Level |
Topic |
Subtopics |
Basic |
Introduction to ML |
What is Machine Learning, History of ML, Applications, Types of ML (Supervised, Unsupervised, Reinforcement), Difference between AI, ML, and DL |
|
Data Preparation & Cleaning |
Data Collection, Data Cleaning, Handling Missing Values, Feature Engineering, Data Normalization, Data Splitting |
|
Supervised Learning |
Regression, Classification, Linear Regression, Logistic Regression, Decision Trees |
|
Evaluation Metrics |
Accuracy, Precision, Recall, F1 Score, Confusion Matrix, ROC-AUC, Cross-Validation |
|
ML Tools & Libraries |
Python, NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, Jupyter Notebooks |
Intermediate |
Unsupervised Learning |
Clustering (K-Means, Hierarchical), Dimensionality Reduction (PCA, t-SNE), Association Rules, Anomaly Detection |
|
Feature Selection & Engineering |
Feature Importance, Correlation Analysis, One-Hot Encoding, Scaling, Normalization, Feature Transformation |
|
Model Selection & Tuning |
Hyperparameter Tuning, Grid Search, Random Search, Model Validation, Bias-Variance Tradeoff |
|
Ensemble Methods |
Bagging, Boosting, Random Forest, Gradient Boosting, XGBoost, AdaBoost |
|
ML Pipelines & Workflow |
Pipeline Creation, Data Preprocessing, Model Training, Validation, Deployment, Model Monitoring |
Advanced |
Deep Learning Basics |
Neural Networks, Perceptron, Backpropagation, Activation Functions, Gradient Descent, Loss Functions |
|
Convolutional Neural Networks |
CNN Architecture, Convolution & Pooling Layers, Image Classification, Object Detection, Transfer Learning |
|
Recurrent Neural Networks |
RNN, LSTM, GRU, Sequence Modeling, Time Series Forecasting, Text Generation |
|
Optimization & Regularization |
Learning Rate, Momentum, Adam, RMSProp, Dropout, Batch Normalization, Early Stopping |
|
Model Evaluation & Interpretability |
Confusion Matrix, ROC-AUC, SHAP, LIME, Feature Importance, Error Analysis |
Expert |
Advanced ML & DL Techniques |
GANs, Variational Autoencoders, Reinforcement Learning, Deep Q-Networks, Policy Gradient, Multi-Agent RL |
|
Natural Language Processing |
Tokenization, Word Embeddings, Transformers, BERT, GPT, Sequence-to-Sequence Models |
|
Computer Vision Advanced |
Image Segmentation, Object Detection, Instance Segmentation, Attention Mechanisms, Vision Transformers |
|
Model Deployment & MLOps |
Model Serving, API Creation, Model Monitoring, A/B Testing, CI/CD for ML, Cloud Deployment |
|
AI Ethics & Research |
Bias Mitigation, Explainable AI, Responsible AI, Research Trends, Few-Shot & Zero-Shot Learning |
|
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