Machine Learning Roadmap - Advanced

Follow this step-by-step roadmap to master machineLearning at Advanced level

1

Neural Networks & Deep Learning

4 weeks

▹Perceptron & Multilayer Perceptron
▹Activation Functions (ReLU, Sigmoid, Tanh, Softmax)
▹Forward & Backpropagation
▹Gradient Descent Variants (SGD, Adam, RMSProp)
▹Mini-Project: Handwritten Digit Classifier (MNIST)

2

Convolutional Neural Networks (CNNs)

4 weeks

▹Convolution & Pooling Layers
▹CNN Architectures (LeNet, AlexNet, ResNet, VGG)
▹Transfer Learning
▹Data Augmentation
▹Mini-Project: Image Classification (CIFAR-10)

3

Recurrent Neural Networks (RNNs)

3 weeks

▹RNN Basics
▹Vanishing Gradient Problem
▹LSTM & GRU Networks
▹Sequence-to-Sequence Models
▹Mini-Project: Text Generation with LSTMs

4

Natural Language Processing (NLP)

4 weeks

▹Text Preprocessing (Tokenization, Lemmatization, Stopwords)
▹Word Embeddings (Word2Vec, GloVe, FastText)
▹Transformers (Attention Mechanism, BERT, GPT basics)
▹Mini-Project: Sentiment Analysis

5

Reinforcement Learning

4 weeks

▹Markov Decision Processes
▹Q-Learning
▹Deep Q Networks (DQN)
▹Policy Gradient Methods
▹Mini-Project: Game AI with RL

6

Production & Deployment

3 weeks

▹Saving & Loading Models
▹Flask/FastAPI for ML Deployment
▹Dockerizing ML Models
▹ML in Cloud (AWS, GCP, Azure)
▹MLOps Basics (CI/CD for ML, Monitoring Models)
▹Mini-Project: Deploy ML Model as API

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