Deep Learning Specialization: Advanced AI, Hands on Lab
Data Science Academy
AI Engineer | Deep Learning Instructor
Deep Learning Specialization: Advanced AI & Hands-On Labs for Real AI Systems
Artificial Intelligence is transforming every industry, but many professionals struggle to move beyond theory and actually build real AI systems. Most courses teach isolated concepts without showing how deep learning models are applied in real-world systems.
In this course, you will go beyond basic neural network theory and learn how modern AI systems are designed, trained, optimized, and deployed. Through structured lessons and hands-on labs, you will develop practical skills in building deep learning models used in real applications.
You will learn how neural networks work internally, how modern architectures such as deep feedforward networks are trained, and how optimization techniques improve model performance.
By the end of the course, you will understand how deep learning systems are built from the ground up and how they are used to solve complex real-world problems in areas such as computer vision, natural language processing, and intelligent automation.
This course is ideal for learners who want to move from simply studying AI to actually building modern deep learning systems.
What you’ll learn
Transform from AI learner to AI builder by mastering deep learning systems and applying advanced techniques through practical hands-on labs
Design, train, and optimize deep learning models using modern neural network architectures and advanced optimization techniques.
Apply reinforcement learning methods to build intelligent agents that learn from interaction and improve decision making.
Deploy deep learning models into real-world applications using scalable AI workflows and modern deployment strategies.
Evaluate model performance, interpret AI predictions, and apply responsible AI practices for reliable systems.
Understand emerging AI technologies including multimodal AI, foundation models, and next-generation deep learning systems.
Learn directly from Data
Data Science Academy
Practical, industry-focused training in Data Analytics, Data Science & BI.
Who this course is for
Aspiring Data Scientists and Machine Learning Engineers
AI Enthusiasts and Researchers
Software Developers and Engineers
Prerequisites
Basic Knowledge of Python
Learners should understand basic Python programming including variables, functions, loops, and working with common libraries.
Foundational Understanding of Machine Learning
Familiarity with core ML concepts such as supervised learning, model training, evaluation metrics, and basic algorithms.
Linear Algebra & Probability Basics
Basic understanding of vectors, matrices, probability, and statistics used in neural networks and machine learning models.
What's included
Live sessions
Learn directly from Data Science Academy in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
8 Days of structured deep learning training with hands-on labs
A structured 8-day learning roadmap designed to progressively build deep learning expertise, covering neural networks, optimization techniques, modern architectures, and practical AI system development through guided lessons and hands-on practice.
Step-by-step coding labs for CNNs, RNNs, Transformers, GANs, and RL
Hands-on coding labs where you implement deep learning models such as CNNs, RNNs, and Transformers using Python and popular deep learning frameworks. Each lab walks you through model design, training, and evaluation step-by-step.
Practical projects using real-world datasets and AI applications
Build practical AI projects using real-world datasets to gain experience solving real problems. Projects help you apply deep learning concepts to tasks like prediction, classification, and pattern recognition.
Implementation of modern architectures like BERT, GPT, and ResNet
Learn how to implement modern deep learning architectures such as Convolutional Neural Networks, Recurrent Neural Networks, and Transformer-based models used in computer vision and natural language processing.
Hands-on reinforcement learning with Q-learning and Deep Q Networks
Understand reinforcement learning concepts and implement algorithms such as Q-learning to train intelligent agents that learn optimal decisions through interaction with environments.
Generative AI labs including Autoencoders, GANs, and Diffusion models
Explore generative AI through practical labs implementing models like Autoencoders and Generative Adversarial Networks (GANs) to create synthetic data, images, and representations.
Model deployment using Flask/FastAPI and Docker
Learn how to deploy trained deep learning models as APIs using frameworks like Flask and FastAPI, enabling real-world AI applications that can serve predictions in production environments.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
Week 1
Day 1 - Foundations of Deep Learning & Neural Networks
Day 2 - Optimization & Regularization Techniques
Day 3 - Convolutional Neural Networks (CNNs)
Day 4 - Recurrent Neural Networks (RNNs) & Sequence Models
Day 5 - Transformers & Natural Language Processing (NLP)
Day 6 - Generative Models
Day 7 - Reinforcement Learning (RL) & Deep RL
Week 2
Day 8 - Ethics, Deployment & Future of AI
Schedule
Live sessions
1-2 hrs / week
Projects
6-7 hrs / week
Async content
10-15 hrs / week
Frequently asked questions
$200
USD
20 hours left to enroll