Lecture: Introduction to Machine Learning

Speaker: Luv Aggarwal, Data Platform Solution Engineer at IBM

Key Topics Covered:

1. Definitions and Distinctions

   • Artificial Intelligence (AI): Leveraging machines to mimic human problem-solving and decision-making.
   • Machine Learning (ML): A subset of AI; uses self-learning algorithms to predict outcomes based on data.
   • Deep Learning: A subset of ML; minimizes human intervention; allows for using very large datasets by automating feature extraction.

2. Types of Machine Learning

   • Supervised Learning
     • Uses labeled datasets to train algorithms for classification or prediction.
     • **Classification Models: **
       • Example: Customer retention—identifying potential churners to take action for retention.
     • **Regression Models: **
       • Example: Airline ticket pricing—using factors like days before departure, day of the week, etc. to set prices.
   • Unsupervised Learning
     • Uses algorithms to analyze and cluster unlabeled datasets.
     • Clustering: Grouping data based on similarities.
       • Example: Customer segmentation—understanding customer types for targeted marketing.
     • Dimensionality Reduction: Reducing input variables to prevent redundancy.
   • Reinforcement Learning
     • Semi-supervised learning where an agent/system takes actions and learns from rewards or punishments.
     • **Example: **Self-driving cars—learning to follow traffic rules and avoid collisions.

Additional Resources:

• Check the provided links in the description for more information on machine learning algorithms and their applications in data science.
• IBM Cloud Labs for free browser-based interactive Kubernetes labs.

Conclusion:

• Encouragement to delve deeper into specific aspects of machine learning that interest you.
• Invitation to like, subscribe, and ask questions for further engagement.