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Machine Learning Overview

• Machine Learning (ML) is a foundational technology in information technology over the past two decades, becoming an integral part of daily life.
• The increasing availability of data suggests that intelligent data analysis will become more prevalent and crucial for technological advancement.

What is Machine Learning?

• Machine Learning (ML) or ML is a technical term for using various techniques and tools that empower computers and intelligent machines to learn and adapt autonomously.
• It's an application of artificial intelligence that allows computers to learn from experience and improve their performance through repetition without explicit programming.
• The focus of machine learning is on developing computer programs capable of handling massive datasets to learn and improve themselves.

History of Machine Learning

• The field of machine learning emerged when computer scientists began questioning the ability of mimicking human thinking and learning through data and experience.
• They sought to equip machines with the ability to learn, understand, and draw inferences without explicit instructions.
• This differed from traditional programming, where humans define rules for the computer and specify the data to be processed.

Machine Learning vs. Traditional Programming

• Traditional programming involves humans inputting rules and specific data, resulting in predefined outputs.
• Machine learning involves inputting data and desired outputs to train the machine. The machine then generates its own rules to arrive at the output.

How Machine Learning Works

• In the machine learning model, humans input data and expected outputs to the computer, and the machine outputs the rules.
• These rules can be applied to new data to predict outputs.
• Machine learning systems learn instead of being explicitly programmed, learning statistical patterns from provided data sets linking input to output, which enables automation.

Data and Machine Learning

• Data is the fuel for machine learning algorithms.
• Algorithms like deep learning require increased and specialized datasets for improved performance.

Data Types in Machine Learning

• Data in machine learning is categorized into labeled and unlabeled data sets.
• Labeled data includes a classification that is associated with the input variables.
• Unlabeled data does not include a classification.

Types of Machine Learning

• Supervised Learning: Algorithms are trained on labeled data and expected output so they learn how to generate a suitable output from unseen input data.
• Unsupervised Learning: Algorithms learn from unlabeled data, identifying patterns and relationships without explicit guidance.
• Semi-supervised Learning: A combination of supervised and unsupervised learning, where a small amount of labeled data is used alongside a large amount of unlabeled data.
• Reinforcement Learning: Algorithms learn by interacting with an environment, receiving rewards or penalties for their actions, and optimizing their behavior to maximize rewards.

Machine Learning Applications

• Machine learning has various applications across fields, such as personalized content recommendations in social media, customer service through chatbots, email spam filtering, and medical diagnostics.
• Many applications in different industries are using machine learning, including finance, retail, e-commerce, health care, and many more.

Probabilistic Modeling, Kernel Methods, Decision Trees, Random Forests, Neural Networks, Deep Learning

• Probabilistic modeling employs statistical principles for data analysis and is a foundational approach for machine learning.
• Kernel methods emerged as a response to the rise of neural networks, focusing on classification and support vector machines.
• Decision trees and random forests encompass methods for prediction and classification with a hierarchical structure resembling flowcharts. Neural networks are a sophisticated approach that uses interlinked nodes. Deep learning uses deep layers of interconnected nodes to facilitate more complex feature extraction and recognition of patterns.

Computer Vision

• Computer vision is a practical application of deep learning that enables computers to "see" images and videos.
• This technology assists in applications like self-driving cars, image recognition, and video surveillance.

Relationship Between Machine Learning, Artificial Intelligence and Data Science

• Machine learning is a subset of artificial intelligence, designed to empower machines to mimic human cognitive functions.
• Data science is the overarching field dealing with data; machine learning is a tool within data science.

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