THE PREREQUISITES:
When you started with machine learning you must familiar with these concepts like statistics, linear algebra, calculus, probability, data mining and programming language(R,Python).
What is Machine learning?
Machine learning is a subset of artificial intelligence and it enables statistical techniques that machines to improve its task with experience
INTRODUCTION TO MACHINE LEARNING:
Machine learning is a subfield of artificial intelligence(AI).Which enables computer to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information.
Machine learning used for various task such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system etc.
Machine learning algorithms are able to detect patterns in data and learn from them, in order to make their own predictions.
Arthur Samuel an American leader in the field of computer gaming and artificial intelligence, the term machine learning coined in the year 1959 at IBM.
Before 40 to 50 years machine learning was science fiction, but today it is a part of our daily life.
Machine learning making our daily life easy from self driving cars to virtual assistance “alexa”.
*Different authors define different definition about machine learning
Standard Definition of ML :
“A computer program is said to learn from experience E with respect to some of class of task T and performance measure P, if its performance at task in T, as measured by P, improves with experience E.”
-E.”-Tom M. Mitchell(1997)
⦁ “Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed.” —Arthur Samuel, 1959
⦁ When machine learning is seen as a process, the following definition is insightful: “Machine learning is the process by which a computer can work more accurately as it collects and learns from the data it is given.” —Mike Roberts
⦁ In the broader field of science, machine learning is a subfield of artificial intelligence and is closely related to applied mathematics and statistics.
MACHINE LEARNING CLASSIFICATION
Machine learning broadly classified into four types:
⦁ Supervised learning
⦁ Unsupervised learning
⦁ Semi supervised learning
⦁ Reinforcement learning
1.Supervised learning:
Supervised learning is used to predict a certain outcome from a given input, and we have examples of input/ output , yes/no pairs.
Example: predicting house prices , identifying mail is spam or not.
⦁ User provides pair of inputs and desired output and the algorithm finds a way to give output for given input.
There are two types of supervised machine learning they are:
⦁ Classification
⦁ Regression
* Classification is a type of supervised machine learning.
* In classification have to predict class labels, which is from a predefined list of possibalites.
Example : identifying mail is spam or not.
Classification algorithms are:
⦁ Decision Tree
⦁ K-Nearest Neighbors
⦁ Random Forest
⦁ Support Vector Machine
⦁ Naïve Bayes
⦁ Logistic Regression
Regression is one of the supervised machine learning type.
Regression algorithm is to plot a best fit line or a curve between the data.
Example: predicting house prices.
Regression algorithms are:
⦁ Linear Regression
⦁ Neural Networks
⦁ Ordinary Least Squares Regression
⦁ LOESS(Local Regression)
2. Unsupervised Learning: Unsupervised learning is machine learning type. Unsupervised learning finds hidden patterns or grouping in data.
Example: Set of blog post.
In unsupervised learning classified into two types:
⦁ Cluster analysis
⦁ K-Means Clustering
⦁ Hierarchical Clustering
⦁ Dimension Reduction
⦁ Principal Component Analysis (PCA)
⦁ Linear Discriminant Analysis (LDA)
3. Semi Supervised Learning: Combines with a small amount of labelled data with large amount unlabeled data.
Example: Speech recognition, Text to speech, Voice activity detection.
4. Reinforcement Learning: Reinforcement learning is learn from its experience. Its takes the action to maximize reward in particular situation.
Example: Self Driving Cars , Computer Learning Chess.
Applications of Machine Learning:
⦁ Image Recognition
⦁ Speech Recognition
⦁ Self – Driving Cars
⦁ Traffic Prediction
⦁ Product Recommendation
⦁ Stock Market Trading
⦁ Online Fraud Detection
⦁ Virtual Personal Assistance
⦁ Medical Diagnosis
