Machine Learning is a subset of 🤖 AI that involves building models to perform tasks without explicit programming by learning patterns from historical data. These models improve over time as they learn from data.

Notes

🤖 ML is a powerful tool that has transformed various industries by enabling computers to learn from data without being explicitly programmed. It involves the use of algorithms to analyze large datasets, identify patterns, and make predictions or decisions based on that analysis. This process continues as the models are exposed to more data, allowing them to improve their performance over time.

Take-aways

• 📌 Machine learning uses algorithms to analyze and make predictions based on data.
  • Algorithms can handle large datasets.
  • They adapt to new data, improving performance.
• 💡 Key techniques include Supervised Learning, Unsupervised Learning, and Semi-supervised Learning
  • Choosing the right technique depends on the data and goal.
• 🤖 ML is used in various fields such as image recognition, natural language processing, and recommendation systems.
• Statistics/Facts: According to a report by McKinsey & Company, machine learning could add about $14 trillion to global GDP by 2030.

Thoughts

1. 🏗️ Building Models: Algorithms learn from data to make predictions or decisions.
2. 🔄 Adaptation: Machine learning models improve over time with new data.
3. 🌐 Applications: Widely used in Image recognition, NLP, and Recommendation systems.

Related

1. Machine Learning: A Probabilistic Perspective
2. McKinsey Global Institute: Artificial Intelligence: The Next Era of Automation