Machine Learning (ML) is one of the most exciting fields in technology today. It allows computers to learn from data and make decisions without being explicitly programmed. ML is closely related to Artificial Intelligence (AI), as it provides the techniques that enable machines to recognise patterns, make predictions, and improve over time. From personalised recommendations on streaming platforms to fraud detection in banking, machine learning powers many systems we use every day.
If you are someone beginning your journey for Machine Learning, this series will help you will build a strong foundation in machine learning concepts and also explore tools that make it easier to experiment, even without programming skills.
1. Machine Learning Fundamentals
- Relationship Between Machine Learning and Artificial Intelligence
- Brief History of Machine Learning
- How Machine Learning Works
- Types of Data in Machine Learning
- Importance of Data Quality
- Real-World Applications of Machine Learning
2. Supervised vs Unsupervised Learning
- What is Supervised Learning?
- Algorithms in Supervised Learning
- What is Unsupervised Learning?
- Algorithms in Unsupervised Learning
3. Reinforcement Based Learning
- What is Reinforcement Based Learning?
- Environment-Agent Interaction
- Rewards, Punishments, and Learning Loops
- Real-World Examples of Reinforcement Based Learning
- Why is Reinforcement Based Learning Important?
4. No-Code tools for Machine Learning
- What are No-Code ML Tools?
- Popular No-Code ML Tools
- Orange – Visual Programming for ML Workflows
- Teachable Machine (Google)
- Scratch with AI Extensions
- MIT App Inventor (with AI Features)
- Runway ML