic 2: Gradient Boosting Machines (GBM) intuition.
Day 2: XGBoost & LightGBM
Topic 1: Introduction to XGBoost library.
Topic 2: Introduction to LightGBM library.
Day 3: Support Vector Machines (SVM) Theory
Topic 1: Linear SVM: Max-margin classifier.
Topic 2: Kernels (Polynomial, RBF) for non-linear data.
Day 4: Implementing SVMs
Topic 1: SVM Classifier with scikit-learn.
Topic 2: SVM Regressor with scikit-learn.
Day 5: Choosing the Right Algorithm
Topic 1: Factors influencing algorithm choice (data size, linearity, interpretability).
Topic 2: When to use which algorithm.
Day 6: Project Setup & Environment Management
Topic 1: Virtual environments (conda, venv).
Topic 2: requirements.txt best practices.
Day 7: Mini-Project: Advanced Classification/Regression
Day 1: Introduction to Unsupervised Learning
Topic 1: Difference between supervised and unsupervised learning.
Topic 2: Use cases for clustering and dimensionality reduction.
Day 2: K-Means Clustering Theory
Topic 1: K-Means algorithm: Centroids, inertia.
Topic 2: Elbow method for optimal K.
Day 3: Implementing K-Means
Topic 1: K-Means with scikit-learn.
Topic 2: Interpreting clusters.
Day 4: Hierarchical Clustering
Topic 1: Agglomerative vs. Divisive clustering (conceptual).
Topic 2: Dendrograms.
Day 5: DBSCAN Clustering
Topic 1: DBSCAN algorithm: Density-based clustering.
Topic 2: Advantages and disadvantages over K-Means.
Day 6: Cluster Evaluation Metrics
Topic 1: Silhouette Score.
Topic 2: Davies-Bouldin Index.
Day 7: Review & Practice
Day 1: Introduction to Dimensionality Reduction
Topic 1: Curse of Dimensionality.
Topic 2: Use cases for dimensionality reduction.
Day 2: Principal Component Analysis (PCA) Theory
Topic 1: PCA intuition: Variance maximization, eigenvalues/eigenvectors.
Topic 2: Interpreting principal components.
Day 3: Implementing PCA
Topic 1: PCA with scikit-learn.
Topic 2: Using PCA for visualization and feature extraction.
Day 4: t-SNE and UMAP (Conceptual)
Topic 1: t-SNE for visualization of high-dimensional data.
Topic 2: UMAP for dimensionality reduction and visualization.
Day 5: Feature Selection Techniques
Topic 1: Filter methods (e.g., variance threshold, correlation).
Topic 2: Wrapper methods (e.g., RFE).
Day 6: Model Interpretability
Topic 1: SHAP values (conceptual).
Topic 2: LIME (conceptual).
Day 7: Mini-Project: Unsupervised Learning Application
This phase introduces Deep Learning concepts and lays the groundwork for MLOps.
Day 1: Neural Network Fundamentals
Topic 1: Perceptrons, activation functions.
Topic 2: Feedforward Neural Networks architecture.
Day 2: Backpropagation & Optimization
Topic 1: How neural networks learn (conceptual understanding of backpropagation).
Topic 2: Optimizers (SGD, Adam, RMSprop).
Day 3: TensorFlow/Keras Basics
Topic 1: Setting up TensorFlow/Keras environment.
Topic 2: Building a simple dense neural network for classification.
Day 4: Training & Evaluation of NNs
Topic 1: Training process (epochs, batch size).
Topic 2: Loss functions, metrics for neural networks.
Day 5: Overfitting in Neural Networks
Topic 1: Dropout regularization.
Topic 2: Early stopping.
Day 6: Introduction to Computer Vision with CNNs
Topic 1: Convolutional Neural Network (CNN) intuition.
Topic 2: Common CNN architectures (LeNet, AlexNet - conceptual).
Day 7: Review & Practice
Day 1: Deeper Dive into CNNs
Topic 1: Convolutional layers, pooling layers.
Topic 2: Batch Normalization.
Day 2: Building a CNN for Image Classification
Topic 1: Practical implementation of a CNN in Keras for image classification (e.g., CIFAR-10).
Topic 2: Data Augmentation for image data.
Day 3: Transfer Learning
Topic 1: What is transfer learning and why it's useful.
Topic 2: Using pre-trained models (e.g., VGG16, ResNet) with Keras.
Day 4: Object Detection (Conceptual)
Topic 1: Introduction to object detection tasks.
Topic 2: High-level overview of R-CNN, YOLO, SSD.
Day 5: Image Segmentation (Conceptual)
Topic 1: Introduction to image segmentation tasks.
Topic 2: High-level overview of U-Net, Mask R-CNN.
Day 6: Model Explainability for Deep Learning (Conceptual)
Topic 1: Grad-CAM, LRP (Layer-wise Relevance Propagation).
Topic 2: Importance of interpreting deep learning models.
Day 7: Mini-Project: Image Classification with Transfer Learning
Build an image classifier using a pre-trained CNN on a custom dataset.
Week 11: Natural Language Processing (NLP) Fundamentals
Day 1: NLP Basics
Topic 1: Text preprocessing: Tokenization, stemming, lemmatization.
Topic 2: Bag-of-Words, TF-IDF.
Day 2: Recurrent Neural Networks (RNNs)
Topic 1: RNN architecture and sequential data.
Topic 2: Limitations of simple RNNs.
Day 3: LSTMs & GRUs
Topic 1: Long Short-Term Memory (LSTM) networks.
