Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
What is Machine Learning
simple definition:- Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
History of Machine Learning
Machine Learning Methods
Supervised machine learning
Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values.
The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
unsupervised machine learning
Semi-supervised machine learning
Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy.
Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
Reinforcement machine learning
Mchine Learning Algorithm
Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.)
Types of machine Learning Algorithm are listed below:
- Linear Regression
- Logistic Regression
- Decision Tree
- Naive Bayes
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boosting algorithm
Applications of Machine Learning
1. Image Recognition:
2. Speech Recognition
3. Traffic prediction:
4. Product recommendations:
5. Self-driving cars:
6. Email Spam and Malware Filtering:
- Content Filter
- Header filter
- General blacklists filter
- Rules-based filters
- Permission filters