Road Map - Machine Learning
๐ฏ Ultimate Machine Learning Roadmap (2025)
Beginner → Intermediate → Advanced → Real-World Projects
๐ข Stage 1: Foundations of Machine Learning (Beginner Level)
Learn the basics of programming, math, and ML concepts.
๐ Topics:
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What is Machine Learning?
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Types of ML: Supervised, Unsupervised, Reinforcement Learning
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Basic Python Programming
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Math for ML:
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Linear Algebra (Vectors, Matrices)
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Calculus (Derivatives, Gradients)
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Probability & Statistics
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๐งฐ Tools/IDEs:
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IDEs: Jupyter Notebook, Google Colab, VS Code
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Languages: Python (preferred), R (optional)
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Libraries: Numpy, Pandas, Matplotlib, Seaborn
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Version Control: Git + GitHub
๐งช Mini Projects:
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Predict student scores using linear regression
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Visualize Iris dataset with scatter plots
๐ก Stage 2: Core Machine Learning (Intermediate Level)
Start training models, tuning them, and learning ML workflow.
๐ Topics:
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Data Preprocessing
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Exploratory Data Analysis (EDA)
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Feature Engineering & Selection
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Model Training & Evaluation:
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Regression (Linear, Ridge, Lasso)
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Classification (Logistic, KNN, Decision Trees, SVM)
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Clustering (KMeans, DBSCAN)
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Model Metrics:
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Accuracy, Precision, Recall, F1-score, ROC-AUC
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Cross-Validation, Grid Search, Random Search
๐งฐ Tools:
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Libraries: Scikit-learn, XGBoost, LightGBM, Statsmodels
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Visualization: Seaborn, Plotly
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Datasets: Kaggle, UCI ML Repository
๐งช Projects:
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House Price Prediction
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Spam Email Classifier
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Customer Segmentation (Clustering)
๐ Stage 3: Advanced Machine Learning
Dive into complex models, deployment, and real-world data.
๐ Topics:
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Ensemble Learning: Bagging, Boosting, Stacking
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Time Series Forecasting:
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ARIMA, Prophet, LSTM
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Dimensionality Reduction:
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PCA, t-SNE, UMAP
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Natural Language Processing (Basic NLP)
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TF-IDF, Word Embeddings (Word2Vec, GloVe)
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Deep Learning Introduction:
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Neural Networks (ANN)
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CNNs, RNNs
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Hyperparameter Tuning (Optuna, Hyperopt)
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Model Interpretability (SHAP, LIME)
๐งฐ Tools/Frameworks:
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Libraries: TensorFlow, PyTorch, Keras
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Experiment Tracking: MLflow, Weights & Biases
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Hyperparameter Tuning: Optuna, Scikit-optimize
๐งช Projects:
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Loan Approval Prediction (Classification)
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Stock Price Forecasting (Time Series)
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Sentiment Analysis (NLP)
๐ต Stage 4: Production-Ready ML & MLOps
Learn how to deploy, monitor, and manage ML in production.
๐ Topics:
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Model Deployment:
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Flask API, FastAPI, Streamlit, Gradio
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Dockerization of ML Models
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CI/CD for ML (MLOps)
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Cloud Platforms:
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AWS Sagemaker, GCP Vertex AI, Azure ML
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Data Pipelines with Airflow, Prefect
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Monitoring, Logging, Retraining
๐งฐ Tools:
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Deployment: Docker, Flask, FastAPI, Streamlit
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Cloud: AWS, GCP, Azure
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MLOps: MLflow, Kubeflow, DVC, Airflow
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Web Apps: Streamlit, Gradio, HuggingFace Spaces
๐งช Real-World Projects:
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End-to-End ML App (Streamlit + Flask API)
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Movie Recommendation System
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Image Classifier + Web App
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E-commerce Product Price Optimization
๐ Suggested Timeline
Duration | Focus |
---|---|
Month 1–2 | Python, Math, EDA, Git |
Month 3–4 | ML Algorithms & Projects |
Month 5–6 | Ensemble, NLP, Time Series |
Month 7–8 | Deep Learning, Deployment |
Month 9–10 | MLOps, Cloud, Real Apps |
Month 11–12 | Capstone Projects & Interviews |
๐ง Career Focus Paths:
Choose one or more based on your interest:
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Data Scientist – ML + Data + Statistics + Business Insight
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ML Engineer – ML + Deployment + MLOps + Cloud
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AI Engineer – ML + Deep Learning + Computer Vision/NLP
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Researcher – Theory + Math + Paper Reimplementation
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Product/Decision Scientist – ML + Visualization + Stakeholder reports
๐ Recommended Resources
Platforms:
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Free: Kaggle, Google ML Crash Course, YouTube (Krish Naik, StatQuest)
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Paid: Coursera ML by Andrew Ng, DeepLearning.AI, Udemy, fast.ai
Books:
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"Hands-On ML with Scikit-Learn, Keras & TensorFlow" – A. Gรฉron
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"Deep Learning" – Ian Goodfellow
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"Pattern Recognition and Machine Learning" – Bishop
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