Certification in Deep Learning AI

Learn Python for Deep Learning, Neural Networks, Transfer Learning and Pre-trained Models, Generative Deep Learning, NLP using Deep Learning, Model Evaluation, Hyperparameter Tuning, and Deployment

Description

Take the next step in your AI and Deep Learning journey. Whether the goal is to become a deep learning engineer, AI researcher, or data scientist, this course provides the theoretical foundation and practical skills required to build, train, evaluate, and deploy deep learning models.

Guided by structured modules, hands-on projects, and real-world case studies, participants will:

• Master core AI and deep learning concepts.

By the end of the course, learners will be prepared to design and implement deep learning solutions used in modern AI-driven applications.

The Frameworks of the Course

• Engaging video lectures, conceptual explanations, hands-on labs, projects, and downloadable resources designed to build strong theoretical and practical understanding.

• The course includes real-world case studies, coding exercises, self-paced assessments, and guided projects to reinforce learning outcomes.

• In the first part of the course, foundational concepts of AI, Python, and neural networks are established.

• In the middle part, learners work extensively with CNNs, RNNs, transfer learning, and generative models using TensorFlow and PyTorch.

• In the final part, focus shifts to NLP, model evaluation, deployment, monitoring, and capstone project implementation.

Course Content:

Part 1

Introduction and Study Plan

· Introduction and know your instructor

· Study Plan and Structure of the Course

Module 1. Introduction to AI & Deep Learning

1.1. Overview of AI and Machine Learning

1.2. History and Evolution of Deep Learning

1.3. Applications of Deep Learning

1.4. Conclusion of Introduction to AI and Deep Learning

Module 2. Python for Deep Learning

2.1. Numpy, Pandas, Matplotlib

2.2. Scikit-Learn Basics

2.3. Data Preprocessing and feature engineering for machine learning 101

2.4. Conclusion of Python for Deep Learning

Module 3. Fundamentals of Neural Networks

3.1. Biological vs Artificial Neurons

3.2. Perceptron, MLPs

3.3. Activation Functions

3.4. Forward & Backward Propagation

3.5. Cost Functions

3.6. Conclusion of Fundamentals of Deep Learning

Module 4. Deep Neural Networks (DNN)

4.1. Architecture & Layers

4.2. Gradient Descent & Optimization

4.3. Overfitting and Regularization

4.4. Weight Initialization

4.5. Batch Normalization and Dropout

4.6. Conclusion of Deep Neural Networks

Module 5. Convolutional Neural Networks

5.1. Convolution Operation

5.2. Pooling Layers

5.3. CNN Architectures (LeNet, AlexNet, VGG, ResNet)

5.4. Image Classification

5.5. Conclusion of Convolutional Neural Networks

Module 6. Recurrent Neural Networks (RNN) & LSTM

6.1. Sequence Modeling Basics

6.2. RNNs, Vanishing Gradient Problem

6.3. LSTM, GRU

6.4. Applications - Sentiment Analysis, Text Generation

6.5. Conclusion of Recurrent Neural Networks & LSTM

Module 7. Transfer Learning & Pre-trained Models

7.1. Concept of Transfer Learning

7.2. Feature Extraction vs Fine Tuning

7.3. Popular Pre - Trained Models

7.4. Hands-on with Pre-trained Models

7.5. Conclusion of Transfer Learning & Pre-trained Models

Module 8. Generative Deep Learning (GANs, VAEs)

8.1. Introduction to GANs

8.2. Generator and Discriminator

8.3. Variational Autoencoders (VAEs)

8.4. Use Cases

8.5. Hands-on Projects

8.6. Conclusion of Generative Deep Learning

Module 9. NLP with Deep Learning

9.1. Word Embeddings (Word2Vec, GloVe)

9.2. Sequence - to - Sequence Models

9.3. Transformers & Attention Mechanism

9.4. BERT, GPT Basics

9.5. Conclusion of NLP with Deep Learning

Module 10. Frameworks & Tools

10.1. TensorFlow Basics

10.2. PyTorch Basics

10.3. Projects and Assignments

10.4. Conclusion of Frameworks & Tools

Module 11. Model Evaluation, Tuning & Deployment

11.1. Confusion Matrix, ROC-AUC

11.2. Hyperparameter Tuning

11.3. Deployment

11.4. Model Monitoring

11.5. Conclusion of Model Evaluation, Tuning & Deployment

Part 2

Module 12. Capstone Project & Case Studies

Deep Learning is a subset of Artificial Intelligence (AI) and Machine Learning (ML) that uses artificial neural networks with multiple layers to automatically learn patterns from large volumes of data. These models mimic the way the human brain processes information, enabling machines to perform complex tasks such as vision, speech, language understanding, and decision-making.

How Deep Learning Works

Deep Learning models are built using deep neural networks consisting of:

• Input Layer – receives raw data (images, text, audio, numbers)

The models learn by:

• Forward propagation (prediction)

Key Deep Learning Models

• Artificial Neural Networks (ANNs) – Basic deep learning models

Core Tools & Frameworks

• Programming: Python

Uses of Deep Learning AI

1. Computer Vision

• Face recognition

2. Natural Language Processing (NLP)

• Chatbots & virtual assistants

3. Speech & Audio Processing

• Speech-to-text & text-to-speech

4. Healthcare & Biotech

• Disease prediction & diagnosis

5. Finance & Banking

• Fraud detection

6. Retail & Marketing

• Recommendation systems

7. Autonomous Systems

• Self-driving vehicles

8. Cyber Security

• Anomaly detection

9. Manufacturing & Industry 4.0

• Predictive maintenance