What is Machine Learning:
Machine learning is a method of data analysis that
automates analytical model building.
● Using algorithms that iteratively learn from data, machine
learning allows computers to find hidden insights without
being explicitly programmed where to look. Machine learning is about teaching computers how to learn from data to make decisions or predictions. For true machine learning, the computer must be able to learn to identify patterns without being explicitly programmed to. 7dijits.blogspot.com
It sits at the intersection of statistics and computer science, yet it can wear many different masks. Machen is in many categories and can be practiced in many fields like Data Science, Big Data, Artificial Intelligence, Predictive Analytics, Computational Statistics, Data Mining, and many more.
While machine learning is related to those fields, it shouldn't be tagged together with them. For example, machine learning is a tool for data science. It's also another use of infrastructure that can handle big data analysis.
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What is Machine Learning used for?
● Fraud detection.
● Web search results.
● Real-time ads on web pages
● Credit scoring and next-best offers.
● Prediction of equipment failures.
● New pricing models.
● Network intrusion detection. ● Recommendation Engines
● Customer Segmentation
● Text Sentiment Analysis
● Predicting Customer Churn
● Pattern and image recognition.
● Email spam filtering.
● Financial Modeling
Machine Learning Process:
*Test Data *Data Acquisition *Data Cleaning * *Mouldel Training and Building *Model Testing *Model Deployment. These are the machine learning process we can make use of in the process of learning. Machine learning is applied in almost all the fields in the world today like; Financial Services, Government Organisations, Health Care, Oil & Gas, Retail, And Transportation. Machine learning is categorized into three types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
Neural Network:
We can not talk about machine learning without a neural network, What is a neural network? and How does it work in with machine learning? Neural networks are modeled after biological neural
networks and attempt to allow computers to learn in a
similar manner to humans - reinforcement learning.
Use cases:
● Pattern Recognition
● Time Series Predictions
● Signal Processing
● Anomaly Detection
● Contro
Machine Learning And Artificial Intelligent
In a real sense, artificial intelligence (AI) is the wild science of mimicking human abilities, while machine learning is a specific subset of AI that trains a machine how to learn. understanding the machine learning life circle will help you know what AI is all about.
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What are the Differences Between Data Mining, Machine Learning, and Deep Learning?
Deep learning is a machine learning subset that trains a computer to perform human-like tasks, such as speech recognition, image identification, and prediction making. It improves the ability to classify, recognize, detect and describe using data. The current interest in deep learning is due, in part, to the buzz surrounding artificial intelligence (AI). Deep learning has a lot of applications and opportunities, A lot of computational power is needed to solve deep-learning problems because of the iterative nature of deep-learning algorithms, their complexity as the number of layers increases, and the large volumes of data needed to train the networks. techtarget.com
What is data mining?
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Data mining is the process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis. Data mining techniques and tools enable enterprises to (Predict Future Trends) and make more-informed business decisions. Data mining is a key part of (data analytics) overall and one of the core disciplines in (data science) which uses advanced analytics techniques to find useful information in data sets. At a more granular level, data mining is a step in the knowledge discovery in databases (KDD) process, a data science methodology for gathering, processing, and analyzing data. Data mining and KDD are sometimes referred to interchangeably, but they're more commonly seen as distinct things.