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Requirements
- Basic computer skills
- Basic math knowledge
- Familiarity with data concepts
- Stable internet access
- A willingness to learn
- Basic Python knowledge
- Basic Statistics knowledge
Features
- Step-by-step lessons
- Hands-on projects
- Real-world case studies
- Practice datasets
- Quizzes and assessments
- Downloadable resources
- A case study project
- Beginner-friendly explanations
- Industry applications
Target audiences
- Beginners in tech
- Students
- IT professionals
- Engineers
- Entrepreneurs
- Business analysts
- Data professionals
- Career switchers
- Researchers
- AI enthusiasts
Featured Review
This AI & ML course simplified complex topics and gave me real skills I can use. The projects and examples made learning easy and practical - E. Mwaka, Software Engineer
The Artificial Intelligence & Machine Learning (AI & ML) course introduces learners to the concepts, tools, and techniques that allow computers to learn from data and make intelligent decisions.
This course explains how AI systems work, how machine learning models are built, and how these technologies are applied in real-world industries such as healthcare, finance, marketing, and automation.
By the end of this course, learners will be able to:
- Understand how AI and ML work
- Build simple machine learning models
- Analyze and prepare data
- Use Python for ML
- Apply AI to real-world problems
- Understand ethical AI practices
Curriculum
- 10 Sections
- 112 Lessons
- 4 Weeks
Expand all sectionsCollapse all sections
- Section 1: Introduction to ArtificiaI Intelligence & Machine Learning15
- 1.1Lesson 1: What is Artificial Intelligence?
- 1.2Lesson 2: History of Artificial Intelligence
- 1.3Lesson 3: Types of Artificial Intelligence
- 1.4Lesson 4: What is Machine Learning?
- 1.5Lesson 5: AI vs ML vs Data Science
- 1.6Lesson 6: Real-world Examples and Applications
- 1.7Lesson 7
- 1.8Lesson 8
- 1.9Lesson 9
- 1.10Lesson 10
- 1.11Lesson 11
- 1.12Lesson 12
- 1.13Lesson 13
- 1.14Lesson 14
- 1.15Quiz 140 Minutes12 Questions
- Section 2: Foundations of Machine Learning14
- 2.1Lesson 1: How machines learn
- 2.2Lesson 2: Training vs testing data
- 2.3Lesson 3: Features and labels
- 2.4Lesson 4: Model Lifecycle
- 2.5Lesson 5: Overfitting and underfitting
- 2.6Lesson 6:
- 2.7Lesson 7:
- 2.8Lesson 8:
- 2.9Lesson 9:
- 2.10Lesson 10:
- 2.11Lesson 25
- 2.12Lesson 26
- 2.13Lesson 27
- 2.14Quiz 230 Minutes11 Questions
- Section 3: Python for Artificial Intelligence & ML15
- 3.1Lesson 1: Python basics
- 3.2Lesson 2: Machine Learnin libraries
- 3.3Lesson 3: Loading datasets
- 3.4Lesson 4: Data preprocessing
- 3.5Lesson 5: Model training basics
- 3.6Lesson 33
- 3.7Lesson 34
- 3.8Lesson 35
- 3.9Lesson 36
- 3.10Lesson 37
- 3.11Lesson 38
- 3.12Lesson 39
- 3.13Lesson 40
- 3.14Lesson 41
- 3.15Quiz 340 Minutes10 Questions
- Section 4: Supervised Learning11
- Section 5: Unsupervised Learning14
- Section 6: Neural Networks & Deep Learning11
- Section 7: Model Evaluation & OptimizationEa videat maxima irridebat sequor coletur poterimus diligenter labor constanter drusum munus10
- Section 8: AI Applications11
- Section 9: Ethics & Responsible AI10
- Section 10: Case study Project11
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Requirements
- Basic computer skills
- Basic math knowledge
- Familiarity with data concepts
- Stable internet access
- A willingness to learn
- Basic Python knowledge
- Basic Statistics knowledge
Features
- Step-by-step lessons
- Hands-on projects
- Real-world case studies
- Practice datasets
- Quizzes and assessments
- Downloadable resources
- A case study project
- Beginner-friendly explanations
- Industry applications
Target audiences
- Beginners in tech
- Students
- IT professionals
- Engineers
- Entrepreneurs
- Business analysts
- Data professionals
- Career switchers
- Researchers
- AI enthusiasts
Lessons: