Deep Learning Masterclass

The Deep Learning Masterclass is a project-based, advanced specialization program designed for professionals aiming to master the core foundations and applications of deep learning. This hands-on program dives deep into artificial neural networks (ANNs), convolutional neural networks (CNNs) for computer vision, and recurrent architectures like RNNs and LSTMs for natural language processing (NLP). Participants will explore cutting-edge tools while building real-world AI solutions through guided projects. From handwritten digit recognition and facial emotion detection to sentiment analysis and text summarization, learners will gain practical experience across multiple domains. With over 6 real-world projects, this masterclass equips participants with the skills to build, train, and optimize deep learning models—empowering them to lead innovation in AI development, automation, and intelligent decision-making across industries. The program culminates in a Capstone Project where you will build and deploy intelligent models—proving your ability to solve complex problems with deep learning at scale.

To enroll in the Deep Learning Masterclass (DLM), the Certified Data Scientist Professional (CDSP) certification is required (mandatory).

How you'll learn

• Instructor-Led Training (ILT): Participate in interactive, classroom-based sessions led by experienced industry professionals for an immersive learning experience.
• Virtual Instructor-Led Training (VILT): Join live online classes from anywhere, offering flexibility without compromising interactivity and mentorship.

• CDSP and CDSE graduatesGraduates ready to move to the next level.
• Data scientists and analystsData scientists and analysts ready for a hands-on deep learning experience.
• Software engineers and developersSoftware engineers and developers transitioning into AI/ML roles.
• Tech leads and managersTech leads and managers driving innovation with data.
• StatisticiansStatisticians expanding into scalable machine learning solutions.

• Build and train deep learning models.
• Master the foundations of neural networks, optimization techniques, and training strategies.
• Design and implement CNNs for real-world computer vision tasks like mask detection and emotion recognition.
• Apply RNNs and LSTMs for natural language processing, including sentiment analysis and text summarization.
• Complete a series of practical projects across vision and NLP domains.
• Deliver a full end-to-end deep learning capstone project—from data preparation to model evaluation and presentation.

Program Hours

40 Hours

Corporate Training

5 full days, with each day lasting 7–8 hours.

Individual Learners

5-week program, with 2 sessions per week, each session lasting 4 hours.

Upon successful completion of the Deep Learning Masterclass module, participants will receive an official Certificate of Achievement issued by Epsilon AI Learning — USA. This certification validates the participant's advanced technical capability in specialized domains of Deep Learning. The program is delivered in alignment with international standards, including ISO 21001 and ISO 9001 — with Epsilon AI Global, the parent company, also certified to ISO/IEC 27001:2022 for information security — and follows globally recognized frameworks such as IACET and CPD, ensuring a high-quality, industry-relevant learning experience. All graduates receive a digitally authenticated certificate containing a unique Certificate ID and personal EPSILON ID, enabling instant, secure online verification—ideal for professional portfolios, LinkedIn profiles, and employer validation. In addition to the digital credential, a prestigious hardcopy certificate is issued, printed with advanced security and branding features, including a unique serial number and verification code, a gold-embossed seal and authorized EPSILON AI signatures, and UV-printed security elements for enhanced authenticity and protection against forgery.

• Attend a minimum of 80% of live instructional sessions.
• Achieve a minimum score of 80% on the final examination.
• Successfully complete and present the Capstone Project.

• Introduction to Deep Learning
• Real-world applications in AI and automation
• Tools and libraries overview (TensorFlow, Keras, PyTorch)
• Setting up the Environment
• Google Colab configuration
• Managing dependencies and runtime

• Perceptron and Multi-layer Perceptrons (MLPs)
• Activation Functions (ReLU, Sigmoid, Tanh)
• Cost and Loss Functions
• Optimization Algorithms (SGD, Adam, RMSProp)
• Backpropagation Mechanics
• Strategies to Improve Training
• Dropout, Regularization, Batch Normalization
• Project #1: Handwritten Digit Recognition using the MNIST dataset

• Image Data Challenges and CNN Fundamentals
• Convolution and Pooling Layers
• Feature Extraction Techniques
• Data Augmentation Strategies
• CNN Architectures
• AlexNet, VGG, Inception
• Batch Normalization and Transfer Learning
• Image Classification Use Cases
• Project #2: COVID-19 Mask Detection using CNN
• Project #3: Emotion Recognition using Transfer Learning

• Introduction to NLP and Applications
• Text Preprocessing
• Tokenization, Stemming, Lemmatization
• Text Representation Techniques
• Bag-of-Words, TF-IDF
• Exploratory Text Analysis
• Word Clouds, Frequency Distributions
• Recurrent Neural Networks (RNNs)
• Introduction to Sequence Modeling
• RNN Architecture and Backpropagation Through Time
• Challenges: Vanishing and Exploding Gradients
• Long Short-Term Memory (LSTM)
• RNN Limitations Recap
• LSTM Cell Architecture (Gates and Memory)
• LSTM vs GRU Overview
• Project #4: Sentiment Analysis on IMDB Dataset
• Text Classification
• Project #5: IMDB Movie Review Sentiment Classification
• Information Retrieval & Text Summarization
• Extractive Summarization Techniques
• Sentiment and Semantic Analysis
• Project #6: Wikipedia Article Summarizer using NLP
• Final Capstone Project
• End-to-End Deep Learning Pipeline
• Build and present a comprehensive deep learning solution integrating both computer vision and NLP. Participants will define a real-world problem, gather and preprocess data, apply CNNs or LSTMs as needed, train and evaluate models, and present the solution with insights and visualizations.