Introduction to Machine Learning Basics

Machine Learning for Beginners

• Machine learning has diverse applications across various fields.
• Machine learning algorithms can be categorized as supervised or unsupervised.
• Supervised learning utilizes labeled data, while unsupervised learning uses unlabeled data.
• Supervised learning includes classification (an example), and unsupervised includes clustering (an example).
• Classification involves training a model on a training set and evaluating performance on a test set.
• The number of features directly correlates with the data's dimensionality.
• Euclidean distance is utilized to calculate distances between data points.
• K-means is an iterative clustering algorithm, grouping data points based on proximity to cluster centers.

What is Machine Learning?

• Machine learning involves programming machines to mimic human learning processes.
• This involves providing examples for the machine to analyze and develop the ability to predict or categorize new data.

Examples of Machine Learning

• Surgical robots use a process where surgeons provide detail on a procedure, a programmer turns this into a learning algorithm, example procedures are fed to the machine, and programmers test extensively to ensure 100% accuracy.
• Drones employ machine learning to automate flight using autopilot systems. These systems process sensor data, execute flight plans, and make autonomous decisions through sophisticated onboard computers.
• Text mining and NLP extract vital information from different text sources (e.g., news), integrate them, and analyze trends.
• Social networks apply machine learning to personalize feeds, product suggestions, and friend recommendations.
• Bioinformatics uses human genome data to identify biological details and predict diseases.
• Financial systems predict profitability or loss using historical financial data.
• Weather forecasting uses historical data to predict future weather patterns.

Types of Machine Learning: Supervised Learning

• Supervised learning is a machine learning technique that differentiates multiple classes or groups from data with two steps
• Training step: Thousands of examples are fed to a model differentiating multiple groups or classes.
• Testing Step: The model is evaluated against a separate dataset to assess its accuracy.

Types of Machine Learning: Unsupervised Learning

• Unsupervised learning focuses on discovering patterns and structures within datasets without predefined labels or categories.
• Decisions are made based on observed patterns without explicit training.
• Clustering is a key unsupervised technique which groups similar data points based on defined criteria.

Supervised vs Unsupervised Learning

• Supervised learning requires labeled data.
• Unsupervised learning works with unlabeled data, aiming to discover patterns.

Features or Dimensions

• Features describe objects in data sets.
• Examples include color, texture, shape, size, weight.
• Relevant features selection can optimize algorithms.
• The number of features equals the number of dimensions.

Distances Between Objects

• Euclidean distance calculates the distances between two objects.
• It's the square root of the sum of squared differences of their corresponding features.

K-means Clustering

• K-means is a data clustering technique.
• It groups data objects based on distance from cluster centers.
• It iteratively refines clusters using feature calculations for better clustering.

Summary

• Machine learning encompasses supervised and unsupervised types.
• Supervised learning uses labeled data, and unsupervised does not.
• Classification(supervised), clustering(unsupervised) are examples of these types of machine learning.
• The number of features corresponds to data dimensionality.
• Euclidean distance measures distance between data points
• K-means is a common clustering algorithm, assigning data points to clusters based on distance from centers.