Engbrick is proud to present an innovative AI Learning Course designed to equip learners with comprehensive knowledge and skills in artificial intelligence. This immersive program spans 8 weeks and is structured into 8 meticulously crafted modules. Each module is designed to build a strong foundation in AI, covering essential topics from fundamentals to advanced applications. With a tuition fee of USD 2'500, this course offers an exceptional value for those seeking to advance their careers or deepen their understanding of AI. Join us at Engbrick and take the first step towards mastering the future of technology.
Engbrick AI Learning Course Curriculum
Course Duration: 8 Weeks
Tuition Fee: $2,500 USD
Overview: This 8-week immersive AI Learning Course by Engbrick is designed to provide learners with a comprehensive understanding of artificial intelligence, from foundational concepts to advanced applications. The curriculum is structured into 8 modules, each focusing on a critical aspect of AI, equipping participants with the skills needed to excel in the field.
Module 1: Introduction to Artificial Intelligence
Duration: Week 1
Objective: Understand the fundamentals of AI and its impact on various industries.
Topics:
Definition and history of AI
Types of AI: Narrow, General, and Superintelligence
AI applications in healthcare, finance, and technology
Ethical considerations in AI Activities:
Case studies on real-world AI applications
Interactive quiz on AI concepts
Module 2: Python Programming for AI
Duration: Week 2
Objective: Master Python programming as a foundational tool for AI development.
Topics:
Python basics: syntax, data structures, and functions
Libraries for AI: NumPy, Pandas, and Matplotlib
Working with Jupyter Notebooks
Introduction to version control with GitActivities:
Coding exercises to manipulate datasets
Building a simple data visualization project
Module 3: Mathematics for AI
Duration: Week 3
Objective: Develop a strong mathematical foundation for AI algorithms.
Topics:
Linear algebra: vectors, matrices, and eigenvalues
Calculus: derivatives, gradients, and optimization
Probability and statistics: distributions, Bayesian inference
Introduction to optimization techniquesActivities:
Problem-solving sessions on linear algebra and calculus
Application of statistical methods to sample datasets
Module 4: Machine Learning Fundamentals
Duration: Week 4
Objective: Gain proficiency in core machine learning concepts and techniques.
Topics:
Supervised vs. unsupervised learning
Regression, classification, and clustering
Model evaluation: accuracy, precision, recall, and F1-score
Introduction to scikit-learnActivities:
Building and evaluating a linear regression model
Hands-on classification project using scikit-learn
Module 5: Deep Learning and Neural Networks
Duration: Week 5
Objective: Explore deep learning architectures and their applications.
Topics:
Neural network basics: perceptrons, activation functions
Deep learning frameworks: TensorFlow and PyTorch
Convolutional Neural Networks (CNNs) for image processing
Recurrent Neural Networks (RNNs) for sequential dataActivities:
Implementing a CNN for image classification
Experimenting with RNNs on a time-series dataset
Module 6: Natural Language Processing (NLP)
Duration: Week 6
Objective: Understand and apply NLP techniques to process and analyze text data.
Topics:
Text preprocessing: tokenization, stemming, and lemmatization
Word embeddings: Word2Vec, GloVe
Transformer models: BERT and GPT architectures
Sentiment analysis and text generationActivities:
Building a sentiment analysis model
Creating a simple chatbot using a transformer model
Module 7: Advanced AI Applications
Duration: Week 7
Objective: Dive into cutting-edge AI applications and their implementation.
Topics:
Reinforcement learning: Q-learning, Deep Q-Networks
Generative AI: GANs and VAEs
AI in robotics and autonomous systems
AI deployment: cloud platforms and edge devicesActivities:
Simulating a reinforcement learning agent
Developing a generative model for image synthesis
Module 8: Capstone Project and AI Ethics
Duration: Week 8
Objective: Apply knowledge to a real-world project and explore ethical AI practices.
Topics:
Project planning and execution
Model deployment and monitoring
Bias, fairness, and transparency in AI
Future trends in AIActivities:
Completing a capstone project (e.g., building an end-to-end AI solution)
Group discussion on ethical dilemmas in AI
Presentation of capstone projects
Course Features
Hands-On Learning: Weekly projects and coding assignments to reinforce concepts.
Expert Instruction: Learn from industry professionals with extensive AI experience.
Community Support: Access to a dedicated forum for peer collaboration and Q&A.
Certificate of Completion: Awarded upon successful completion of the course and capstone project.
Learning Outcomes
By the end of the course, participants will be able to:
Understand and apply core AI concepts and techniques.
Develop and deploy machine learning and deep learning models.
Analyze and process text data using NLP.
Build advanced AI applications using reinforcement learning and generative models.
Address ethical considerations in AI development.
Join Engbrick’s AI Learning Course and take the first step toward mastering artificial intelligence!