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Certification in Deep Learning AI
Free Download Certification in Deep Learning AI
Published 2/2026
Created by Human and Emotion: CHRMI
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Expert | Genre: eLearning | Language: English | Duration: 78 Lectures ( 15h 4m ) | Size: 9.84 GB
Learn Python for Deep Learning, Neural Networks, Transfer Learning and Pre-trained Models, Generative Deep Learning, NLP
What you'll learn
✓ You will begin your deep learning journey with an Introduction to Artificial Intelligence and Deep Learning
✓ You will develop practical skills in Python for Deep Learning, working with essential libraries such as NumPy, Pandas, and Matplotlib for data handling
✓ You will gain a solid understanding of the Fundamentals of Neural Networks, comparing biological and artificial neurons and exploring perceptrons
✓ You will advance into Deep Neural Networks (DNNs), where network architectures, optimization techniques, gradient descent, and weight initialization
✓ You will explore Convolutional Neural Networks (CNNs), focusing on convolution operations, pooling layers, and well-known architectures
✓ You will study Recurrent Neural Networks (RNNs), LSTMs, and GRUs, learning sequence modeling fundamentals and understanding issues
✓ You will learn Transfer Learning and Pre-trained Models, exploring feature extraction and fine-tuning strategies
✓ You will explore Generative Deep Learning, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
✓ You will work with Natural Language Processing (NLP) using Deep Learning, covering word embeddings such as Word2Vec and GloVe
✓ You will gain hands-on experience with Deep Learning Frameworks and Tools, focusing on TensorFlow and PyTorch for model development
✓ You will master Model Evaluation, Hyperparameter Tuning, and Deployment, learning to use confusion matrices, ROC-AUC metrics
✓ By the end of the course, you will apply everything learned through a Capstone Project and Case Studies, where complete end-to-end deep learning
Requirements
● An interest in artificial intelligence, machine learning, and deep learning concepts
● Basic familiarity with Python programming and mathematical concepts such as linear algebra and probability is beneficial
Description
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.
• Build and train neural networks using Python.
• Apply CNNs, RNNs, and transformers to real-world problems.
• Work with transfer learning and generative models.
• Evaluate, tune, and deploy deep learning models effectively.
• Complete a full-scale capstone project demonstrating end-to-end expertise.
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
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)
• Hidden Layers – extract features and patterns through weighted connections
• Output Layer – produces predictions or classifications
The models learn by
• Forward propagation (prediction)
• Loss calculation (error measurement)
• Backpropagation (weight adjustment)
• Optimization (improving accuracy over time)
Key Deep Learning Models
• Artificial Neural Networks (ANNs) – Basic deep learning models
• Convolutional Neural Networks (CNNs) – Image & video processing
• Recurrent Neural Networks (RNNs) – Sequential data
• LSTM / GRU – Time-series & long-term memory tasks
• Transformers – Language & generative AI (BERT, GPT)
• Autoencoders – Feature extraction & anomaly detection
• GANs – Image generation & data synthesis
Core Tools & Frameworks
• Programming: Python
• Libraries: TensorFlow, PyTorch, Keras
• Data Handling: NumPy, Pandas
• Visualization: Matplotlib, Seaborn
• Hardware: GPUs / TPUs
• Cloud: AWS, Azure, Google Cloud
Uses of Deep Learning AI
1. Computer Vision
• Face recognition
• Medical image analysis (X-rays, MRI, CT scans)
• Object detection (self-driving cars, surveillance)
• Quality inspection in manufacturing
2. Natural Language Processing (NLP)
• Chatbots & virtual assistants
• Language translation
• Sentiment analysis
• Text summarization & document classification
3. Speech & Audio Processing
• Speech-to-text & text-to-speech
• Voice assistants (Alexa, Siri)
• Call center automation
• Speaker recognition
4. Healthcare & Biotech
• Disease prediction & diagnosis
• Drug discovery & molecular modeling
• Genomics & bioinformatics
• Personalized medicine
5. Finance & Banking
• Fraud detection
• Credit risk analysis
• Algorithmic trading
• Customer behavior prediction
6. Retail & Marketing
• Recommendation systems
• Demand forecasting
• Customer churn prediction
• Personalized advertising
7. Autonomous Systems
• Self-driving vehicles
• Robotics & automation
• Drones & smart navigation
8. Cyber Security
• Anomaly detection
• Intrusion detection systems
• Malware classification
9. Manufacturing & Industry 4.0
• Predictive maintenance
• Fault detection
• Process optimization
Who this course is for
■ Aspiring deep learning engineers, AI specialists, and machine learning practitioners seeking strong foundational and advanced skills.
■ Data scientists and software engineers looking to specialize in neural networks and deep learning applications.
■ Students, educators, and researchers interested in AI-driven problem solving and model development.
■ Professionals aiming to apply deep learning techniques in computer vision, NLP, and generative modeling.
Homepage
https://www.udemy.com/course/certification-in-deep-learning-ai
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