Month 1: Foundation of Machine Learning
Introduction to Machine Learning
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Supervised Learning Algorithms (Linear Regression, Logistic Regression, Decision Trees, Random Forests)
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Evaluation Metrics for Classification and Regression
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Feature Engineering and Data Preprocessing
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Model Evaluation and Cross-Validation Techniques
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Month 2: Advanced Machine Learning Techniques
Ensemble Methods (Bagging, Boosting)
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Support Vector Machines (SVM)
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Dimensionality Reduction (Principal Component Analysis, t-SNE)
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Hyperparameter Tuning and Model Selection
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Introduction to Neural Networks
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Month 3: Deep Learning Fundamentals
Neural Network Architectures
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Activation Functions and Loss Functions
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Backpropagation and Gradient Descent
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Regularization Techniques (Dropout, L1/L2 Regularization)
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Optimization Algorithms (Adam, RMSprop, etc.)
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Month 4: Convolutional Neural Networks (CNNs)
Introduction to CNNs and Image Classification
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Convolutional Layers, Pooling Layers, and Padding
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Transfer Learning and Fine-Tuning
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Object Detection and Localization (YOLO, Faster R-CNN)
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Image Generation and Style Transfer
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Month 5: Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP)
Introduction to RNNs and Sequence Modeling
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Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
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Word Embeddings (Word2Vec, GloVe)
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Sentiment Analysis and Text Classification
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Sequence-to-Sequence Models and Machine Translation
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Month 6: Advanced Deep Learning Topics
Generative Adversarial Networks (GANs)
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Reinforcement Learning Basics
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Deep Reinforcement Learning (Q-Learning, Policy Gradient Methods)
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Autoencoders and Variational Autoencoders
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Deploying Deep Learning Models in Production
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