Tarunika Nextgen Notes


 R K RAMYA TARUNIKA KARTHICK

2YEAR CSE ( AI&ML )

MANGAYARKARASI COLLEGE OF ENGINEERING 

D.O.B : 28-07-2007







🌟 Introduction to Machine Learning: 

What is Machine Learning?

Machine Learning (ML) is a branch of Artificial Intelligence (AI) that enables computers to learn patterns from data and make predictions or decisions without being explicitly programmed. Instead of following strict rules, ML systems improve over time as they process more information.

Why is Machine Learning Important?

  • Data is Everywhere – From social media to healthcare, we generate huge amounts of data daily.

  • Automation – ML powers automation in industries like banking, e-commerce, and manufacturing.

  • Smarter Systems – Virtual assistants (like Siri, Alexa), recommendation engines (Netflix, Amazon), and even self-driving cars rely on ML.

Types of Machine Learning

  1. Supervised Learning

    • Learns from labeled data (input → output).

    • Example: Predicting house prices based on features like size, location, and rooms.



  1. Unsupervised Learning

    • Works with unlabeled data to find patterns or clusters.

    • Example: Customer segmentation in marketing.

  2. Reinforcement Learning

    • An agent learns by interacting with an environment and receiving rewards/punishments.

    • Example: Training robots or AI to play games.

Real-World Applications

  • Recommendation Systems – Netflix suggesting movies you might like.

  • Healthcare – Detecting diseases from medical images.

  • E-commerce – Personalized shopping experiences.

  • Autonomous Cars – Learning how to drive safely on roads.

  • Spam Detection – Filtering unwanted emails.

Getting Started with Machine Learning

If you’re a beginner, here’s a simple roadmap:

  1. Learn Python basics (most popular ML language).

  2. Understand mathematics: Linear Algebra, Probability, Statistics.

  3. Get familiar with libraries: NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch.

  4. Start small projects like:

    • Predicting student scores.

    • Classifying spam vs non-spam emails.

    • Handwritten digit recognition (MNIST dataset).



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