Introduction to Machine Learning

Definition :

Machine Learning is a subset of Artificial Intelligence that focuses on the development of algorithms that allows computers to learn from and make predictions or decisions based on data.

Key Idea :

Instead of explicitly programming rules (as in expert systems), ML algorithms identify patterns in data and improve performance over time.

In short, Machine Learning is similar to a mathematical function, it takes input data, predicts an output and provides a response.

Types of Machine Learning :

1. Supervised Learning : The model is trained on a labeled dataset where the desired output is known.
2. Unsupervised Learning : The model works on an unlabeled dataset, trying to identify hidden patterns.
3. Reinforcement Learning : The model learns by interacting with an environment and receiving awards and penalties

How does Machine Learning works?

fig. 1 : Steps involved in Machine Learning

What are different Machine Learning Problems?

1. Regression : Predict Continuous values e.g. predict stock prices. These regression problems can be further divided into

   – Simple Linear Regression

   – Multiple Linear Regression

   – Non Linear Regression

2. Classification : Classify the data points based on the features of the classes in the data points. Some classifications techniques are.

   – KNN

   – Naive Bayes

   – Logistic Regression

3. Clustering : Partitioning the data into clusters based on feature similarity.

   – K-Means clustering

   – Hierarchical clustering

Calculus : Helps in optimizing algorithms by finding maximum and minimum points (e.g. cost function optimization in gradient descent, backpropagation in Neural Networks).

Probability and Statistics : Machine Learning often makes predictions under uncertainty and probability theory provides the mathematical formula (Algorithms like Naive Bayes, Bayesian Network, Markov Decision Process).

Linear Algebra : Data is often represented as matrices or vectors (e.g. dot product, matrix multiplication, eigen values, eigen vectors are fundamentals in algorithms like PCA).

Reference Books :

1. Pattern Recognition and Machine Learning

   Book by Christopher Bishop

2. Understanding Machine Learning: From Theory to Algorithms

   Textbook by Shai Ben-David and Shai Shalev-Shwartz

3. Deep Learning

   Textbook by Aaron Courville, Ian Goodfellow, and Yoshua Bengio

4. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

   Book by Aurelien Geron