Introduction to Machine Learning.

Machine learning tries to teach a computer a program and then expecting it to learn and create its own programs. It is like repeatedly teaching as task for the computer in a way that it becomes more efficient in performing the task. For example, if you enter a sales data set from 2000 to 2022 and perform tasks on it, the computer should be equipped with the ability to undern stand the underlying patterns and be able to predict for future.
We can see machine learning being used in different areas. Every e-commerce company uses machine learning like average order value, average time spent, recommendations.It is also used for facial recognitions, self-driving cars, Grammarly, mail classification as spams, product bundling, social media analysis, default tax payments, loan prediction model and time series forecasting.
Machine Learning Paradigms:
1. Supervised Learning:
Concept: You have labeled data, where each data point has a corresponding target value you want to predict. Think of it like learning with a teacher providing answers.
Training: The model learns the relationship between input data and target values by analyzing labeled examples. Imagine fitting a line through data points to capture the underlying pattern.
Prediction: Once trained, the model can predict target values for new, unseen data based on the learned relationship. Think of using the fitted line to predict values beyond the training data points.
Common tasks: Regression (predicting continuous values like house prices), classification (categorizing data points like spam emails), and decision trees (building tree-like structures for predictions).
2. Unsupervised Learning:
Concept: You have unlabeled data, where no specific target value is provided. Think of exploring and making sense of unknown territory without a guide.
Analysis: The goal is to uncover hidden patterns or structures within the data. Imagine grouping similar data points or identifying important features.
Applications: Clustering (grouping similar data points), dimensionality reduction (compressing data while retaining key information), anomaly detection (finding unusual data points), and recommendation systems (suggesting relevant items based on user preferences).
3. Reinforcement Learning:
Concept: Imagine teaching a baby to walk through trial and error, with rewards for taking successful steps. Reinforcement learning works similarly, where an agent interacts with an environment, takes actions, and receives rewards for good choices.
Learning: The agent learns through trial and error, aiming to maximize long-term rewards. Think of exploring different paths and learning from successes and failures.
Applications: Robotics (training robots to navigate and perform tasks), game playing (training AI agents to play complex games), and resource management (optimizing decisions in complex systems).Reinforcement learning often involves reward functions designed to guide the agent towards desired behavior.Q-learning is a popular technique where the agent learns the value of taking specific actions in different states.