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AI & TechnologyIntermediate
Level 2: AI Practitioner
Work with data and build your first machine-learning models.
Hands-on, bilingual AI: data, the core ideas of machine learning, supervised and unsupervised techniques, the practitioner toolkit, and no-code automation.
6 Weeks
What you'll learn
- Collect, clean and visualise data
- Explain supervised vs unsupervised learning
- Build classification and clustering models
- Use Python, Colab, Pandas, NumPy
- Automate workflows with AI
Lessons
35 video lessons
- 16.1 What is Data?
- 26.2 Types of Data
- 36.3 Data Collection
- 46.4 Data Cleaning
- 56.5 Data Visualization
- 66.6 Datasets for AI
- 77.1 What is Machine Learning?
- 87.2 Types of Machine Learning
- 97.3 Training vs Testing Data
- 107.4 Features and Labels
- 117.5 Model Evaluation Basics
- 128.1 Classification
- 138.2 Regression
- 148.3 Decision Trees
- 158.4 Random Forests
- 168.5 K-Nearest Neighbors
- 178.6 Supervised Learning Practical Examples
- 189.1 Clustering
- 199.2 K-Means
- 209.3 Hierarchical Clustering
- 219.4 Dimensionality Reduction
- 229.5 Customer Segmentation Examples
- 2310.1 Python Basics
- 2410.2 Jupyter Notebook
- 2510.3 Google Colab
- 2610.4 Pandas
- 2710.5 NumPy
- 2810.6 Matplotlib
- 2911.1 AI Workflows
- 3011.2 AI Agents
- 3111.3 No-Code Automation
- 3211.4 Zapier
- 3311.5 Make.com
- 3411.6 Automation Business Use Cases
- 35Intermediate Project
Practice with AI Companion
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