๐ 60-Day Python ML & Data Science Syllabus Plan
๐ Table of Contents
Phase 1: Python & Data Basics (Day 1–10)
๐ Goal: Python fundamentals + data handling
- Day 1: Python Basics – Variables, Data Types, Operators
- Day 2: Control Structures – if-else, loops, functions
- Day 3: Lists, Tuples, Sets, Dictionaries (examples)
- Day 4: File handling + Error handling
- Day 5: NumPy basics (arrays, vector operations)
- Day 6: NumPy advanced (matrix ops, broadcasting)
- Day 7: Pandas Series & DataFrame basics
- Day 8: Pandas Data Cleaning (missing data, duplicates)
- Day 9: Pandas GroupBy, Merge, Join, Concat
- Day 10: Matplotlib + Seaborn (visualization basics)
Phase 2: Statistics & Preprocessing (Day 11–20)
๐ Goal: Data understanding + feature processing
- Day 11: Statistics basics (mean, median, mode, std, variance)
- Day 12: Probability basics (distribution, sampling)
- Day 13: Data visualization advanced (pairplot, heatmap)
- Day 14: Handling Missing Values (drop, fill, impute)
- Day 15: Feature Scaling (Standardization, Normalization, Robust)
- Day 16: Encoding Categorical Variables (Label, OneHot)
- Day 17: Outlier detection (Z-score, IQR method)
- Day 18: Train/Test Split + Validation sets
- Day 19: Pipeline & ColumnTransformer intro
- Day 20: Practice Day — Mini Project (Titanic data preprocessing)
Phase 3: Supervised Learning (Day 21–35)
๐ Goal: Regression + Classification
- Day 21: Intro to ML + Types (Supervised, Unsupervised)
- Day 22: Linear Regression (theory + code)
- Day 23: Multiple Linear Regression + Polynomial Regression
- Day 24: Logistic Regression + Classification basics
- Day 25: KNN Algorithm (theory + implementation)
- Day 26: Decision Trees (regression + classification)
- Day 27: Random Forests + Feature Importance
- Day 28: Support Vector Machines (linear, RBF)
- Day 29: Naive Bayes Classifier (GaussianNB, MultinomialNB)
- Day 30: Practice Day — Compare 5 classifiers on Titanic dataset
- Day 31: Model Evaluation Metrics (accuracy, precision, recall, f1)
- Day 32: ROC Curve, AUC, Confusion Matrix
- Day 33: Cross-validation (k-fold, stratified)
- Day 34: Hyperparameter Tuning (GridSearchCV, RandomSearchCV)
- Day 35: Practice Day — Hyperparameter tuning on RandomForest
Phase 4: Unsupervised Learning & Feature Engineering (Day 36–45)
๐ Goal: Clustering + Dimensionality Reduction
- Day 36: KMeans Clustering (theory + elbow method)
- Day 37: Hierarchical Clustering (dendrograms, agglomerative)
- Day 38: DBSCAN Clustering
- Day 39: PCA (Principal Component Analysis)
- Day 40: t-SNE, LDA intro
- Day 41: Feature Engineering (create new features)
- Day 42: Feature Selection methods (filter, wrapper, embedded)
- Day 43: Dimensionality Reduction practice (PCA on MNIST)
- Day 44: Clustering practice (customer segmentation)
- Day 45: Practice Day — Feature Engineering on Sales dataset
Phase 5: Advanced ML + Deployment (Day 46–55)
๐ Goal: Ensemble, Boosting, Deployment
- Day 46: Ensemble Learning basics
- Day 47: Bagging & Random Forests recap
- Day 48: Boosting (AdaBoost, Gradient Boosting)
- Day 49: XGBoost & LightGBM
- Day 50: Stacking Classifiers
- Day 51: Model Deployment intro (Flask, FastAPI)
- Day 52: Streamlit apps for ML
- Day 53: MLOps Basics (ML lifecycle, pipelines)
- Day 54: CI/CD in ML workflows
- Day 55: Practice Day — Deploy ML model on Streamlit
Phase 6: Advanced Topics + Project (Day 56–60)
๐ Goal: Interpretability, Fairness, End-to-End Project
- Day 56: Model Interpretability (SHAP, LIME)
- Day 57: Bias & Fairness in ML models
- Day 58: Recommender Systems basics (Content, Collaborative)
- Day 59: Time Series Forecasting intro (ARIMA, Prophet)
- Day 60: End-to-End Capstone Project (Data → Model → Deployment)
