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What is Applied machine learning
Machine learning is the part of artificial intelligence that enables a computer system to learn from example and previous data rather than through explicit programming. Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data, and predict outcomes.
Applied machine learning is the development of a learning system to address a specific learning problem.. It involves the building of data products or using algorithms within data science pipelines. Data science is the process of obtaining, transforming, analyzing, and communicating data to answer questions. We can apply machine learning in any case in which there are non-deterministic elements to a problem and especially where the manipulation and analysis of statistically generated data are required.
Machine learning can involve the either supervised learning model, unsupervised learning models or semi-supervised learning model. Supervised machine learning algorithms improve themselves on the basis of label training data. Unsupervised machine learning inferences and analysis are drawn from unlabelled data. In Semi-supervised learning that combines a small amount of labeled data with a large amount of unlabelled data during training.
Why it is important
Nowadays data is being generated rapidly from every sector and the data has become the lifeblood of all businesses. Computational processing is cheaper and more powerful, and affordable data storage. All these things means that it is possible to rapidly and automatically create models that can analyse data. In this case applied ML is most important to deal with larger, more complex data and provide accurate and faster results. By creating more accurate models, an organisation has a better chance of identifying profitable opportunities.
Important Concept of the Applied Machine Learning
Applied ML is about understanding the Machine Learning concepts at an abstract level sufficient enough to solve problems using machine learning algorithms and statistical techniques. This involves gaining expertise in using the tools and libraries which implement the Machine Learning Algorithms at their core.
Optimization Algorithms
Evaluation metrics
Statistical unit testing
Statistical integration testing
Supervised learning
It is a type of learning in which we have both input and output variables. An algorithm can derive a function from input to output. It is used when we have data for the output we have to predict.
Unsupervised learning
It is a type of machine learning in which models are trained using an unlabelled dataset and are allowed to act on that data without any supervision. In unsupervised learning not use the target variable. Unsupervised learning cannot be directly applied to a regression or classification problem because unlike supervised learning, we have the input data but no corresponding output data.
Classification : In machine learning classification is the process of categorising a given set of data into classes. It performed structured and unstructured data.
Regression : In machine learning Regression consists of mathematical methods that allow prediction of continuous data.
Natural Language Processing (NLP) : NLP is a common concept for machine learning. It allows computers to read human language and incorporate it into all kinds of processes.
Application or uses of Applied Machine Learning
Product recommendation
Self driving cars
Speech recognition
Pattern recognition
Image recognition
Online customer support
Traffic prediction
Medical diagnosis
Automatic language translation
Fraud detection
Virtual personal assistance
Email spam and malware filtering
Sentiment Analysis
Tools and Framework, libraries and packages
Scikit Learn : Scikit-learn is for machine learning development in python. It provides a library for the Python programming language.
Pytorch : PyTorch is a Torch based, Python machine learning library. The torch is a Lua based computing framework, scripting language, and machine learning library.
TensorFlow : TensorFlow is a free, open-source and most famous math library for creating neural networks and deep learning models.
Keras : Keras is an open source, most powerful and easy to use python library which is built on top of popular deep learning libraries like Tensorflow, theono etc. for building the deep learning model.
Surprise : Surprise is a Python library for building and analyzing rating prediction algorithms.
H2O : H2O is an open-source ML framework developed to solve the organizational problems of decision support system processes.
Type of Services/development work
Language Translation : This service that manages internationalisation content.
speech recognition : This service that converts audio streams into text.
Image recognition : It can identify an object as a digital image, based on the intensity of the pixels in black and white images or colour images.
Sigma : Sigma is the general classification and anomaly detection framework that is used for a variety of internal applications including site integrity, spam detection, payments, registration, unauthorised employee access, and event recommendations
Lumos : Lumos extracts high-level attributes and embeddings from an image and its content, enabling algorithms to automatically understand it.
Applied Machine learning process
Understand Problem : Understand the problem that is being solved.
Analyzed data : Understand the available information that will be used to develop the model.
Prepare data : In this step transform the raw data that can be used in building the model.
Evaluating Algorithm : Evaluation is the process of measuring the performance of a model.
Improve result : Improve model performance to develop more accurate models.
Present result : In this step describe the problem and solution to understand the others.
Some common projects or case study
Passenger survival prediction
Handwritten digit recognition
Heart Disease Prediction
House price prediction
Coal production Estimation
Credit card fraud detection
Retail store sales prediction
Diabetes prediction
Case study on understanding in-store customer behaviour
Case study on predictive maintenance of oil well pumps
Case study on anticipating emergencies
Use cases
Finance and Banking : ML is used for risk analysis, credit scoring, trading exchange forecasting, client analysis, and fraud detection.
Healthcare : ML is applied to increase diagnostic accuracy, optimize the cost of insurance products, identify at-risk patients, and many more.
E-commerce and Retail : ML is used to forecast optimize prices, product demand, customer recommendation, prevention fraud and more.
Education : Educators are using ML to spot struggling students earlier and take action to improve success and retention.
Cybersecurity : ML can track user behavior within a network to spot irregularities and gaps in existing security measures.
Important terms and keywords
Algorithm Tuning : Tuning is the process of maximising a model’s performance without overfitting or creating too high of a variance. In machine learning, this is accomplished by selecting appropriate “hyper parameters.”.
Bagging : Bagging stands for bootstrap aggregation. It is an ensemble method that is commonly used to reduce the variance within a noisy dataset.
Boosting : Boosting is a sequential ensemble method that iteratively adjusts the weight of observation as per the last classification.
Blending : It is an ensemble machine learning technique that uses a ML model to learn how to best combine the predictions from multiple contributing ensemble member models.
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