Machine learning algorithms are used in a wide variety of applications, such as email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics. for more see – Wikipedia
To better understand the differences, interactions and similarities with neural network here is a little side to side info about both of them to clear things up.
Probability, Programming Skills, Data structures and Algorithms, Knowledge about machine learning frameworks, Big Data
- Health care
- Online recommendations
- Tracking price changes
- Better costumer service and delivery
Siri, Google Maps, Search engines etc.
Probability and statistics, Data modeling, Programming skills, Data structures and algorithms, Mathematics, Linear algebra and graph theory
- Health Care
- Machine learning
- Artificial Intelligence
- Stock Exchange Prediction
Image Recognition, Image Compression and search, Search engines, self driving etc.
The Process of machine learning and algorithms
Gathering date from various sources.
Model Building – Selecting the right ML algorithm
Gaining Insights from the model’s results
Data Visualization – Transforming results into visual graphs.
|The”Best fit” line through all data points. Predictions are numerical.||Easy to understand – you clearly see what the biggest drivers of the model are.||
|The adaptation of linear regression to problems of classification ( e.g. m yes/no questions, groups etc.||Easy to understand.||
|A series of yes/no rules based on the features, forming a tree, to match all possible outcomes of a decision.||Easy to understand.||
|Takes advantages of many decision tree, with rules created from sub-samples of features, each tree is weaker than a full decision tree, but by combining them we get better overall performance.||a sort of “wisdom of the crowd”. Tends to result in very high quality models. Fast to train||
|Uses even weker decision trees, that are increasingly focused on”Hard” examples.||High – Performing.||
|Interconnected <neurons: that pas messages to each other Deep learning uses several layers of neural networks stacked on top of each other.||Can handle extremely complex tasks- no other algorithm comes close in image recognision.||