This document provides an overview of machine learning including definitions, common algorithms, applications, and relationships to other fields like artificial intelligence and deep learning. Machine learning allows computer applications to become more accurate at predicting outcomes without being explicitly programmed by using algorithms that identify patterns in data to build predictive models. It relies heavily on data, with more data enabling more effective machine learning. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive bayes, k-nearest neighbors, k-means, random forest, and dimensionality reduction. Applications span advertising, facial recognition, product recommendations, autonomous vehicles, fraud detection, and more. The document also discusses deep learning as a subset of machine learning and lists some famous companies

2. Machine Learning
● Machine Learning is a type of Artificial
Intelligence that allows computer
application become more accurate in
predicting outcome without being
explicitly performed i.e automcatically.
● It uses algorithm that pick-up data to
build predictive models that choose
where to look insight.
● Machine learning relies on Data; the
more the data, more effective machine
learning is.

3. Machine Learning Algorithms
Broadly, there are 3 types of Machine
Leaning Algorithm:
● Supervised Learning
● Unsupervised Learning
● Reinforcement Learning

4. Simple Machine Learning Logic
Traditional Programming
Machine Learning
Computer
Output
Data
Program
Computer
Output
Program
Data

5. Machine Learning Algorithms Contd..

6. List of Common Machine Learning Algorithms
● Linear Regression
● Logistic Regression
● Decision Tree
● Support Vector Machine (SVM)
● Naive Bayes
● K – Nearest Neighbour
● K – Means
● Random Forest
● Dimonsionality Reduction Algorithm
● Gradient Boosting Algorithm
Here is the list of commonly used machine learning algorithms. These algorithms
can be applied to almost any data problem

7. Application of Machine Learning
● Advertising & Business
Intelligence
● Facial Recognition
● Optical Character Recognition
● Product Recommendation
Engines
● Autonomous Vehicles
● Surveillance System
● Filtering Algorithms
● Online Fraud Detection
● Email Spam and Malware Filtering
● Network Intrusion Detection
● Prediction of Equipment Failures

8. Relation of Machine Learning
The following pictures makes it clear understanding about relation of Machine Learning,
Artificial Intelligence, Knowledge Discovery, Data Mining, Database etc

9. Relation of Machine Learning with Deep Learning
In practical terms, deep learning is just a subset of machine learning. It
technically is machine learning and functions in a similar way, but its capabilities
are different.

10. Famous Companies Using Machine Learning

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15. Table of Contents
● SECTION 1 : Introduction to Machine Learning
● SECTION 2 : Supervised Learning – Part 1
● SECTION 3 : unsupervised Learning
● SECTION 4 : Neural Networks
● SECTION 5 : Real World Machine Learning
● SECTION 6 : Final Project

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17. Table of Contents
● SECTION 1 : Stock Market Clustering
● SECTION 2 : Breast Cancer Detection
● SECTION 3 : Board Game Review Prediction
● SECTION 4 : Credit Card Fraud Detection
● SECTION 5 : Diabetes Onset Detection
● SECTION 6 : DNA Classification – The Dataset

18. Table of Contents Contd..
● SECTION 7 : Intro to Natural Language Processing
● SECTION 8 : Object Recognition
● SECTION 9 : Image Super Resolution
● SECTION 10 : Text Classification
● SECTION 11 : KMeans
● SECTION 12 : PCA

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