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Introduction
Machine learning is the branch of artificial intelligence (AI). It uses statistical models and algorithms to get computers to imitate the way humans learn and improve their learning automatically.
Machine learning is one of the most popular and the toughest subjects in programming.
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What is Machine Learning?
Machine learning and data science is a field of computer science where various mathematical statistic techniques are used to let the computer learn on its own through analysis of data without actual programming. It is the area of Artificial Intelligence where
machine learning is often used and focus on development of computer applications accessing the data and using that data for learning without any human intervention.
Machine learning will use algorithms that will receive data as an input and use statistical
techniques to anticipate the output while keeping on updating the output with the change in data. The process that is used in machine learning is alike to that is data mining and predictive models. In both these processes, search the data for pattern and accordingly adjust the program actions. It helps the data science industires to take right
business decisions by analyzing huge amount of data. In machine learning there are different fields which used to predict the machine learning algorighms. There include – health care, fraud detection, financial services, personalized recommendation, etc. The process of machine learning includes:
Select the right machine learning algorithm for usage
Develop an analytical model that is in accordance with the selected algorithm
Train the model on the data sets prepared for testing
Run the model to generate findings
Type Of Machine Learning Algorithms:
Supervised Learning: Supervised learning is one of the most famous kinds of machine learning. It can be easily implemented and comprehended as it is task-oriented. It focuses on a single task which further feeds different examples, some application of this category of machine learning are:
Ad popularity
Spam classification
You can use this type of learning, if you have the data in your hand to predict the output. There are two types of methods that are used to develop predictive models. There include:
Classification techniques : This will predict direct responses. For instance, this will get to know whether or not the email is real or a spam or tumor is benign or cancerous. This is used for medical imaging, credit scoring, speech recognition, etc. You can use this technique, if you can tag, categorize or separate the data into groups or classes:
Algorithms used to perform classification include:
Super Vector Machine (SVM)
K-nearest neighbor
Neural networks
Logical regression
Bagged decision tree
Regression technique: Under regression technique, the focus in on generating continuous responses like change in temperature, power fluctuations with demand.
The key regression algorithm techniques that are used include:
Linear model
Non-linear model
Regularization
Stepwise regression
Neural network
Bagged decision trees
Adaptive Neuro-fuzzy learning
Unsupervised Learning:
Confidence interval is set, generalized ESD test is implemented, Data points are detected. Unsupervised learning is completely opposite of supervised learning where labels do not exist. Here the data is fed, and tools are given to learn about the attributes of the data. This will learn the way of grouping, make clusters and organizing the data like humans. There are a lot of students who rely on our services like python homework help, artificial intelligence assignment help and machine learning assignment help.
Reinforcement Learning:
This type of machine learning interacts with the environment to generate right actions in order to find the best results. Using reinforcement learning methods one can let the machines detect ideal behavior in a certain content with a motive to enhance performance. It includes:
Q-learning
Temporal Difference (TD)
Monte-Carlo Tree Search
Asynchronous Actor-Critic Agents
Semi-Supervised Learning algorithm -
This type of algorithm is neither fully supervised nor fully unsupervised. This type of algorithm uses a small supervised learning component i.e small amount of pre-labeled annotated data and large unsupervised learning component i.e. lots of unlabeled data for training.
A Semi-Supervised algorithm assumes the following about the data –
Continuity assumption : The algorithm assumes that the points which are closer to each other are more likely to have the same output label.
Cluster assumption : The data can be divided into discrete clusters and points in the same cluster are more likely to share an output label.
Manifold assumption : the data lie approximately on a manifold of much lower dimension than the input space. This assumption allows the use of distances and densities which are defined on manifolds.
Key application of machine learning
Machine learning has applications in almost every industry. However, there are few fields which it can impact on a larger scale. These are:
Forecast accurate sales: Machine learning helps you to promote your product and services in a better way and predict accurate sales. ML will use the data and will modify the marketing strategies on a timely basis based on the behavioral patterns of customers.
Medical Anticipations and diagnosis: Machine learning is used to detect the patients who are prone to high risk and diagnose them with the right treatment and medicines and predict their readmissions.
Other Applications: Face detection, pattern recognition, video games, computer vision and cognitive services.
Feature extraction techniques in machine learning :
Feature extraction involves reducing the number of resources required to describe a large set of data. When the input data to an algorithms is to large to be processed and it is suspected to be redundant. then it can be transformed into a reduced set of features (also named a feature vector). Determining a subset of the initial features is called feature selection/extraction. The selected features are expected to contain the relevant information from the input data, so that the desired task can be performed by using this reduced representation instead of the complete initial data.
Bag of words
Auto-encoders
Countvectorizer
Hashing vectorizer
Kernel PCA
Partial least squares
Semidefinite embedding
Latent semantic analysis (LSA)
t-distributed Stochastic Neighbor Embedding (t-SNE)
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