Topic 2: Gated Recurrent Units (GRU).
Day 4: Implementing RNNs/LSTMs for Text
Topic 1: Text classification with LSTMs in Keras.
Topic 2: Sentiment analysis example.
Day 5: Word Embeddings
Topic 1: Word2Vec, GloVe (conceptual).
Topic 2: Using pre-trained word embeddings.
Day 6: Attention Mechanism & Transformers (Conceptual)
Topic 1: Introduction to Attention.
Topic 2: High-level overview of Transformer architecture.
Day 7: Mini-Project: Basic NLP Task
Day 1: ML Project Lifecycle Overview
Topic 1: CRISP-DM vs. typical ML lifecycle (Problem Definition, Data, Modeling, Deployment, Monitoring).
Topic 2: Why MLOps is crucial.
Day 2: Defining the ML Problem
Topic 1: Business understanding: What problem are we solving?
Topic 2: Defining success metrics (business and technical).
Day 3: Data Collection Strategies
Topic 1: Data sources (databases, APIs, web scraping).
Topic 2: Data acquisition pipelines (conceptual).
Day 4: Data Versioning (DVC - Conceptual)
Topic 1: Why version data?
Topic 2: Introduction to Data Version Control (DVC).
Day 5: Feature Stores (Conceptual)
Topic 1: Why use feature stores?
Topic 2: Online vs. offline feature stores.
Day 6: Data Governance & Ethics in ML
Topic 1: Privacy concerns (GDPR, PII).
Topic 2: Fairness, bias in data and models.
Day 7: Case Study Analysis
Day 1: Experiment Tracking
Topic 1: Why track experiments?
Topic 2: Introduction to MLflow (logging parameters, metrics, models).
Day 2: Reproducibility in ML
Topic 1: Ensuring reproducible results.
Topic 2: Seed management, environment consistency.
Day 3: Code Organization for ML Projects
Topic 1: Modular code structure (src/, scripts/, tests/).
Topic 2: Best practices for writing clean and testable ML code.
Day 4: Unit Testing for ML Code
Topic 1: Testing data preprocessing functions.
Topic 2: Testing model training components.
Day 5: Model Registry (Conceptual)
Topic 1: Centralized model management.
Topic 2: Versioning and staging models.
Day 6: CI/CD for ML (Conceptual)
Topic 1: Continuous Integration/Continuous Deployment for ML.
Topic 2: Automated testing and deployment.
Day 7: Practice: MLflow Integration
Day 1: Model Deployment Overview
Topic 1: Different deployment patterns (batch, real-time, edge).
Topic 2: REST APIs for model serving.
Day 2: Flask/FastAPI for Model Serving
Topic 1: Building a simple Flask/FastAPI endpoint to serve predictions.
Topic 2: Receiving requests and returning predictions.
Day 3: Docker for Containerization
Topic 1: Docker fundamentals: Images, containers, Dockerfiles.
Topic 2: Containerizing your ML model and its dependencies.
Day 4: Cloud Deployment (AWS/GCP/Azure - Conceptual)
Topic 1: Overview of cloud ML services (SageMaker, Vertex AI, Azure ML).
Topic 2: Basic conceptual understanding of deploying a Docker container to a cloud platform.
Day 5: Kubernetes (Conceptual)
Topic 1: Introduction to Kubernetes for orchestration.
Topic 2: Scaling and managing containerized applications.
Day 6: Serverless Functions for ML (Conceptual)
Topic 1: Deploying models as serverless functions (AWS Lambda, Google Cloud Functions).
Topic 2: Pros and cons of serverless.
Day 7: Mini-Project: Deploy a Simple Model
Day 1: Model Monitoring
Topic 1: Why monitor models in production?
Topic 2: Key metrics to monitor (performance, data drift, concept drift).
Day 2: Data Drift Detection
Topic 1: Techniques for detecting changes in input data distribution.
Topic 2: Tools for data drift monitoring (e.g., Evidently AI - conceptual).
Day 3: Concept Drift Detection
Topic 1: Detecting changes in the relationship between input and output.
Topic 2: Strategies for handling concept drift (model retraining).
Day 4: Model Retraining Strategies
Topic 1: Scheduled retraining.
Topic 2: Event-driven retraining.
Day 5: A/B Testing for ML Models
Topic 1: Comparing different model versions in production.
Topic 2: Gradual rollout strategies.
Day 6: Incident Response & Alerting
Topic 1: Setting up alerts for model performance degradation.
Topic 2: Troubleshooting common MLOps issues.
Day 7: Review & Project Ideation
Day 1: Responsible AI & Explainable AI (XAI)
Topic 1: Bias and fairness in ML models.
Topic 2: Introduction to XAI tools (e.g., LIME, SHAP) in MLOps context.
Day 2: ML Security
Topic 1: Adversarial attacks on ML models.
Topic 2: Securing ML pipelines.
Day 3: Model Governance & Auditing
Topic 1: Compliance and regulatory requirements.
Topic 2: Maintaining audit trails for models.
Day 4: Reinforcement Learning (Conceptual)
Topic 1: Introduction to RL: Agents, environments, rewards.
Topic 2: Q-learning, Deep Q-Networks (conceptual).
Day 5: Graph Neural Networks (Conceptual)
Topic 1: Introduction to GNNs.
Topic 2: Use cases for graph data.
Day 6: Ethics in AI
Topic 1: Societal impact of AI.
Topic 2: Ethical guidelines for AI development.
Day 7: Capstone Project Planning & Literature Review