Machine Learning Assignment Help | Machine Learning Homework Help
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What is Machine Learning?
Machine learning is a field of computer science that makes use of various statistical techniques to let the computer learn on its own by analyzing the data without programming. Machine learningis mostly used in Artificial Intelligence. Machine learning majorly focuses on developing computer applications that can access data and use this data to learn without human intervention. The learning process starts by observing or with the help of data. The main aim is to let computer learn automatically without the assistance of humans.
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. This helps the businesses to take right business decisions by analyzing huge chucks of data. There are different fields that are using machine learning. There include – health care, fraud detection, financial services, personalized recommendation, etc. The process of machine learning includes:
- Identify appropriate data set and then prepare for analysis
- 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
Learn Different Machine Learning Methods from Our Data Science Experts
1. Supervised learning
This type of learning will train the model with the input and output data that is known to predict the output of the future. This will predict the output based on the evidences. This will take known set of input data and known responses and then will train the model to get the predictions for the response receive for new data. 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:
A] 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. For instance, an application that is used for recognizing handwriting can be used to recognize numbers as well as letters. The unsupervised pattern recognition technique will be used to detect objects and segment images.
Algorithms used to perform classification include:
- Super Vector Machine (SVM)
- K-nearest neighbor
- Neural networks
- Logical regression
- Bagged decision tree
B] Regression technique : This will produce and predict continuous responses. For instance, temperature change and fluctuation of power with demand and this is widely used by the electricity board to predict load and algorithmic trading. This type of technique is perfect to use when you are working with data range or the response is based on a real number like time and temperature until the equipment starts to malfunction.
The key regression algorithm techniques that are used include:
- Linear model
- Non-linear model
- Stepwise regression
- Neural network
- Bagged decision trees
- Adaptive Neuro-fuzzy learning
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2. Unsupervised learning
This type of learning involves no control of developer directly. Unsupervised learning will extract the data structures and patterns that are hidden. This draws inferences from the available datasets that comprises of input data without having any kind of labeled responses. The output is unknown and has to be defined. The key difference between supervised and unsupervised learning is that, the former will use labeled data and the later will be using unlabeled data. This type of learning is used to explore the data structure, extract key insights, detect patterns and use this into operation to boost efficiency.
The following techniques are used to explain the data. There include:
Clustering: This is used to carry out exploratory data analysis to find out hidden patterns or data groups. The key applications where this type of technique is used include market research, object recognition, etc. For instance, if the telecommunication company is finding out the locations where they can actually build cell towers, then machine learning will be used to find out the clusters of people who are depending on the towers. Generally, a person can use a single tower at a time, so clustering algorithm will be used to design the tower to optimize reception of signals for group of customers. You can seek our machine learning homework help on this topic from our experts.
Dimensionality reduction: A lot of noise is produced in the incoming data. Machine learning algorithms will be used to filter out the noise from the information.
The commonly used algorithms include:
- K-means clustering
- T-Distributed Stochastic Neighbor Embedding
- Principal Component Analysis
- Association rule
3. Semi-supervised learning
This algorithm will stand between supervised learning and unsupervised learning. This type of learning will pick few aspects in each of these learning and form into one. This uses labeled and unlabeled data for carrying out training. So, here a small amount of labeled data and huge amount of unlabeled data will be used. The systems that are using this type of method are able to boost the learning accuracy. This learning method is used when labeled data need appropriate resources to train or learn from it. When unlabeled data is acquired, then you do not need additional resources. Enhance your understanding on the subject by availing Machine learning assignment help from our experts.
4. Reinforcement machine learning
This type of learning will have interaction with the environment to produce actions and find errors. Trial and error method and delayed reward are two key traits of reinforcement learning. This will let the systems and applications to find their ideal behavior in a specific context to improve their performance. The reward feedback is enough for agents to learn the action better.
The key reinforcement machine learning includes:
- Temporal Difference (TD)
- Monte-Carlo Tree Search
- Asynchronous Actor-Critic Agents
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Key Applications 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:
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. This is based on the records of other patients who have the same symptoms. By diagnosing the patient with right treatment will promote their speedy recovery.
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.
Time-intensive data entry tasks: Data duplication, is the key concern faced by organization to automate their data entry process. When the machine learning algorithm is used, machines will be carrying out time-intensive data entry tasks while leaving the workers to focus on other tasks.
Other Applications: Face detection, pattern recognition, video games, computer vision and cognitive services
Automatic Speech Recognition
Natural Language Processing
DNA sequence analysis
Protein sequence analysis
Automatic Game Playing
Predicting functional structures
Object Classification in Photographs
Automatic Machine Translation
Metabolic and regulatory networks
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|Classification Trees||Optimization Methods|
|Logistic Regression||Naive Bayes Theorem|
|K Means clustering||Decision Tree|
|Natural Language Processing||Hidden Markov Models|
|Random Forrests||Kernel PCA|
|Gradient Boosting||Kernel Ridge Regression|
|Factor Analysis Bias and Variance||Probabilistic Modeling|
|Deep Learning||Artificial Neural Networks|
|Clustering Algorithms||Predictive Modeling|
|Hypothesis Space||Hierarchical Clustering|
|Instance based Learning||Graphical Models and Factor Graphs|
|Ensemble Learning||WEKA implementations|
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