Machine learning program teaches computers to do what comes naturally to humans and animals learn from experience.ML algorithms
use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms
adaptively improve their performance as the number of samples available for learning increases
Training a Model to Classify Physical Activities
Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed.
Features of Machine Learning:
- Machine learning uses data to detect various patterns in a given dataset.
- It can learn from past data and improve automatically.
- It is a data-driven technology.
- Machine learning is much similar to data mining as it also deals with the huge amount of the data.
Machine learning explores the construction and study of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions
What are the different Algorithm techniques in ML?
The different types of techniques in Machine Learning are
a) Supervised Learning
b) Unsupervised Learning
c) Semi-supervised Learning
d) Reinforcement Learning
e) Transduction
f) Learning to Learn
What is PAC Learning?
PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to analyze learning algorithms and their statistical efficiency.
Mention the difference between Data Mining and Machine learning?
Machine learning relates to the study, design, and development of the algorithms that give computers the capability to learn without being explicitly programmed. While data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown
interesting patterns. During this processing machine, learning algorithms are used.
Classification of Machine Learning
- Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.
- Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end.
- Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent.
Applications for ML include:
| Adaptive websites | Affective computing | Bioinformatics | Brain-machine-interfaces | Cheminformatics |
| Classifying DNA Sequences | Computational advertising | Computational finance | Computer vision, including object recognition | Detecting credit card fraud |
| Game playing | Information retrieval | Internet fraud detection | – | – |
Software
Software suites containing a variety of machine learning algorithms include the following:
| Dlib | KLKI | Encog |
| H2O | Mahout | mlpy |
| MLPACK | MOA(Massive Online Analysis | ND4J with Deeplearning4j |
| OpenCV | OpenNMS | Orange |
| R | Scikit-learn | Shogun |
| Torch Machine learning | – | – |
Machine Learning: How it works
Machine Learning works in a similar way to human learning. For example, if a child is shown images with specific objects on them, they can learn to identify and differentiate between them. Machine Learning works in the same way: Through data input and certain commands, the computer is enabled to “learn” to identify certain objects (persons, objects, etc.) and to distinguish between them. For this purpose, the software is supplied with data and trained. For instance, the programmer can tell the system that a particular object is a human being (=”human”) and another object is not a human being (=” no human”).
Advantages of Machine Learning
So, let’s have a look at the advantages of Machine Learning.
1. Automation of Everything
2. Wide Range of Applications
3. Scope of Improvement
4. Efficient Handling of Data
5. Best for Education and Online Shopping
6. Easily Identifies Trends and Patterns
7. No Human Intervention Needed (Automation)
8. Continuous Improvement
9. Handling Multi-Dimensional and Multi-Variety Data
10. Wide Applications.
Machine Learning is Ever Improving
Book Pdf In Machine Learning