Deep Learning MiniCamp

By the end of this course, learners will be able to:

1. Understand the fundamentals of Neural Networks — their structure, components, and how they learn from data.
2. Build Neural Networks from scratch to gain a deep understanding of their internal operations.
3. Use Keras to efficiently build, train, and evaluate deep learning models.
4. Apply model optimization techniques to improve performance and prevent overfitting.
5. Perform hyperparameter tuning to fine-tune deep learning models for optimal accuracy.
6. Implement a Computer Vision project focused on Image Classification using deep learning.
7. Understand the basics of Natural Language Processing (NLP) and its real-world applications.
8. Clean and preprocess text data, and extract meaningful features for NLP tasks.
9. Build Sentiment Analysis models using both traditional Machine Learning and Artificial Neural Networks.
10. Implement Recurrent Neural Networks (RNNs) to handle sequential and text data effectively.
11. Engage in hands-on coding exercises and projects to reinforce learning and develop real-world problem-solving skills.
12. Gain the confidence to advance toward more complex topics in Deep Learning, NLP, and Generative AI.

• Machine Learning Students or Professionals who aims to extend their skills to the power of Deep Learning

Requirements

• Basic Python Programming
• Basic Machine Learning Knowledge