/Machine Learning - Advanced

Course Curriculum

Section 1 · Module 1: Introduction to Deep Learning & Neural Networks

○ Understanding Artificial Neural Networks (ANN) ○ Working with TensorFlow & Keras ○ Building and training a simple neural network

Section 2 · Module 2: Convolutional Neural Networks (CNN) for Image Processing

○ How CNNs work (filters, pooling, feature maps) ○ Implementing CNN for image classification ○ Transfer learning using pre-trained models (ResNet, VGG16)

Section 3 · Module 3: Recurrent Neural Networks (RNN) & Time Series Forecasting

○ Understanding RNN & Long Short-Term Memory (LSTM) networks ○ Forecasting stock prices with LSTM ○ Natural Language Processing (NLP) basics with RNNs

Section 4 · Module 4: Model Deployment & MLOps

○ Deploying ML models using Flask and FastAPI ○ Using AWS, GCP, or Azure for cloud deployment ▶ Introduction to MLOps for automating ML pipelines

Module 4: Model Deployment & MLOps

Introduction to MLOps for automating ML pipelines

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