What is Machine Learning?
Table of Content:
Learning, like intelligence, covers such a broad range of processes that it is difficult to define precisely. A dictionary definition includes phrases such as “to gain knowledge, or understanding of, or skill in, by study, instruction, or experience,” and “modification of a behavioral tendency by experience”.
Machine learning is a discipline that deals with programming the systems so as to make them automatically learn and improve with experience. Here, learning implies recognizing and understanding the input data and taking informed decisions based on the supplied data. It is very difficult to consider all the decisions based on all possible inputs. To solve this problem, algorithms are developed that build knowledge from a specific data and past experience by applying the principles of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory.
Data science, machine learning and artificial intelligence are some of the top trending topics in the tech world today. Data mining and Bayesian analysis are trending and this is adding the demand for machine learning.
Machine Leaning Definition
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. ”
Tom Mitchell. Machine Learning 1997.
As described by Arthur Samuel, Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.
Purpose of Machine Learning
Machine learning can be seen as a branch of AI or Artificial Intelligence, since, the ability to change experience into expertise or to detect patterns in complex data is a mark of human or animal intelligence.
As a field of science, machine learning shares common concepts with other disciplines such as statistics, information theory, game theory, and optimization.
As a subfield of information technology, its objective is to program machines so that they will learn.
However, it is to be seen that, the purpose of machine learning is not building an automated duplication of intelligent behavior, but using the power of computers to complement and supplement human intelligence. For example, machine learning programs can scan and process huge databases detecting patterns that are beyond the scope of human perception.
Types of Learning
Learning is the process of converting experience into expertise or knowledge.
Learning can be broadly classified into four categories, as mentioned below, based on the nature of the learning data and interaction between the learner and the environment.
1. Supervised (inductive) learning
Training data includes desired outputs
2. Unsupervised learning
Training data does not include desired outputs
3. Semi-supervised learning
Training data includes a few desired outputs
4. Reinforcement learning
Rewards from sequence of actions
Applications of Machine Learning Algorithms
The developed machine learning algorithms are used in various applications such as ?
- Vision processing
- Language processing
- Forecasting things like stock market trends, weather
- Pattern recognition
- Games
- Data mining
- Expert systems
- Robotics
- Spam filtering
- Credit card fraud detection
- Digit recognition on checks, zip codes
- Detecting faces in images
- MRI image analysis
- Recommendation system
- Search engines
- Handwriting recognition
- Scene classification
Steps Involved in Machine Learning
A machine learning project involves the following steps –
- Defining a Problem
- Preparing Data
- Evaluating Algorithms
- Improving Results
- Presenting Results
The best way to get started using Python for machine learning is to work through a project end-to-end and cover the key steps like loading data, summarizing data, evaluating algorithms and making some predictions. This gives you a replicable method that can be used dataset after dataset. You can also add further data and improve the results.
Component of machine algorithm
Every machine learning algorithm has three components:
- Representation
- Evaluation
- Optimization
Representation
- Decision trees
- Sets of rules / Logic programs
- Instances
- Graphical models (Bayes/Markov nets)
- Neural networks
- Support vector machines
- Model ensembles
Evaluation
- Accuracy
- Precision and recall
- Squared error
- Likelihood
- Posterior probability
- Cost/Utility
- Margin
- Entropy
- K-L divergence
Optimization
- Combinatorial optimization
- g.: Greedy search
- Convex optimization
- g.: Gradient descent
- Constrained optimization
- g.: Linear programming
Why Al & ML
- Al is new and upcoming program in the area of computer science that is rapidly expanding its boundaries to variety of fields like Healthcare, Security, Entertainment, Education, Autonomous transporttion, Intelligent robots, space exploration, speech processing, Stock market etc.
- Al with Robotics process of automation could help to free up sales team a for more meaningful Conversation with customers.
- Improved training in Al will help human identity and rectify problems that automated tools may miss.
- Except more IT executives to push for new results driven contracts with Al consultancies, system integrators and vendors.
- Improvements in semi and self supervised learning are helping companies keep the amount of manually labeled data to a minimum.
- Tuning neural network model, Al will become cheaper and new solution will take less time to reach market.
- Usage in financial, retail or healthcare industries and for clearly defined repetitive tasks.
- New model will facilitate production scale implementation of Al Into creative industries.
- Training data Scientist with cross domain skills such as natural language processing and machine vision techniques.
- Multimodel techniques such as machine vision and optical character recognition could optimize the presentation of results ,improving medical Diagnosis.
- Al and ML models that run on hardware devices such as the microcontroller used for powering cars, Refrigerators and utility meters.
- Localized analysis of simple voice and gesture commands, common sound such as gunshot or baby crying, asset location and orientation, Environmental conditions and vital signs.
- Financial services fraud detection, retail purchase predictions and online customer support interactions.