Machine Learning

 Machine Learning 

Machine Learning is a type of technology that allows computers to learn from data and make decisions without being directly programmed.

ML teaches the system to think and understand like humans by learning from the data.

1. Data processing :Data processing is the step by step process of raw data and turning it into useful information. this is the most part of the ML and first basic step. EX, Handling missing values, Removing duplicates, Scaling/normalizing data.

2. Feature selection : choose the most popular feature from the data that actually help the model learn.

3. Model Learning : Machine learns patterns in the data using algorithm. 

4. Model Evaluation : Test how well the trained model works on new, unseen data.

1. Problem Statement : Clearly define the problem you want to solve.

2. Data Collection : Gather raw data from relevant sources.

3. Data Cleaning : Clean the data to remove error(delete), missing values, or duplicates.

4. Data Analysis & Exploration :Understand patterns, relationships, and distributions in the data.

5. Data Modeling : Use The ML Algorithms to Build a future Modeling.

6. Optimization & Deployment : Make the model better and use it in real life.

Types of Machine Learning :

 1.Supervised Machine Learning 

 2.Unsupervised Machine Learning 

 3.Semi-Supervised Machine Learning 

 4.Reinforcement Learning 

 

Supervised learning : Supervised learning uses labeled datasets where the input and output pairs are provided, and the model learns to predict outputs for new inputs.

Types of Supervised learning : 1. Classification  , 2. Regression

Unsupervised Learning : Unsupervised learning works on unlabeled data, identifying patterns or structures in the data.

Types of Unsupervised learning : 1. Clustering , 2. Dimensionality Reduction

 Applications of Machine Learning Across Fields : 

 1. Healthcare: Disease diagnosis using medical imaging (e.g., X-rays, MRIs). Drug discovery through pattern analysis. Personalized treatment recommendations. 

 2. Finance: Fraud detection using anomaly detection techniques. Credit scoring for loans and mortgages. Stock market prediction. 

 3. Retail and E-commerce: Recommendation systems. Demand forecasting to manage inventory. Dynamic pricing algorithms.

 4. Autonomous Vehicles: Lane detection and obstacle recognition. Path planning and navigation. 

 5. Natural Language Processing (NLP): Chatbots and virtual assistants (e.g., Alexa, Siri). Sentiment analysis on social media. Machine translation (e.g., Google Translate).

 6. Marketing: Customer segmentation for targeted advertising. Predicting churn rates to retain customers.