| 1 |
Exploratory Data Analysis (EDA) (Descriptive Stats, Visualization, Correlation Analysis) |
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| 2 |
Introduction to Machine Learning (ML Basics) |
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| 3 |
Mathematics for ML (Linear Algebra, Calculus, Probability, Statistics) |
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| 4 |
Python for ML (Python Basics, Libraries like NumPy, Pandas, Matplotlib, Seaborn) |
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| 5 |
Data Preprocessing (Handling Missing Data, Outliers, Scaling, Normalization) |
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| 6 |
Feature Engineering & Feature Selection |
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| 7 |
Supervised Learning Basics (Regression & Classification Concepts) |
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| 8 |
Linear Regression (Simple, Multiple, Regularization Techniques) |
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| 9 |
Logistic Regression |
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| 10 |
Decision Trees |
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| 11 |
Random Forest |
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| 12 |
Gradient Boosting (XGBoost, LightGBM, CatBoost) |
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| 13 |
Support Vector Machines (SVM) |
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| 14 |
k-Nearest Neighbors (kNN) |
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| 15 |
Naive Bayes |
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| 16 |
Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC) |
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| 17 |
Cross-Validation (K-Fold, Stratified K-Fold) |
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| 18 |
Hyperparameter Tuning (Grid Search, Random Search, Bayesian Optimization) |
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| 19 |
Unsupervised Learning Basics (Clustering, Dimensionality Reduction) |
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| 20 |
Clustering Algorithms (K-Means, Hierarchical, DBSCAN) |
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| 21 |
Dimensionality Reduction (PCA, LDA, t-SNE, UMAP) |
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| 22 |
Advanced ML Algorithms (Ensemble Methods, Stacking, Bagging, Boosting) |
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| 23 |
Time Series Analysis (ARIMA, SARIMA, Prophet) |
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| 24 |
Natural Language Processing (NLP) Basics (Tokenization, Lemmatization, Stop Words) |
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| 25 |
NLP Advanced (Word Embeddings, Word2Vec, GloVe, Transformers, BERT, GPT) |
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| 26 |
Deep Learning Basics (Neural Networks, Activation Functions, Backpropagation) |
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| 27 |
Convolutional Neural Networks (CNNs) |
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| 28 |
Recurrent Neural Networks (RNNs, LSTM, GRU) |
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| 29 |
Generative Models (GANs, Variational Autoencoders) |
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| 30 |
Deep Learning Frameworks (TensorFlow, Keras, PyTorch) |
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| 31 |
Reinforcement Learning Basics (Markov Decision Process, Q-Learning, Policy Gradients) |
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| 32 |
Model Deployment (Flask, FastAPI, Docker, Cloud Deployment) |
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| 33 |
Monitoring & Performance Tracking of Models |
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| 34 |
Big Data for ML (Hadoop, Spark MLlib, PySpark) |
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| 35 |
Ethics & Bias in ML (Fairness, Explainability, GDPR, Privacy) |
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| 36 |
Continuous Learning (Advanced Deep Learning, Transformers, LLMs, Reinforcement Learning Advanced) |
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