Curriculum
4 Sections
12 Lessons
10 Weeks
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Module 1: Introduction to Deep Learning & Neural Networks
3
1.1
Understanding Artificial Neural Networks (ANN)
1.2
Working with TensorFlow & Keras
1.3
Building and training a simple neural network
Module 2: Convolutional Neural Networks (CNN) for Image Processing
3
2.1
How CNNs work (filters, pooling, feature maps)
2.2
Implementing CNN for image classification
2.3
Transfer learning using pre-trained models (ResNet, VGG16)
Module 3: Recurrent Neural Networks (RNN) & Time Series Forecasting
3
3.1
Understanding RNN & Long Short-Term Memory (LSTM) networks
3.2
Forecasting stock prices with LSTM
3.3
Natural Language Processing (NLP) basics with RNNs
Module 4: Model Deployment & MLOps
3
4.1
Deploying ML models using Flask and FastAPI
4.2
Using AWS, GCP, or Azure for cloud deployment
4.3
Introduction to MLOps for automating ML pipelines
Machine Learning – Advanced
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