Description
This comprehensive course provides learners with a structured path to understanding and mastering the fundamental principles of Artificial Intelligence (AI). The curriculum is designed for beginning and intermediate learners and includes core knowledge, hands-on experience, and industry-relevant skills. By the end of this course, participants will be well-prepared to create AI-powered solutions and pursue higher certifications in the industry.
This course includes
- 03+ hours on-demand video
- Unlimited Access, Anytime, on mobile and TV
- Certificate of completion
1. Introduction to Artificial Intelligence
This module introduces the concept of AI, its history, and its transformative impact on industries. Students will explore various types of AI and its ethical implications. By the end, learners will have a clear understanding of how AI fits into the broader technological landscape.
2. Foundations of Artificial Intelligence
The foundation module covers the essential mathematical and programming skills required for AI. Learners will gain proficiency in data representation, optimization techniques, and logic reasoning. This module builds the groundwork necessary for advanced AI topics.
3. Machine Learning of Artificial Intelligence
Focused on machine learning, this module dives into supervised, unsupervised, and reinforcement learning techniques. Participants will learn how to build predictive models, evaluate their performance, and apply machine learning to real-world datasets.
4. Deep Learning
This advanced module explores the architecture and functioning of deep learning models, including neural networks, CNNs, and RNNs. Participants will use frameworks like TensorFlow and PyTorch to develop and train models for complex tasks like image recognition and sequence processing.
5. AWS Certified AI Practitioner
Learners will explore cloud-based AI solutions, focusing on AWS tools like SageMaker and Rekognition. This module emphasizes practical implementation, scalability, and certification preparation for AWS AI Practitioner.
Requirements
- Basic knowledge of programming concepts like variables, loops, and functions.
- Basic understanding of linear algebra (matrices, vectors).
- Basic knowledge of programming concepts like variables, loops, and functions.
Course Curriculum
- Introduction to Machine Learning AI (3:43)
- Supervised Learning (6:14)
- Unsupervised Learning (6:20)
- Clustering (9:27)
- Distance Measures (5:00)
- Dimensionality Reduction (13:47)
- Association Rule Learning (11:49)
- Reinforcement Learning (11:04)
- Types of Reinforcement Learning Part 1 (13:15)
- Types of Reinforcement Learning Part 2 (14:42)
- Introduction to AWS Certified AI Practitioner (2:24)
- Understanding AI and ML (3:15)
- Natural Language Processing (NLP) (1:21)
- Computer Vison (CV) (1:30)
- Applications of AI in Various Industries (1:33)
- Supervised vs Unsupervised Machine Learning (1:15)
- Algorithms of Supervised and Unsupervised Machine Learning (1:37)
- Reinforcement Learning (RL) (1:20)
- Principal Component Analysis (PCA) (0:31)
- Basic Questions (9:02)
- Introduction to AWS AI Services (0:24)
- Amazon SageMaker (4:14)
- Aws DeepLens (3:20)
- Amazon Comprehend (3:25)
- Case Studies (1:40)
- Intermediate Questions (15:14)
- Implementing AI Solutions with AWS (4:26)
- Woring with Amazon SageMaker (5:49)
- Using AWS Lex (1:41)
- Using AWS Polly (1:11)
- AWS Rekognition (1:16)
- Combining AWS Services (2:01)
- Understanding Foundation Models (1:20)
- Model Selection and Architecture (1:18)
- Data Preperation and Preprocessing (1:19)
- Model Training and Optimization (1:08)
- Model Evaluation and Deployment (1:28)
- Summary (0:29)
- Ethical Considerations and Best Practices in AI-ML (3:17)
- Introduction to Prompt Engineering (5:41)
- Continuous Improvement (0:24)
- Exam Overview (3:35)
- Advanced Questions (17:03)