featured-image
Overview Prerequisites Skills You’ll Gain Self Study Materials Included Tools What You’ll Learn Course Modules FAQs

What’s Included?

icon High-Quality Video, E-book & Audiobook icon Modules Quizzes icon AI Mentor icon Access for Tablet & Phone icon Online Proctored Exam with One Free Retake

Prerequisites

    • Basic math, including familiarity with high school-level algebra and basic statistics, is desirable. 
    • Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential. 
    • A fundamental knowledge of programming skills is required. 

Skills You’ll Gain

  • Python for AI Development
  • Advanced Mathematics and Statistics
  • Optimization Techniques
  • Deep Learning Fundamentals
  • Data Processing and Exploratory Analysis
  • NLP, Computer Vision, or Reinforcement Learning Specialization
  • Time Series Analysis
  • Model Explainability and Deployment

Self Study Materials Included

Videos

Engaging visual content to enhance understanding and learning experience.

Podcasts

Insightful audio sessions featuring expert discussions and real-world cases.

Audiobooks

Listen and learn anytime with convenient audio-based knowledge sharing.

E-Books

Comprehensive digital guides offering in-depth knowledge and learning support.

Module Wise Quizzes

Interactive assessments to reinforce learning and test conceptual clarity.

Additional Resources

Supplementary references and list of tools to deepen knowledge and practical application.

Tools You’ll Master

GitHub Copilot

GitHub Copilot

Lobe

Lobe

H2O.ai

H2O.ai

Snorkel

Snorkel

What You’ll Learn

Python Programming Proficiency

Students will gain a solid foundation in Python programming, a crucial skill for implementing AI algorithms, processing data, and building AI applications effectively.

Deep Learning Techniques

Learners will master machine learning and deep learning techniques to address challenges in classification, regression, image recognition, and natural language processing.

Cloud Computing in AI Development

Students will get hands-on experience in cloud-based AI application development and learn how to use AWS, Azure, and Google Cloud for scalable AI systems.

Project Management in AI

Participations will master the skills necessary to manage AI projects effectively, from initiation to completion, including planning, resource allocation, risk management, and stakeholder communication.

Course Modules

Course Overview
  1. Course IntroductionPreview
Module 1: Foundations of Artificial Intelligence
  1. 1.1 Introduction to AI Preview
  2. 1.2 Types of Artificial Intelligence Preview
  3. 1.3 Branches of Artificial Intelligence
  4. 1.4 Applications and Business Use Cases
Module 2: Mathematical Concepts for AI
  1. 2.1 Linear Algebra Preview
  2. 2.2 Calculus Preview
  3. 2.3 Probability and Statistics Preview
  4. 2.4 Discrete Mathematics
Module 3: Python for Developer
  1. 3.1 Python Fundamentals Preview
  2. 3.2 Python Libraries
Module 4: Mastering Machine Learning
  1. 4.1 Introduction to Machine Learning
  2. 4.2 Supervised Machine Learning Algorithms
  3. 4.3 Unsupervised Machine Learning Algorithms
  4. 4.4 Model Evaluation and Selection
Module 5: Deep Learning
  1. 5.1 Neural Networks
  2. 5.2 Improving Model Performance
  3. 5.3 Hands-on: Evaluating and Optimizing AI Models
Module 6: Computer Vision
  1. 6.1 Image Processing Basics
  2. 6.2 Object Detection
  3. 6.3 Image Segmentation
  4. 6.4 Generative Adversarial Networks (GANs)
Module 7: Natural Language Processing
  1. 7.1 Text Preprocessing and Representation
  2. 7.2 Text Classification
  3. 7.3 Named Entity Recognition (NER)
  4. 7.4 Question Answering (QA)
Module 8: Reinforcement Learning
  1. 8.1 Introduction to Reinforcement Learning
  2. 8.2 Q-Learning and Deep Q-Networks (DQNs)
  3. 8.3 Policy Gradient Methods
Module 9: Cloud Computing in AI Development
  1. 9.1 Cloud Computing for AI
  2. 9.2 Cloud-Based Machine Learning Services
Module 10: Large Language Models
  1. 10.1 Understanding LLMs
  2. 10.2 Text Generation and Translation
  3. 10.3 Question Answering and Knowledge Extraction
Module 11: Cutting-Edge AI Research
  1. 11.1 Neuro-Symbolic AI
  2. 11.2 Explainable AI (XAI)
  3. 11.3 Federated Learning
  4. 11.4 Meta-Learning and Few-Shot Learning
Module 12: AI Communication and Documentation
  1. 12.1 Communicating AI Projects
  2. 12.2 Documenting AI Systems
  3. 12.3 Ethical Considerations
Optional Module: AI Agents for Developers
  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents

Frequently Asked Questions

Upon completion, you will receive an AI+ Developer™ certification, showcasing your proficiency in AI. You'll have the skills to tackle real-world AI challenges and implement advanced AI solutions in various domains.

While prior AI knowledge is not mandatory, a fundamental understanding of Python programming and basic math and statistics will help you grasp the advanced concepts covered in this course.

Yes, the course includes various hands-on projects and practical exercises to help you apply theoretical concepts to real-world scenarios, reinforcing your learning through practical experience.

You cannot choose a specialization in this course. However, you will be trained in areas such as Natural Language Processing (NLP), computer vision, and reinforcement learning.

Your progress will be evaluated through a combination of quizzes, hands-on exercises, and a final assessment. These evaluations are designed to test your understanding and application of the material.