Demystifying Machine Learning (ML)

Machine learning has become the game-changing technology and transforming the and revolutionizing the way the industries (Industry 4.0) work. From virtual assistants to self-driving cars, machine learning is at the forefront of innovation.

However, many of the technology experts are intimidated by the complexity of machine learning algorithms and are unsure where to begin. In this blog post, I aimed to demystify machine learning and provide an introduction to core concepts that will help to understand the basics of this exciting field. 

A decision-making Cheat Sheet will help you to identify the right kind of tools, algorithms, libraries and/or framework to be used for a given problem. This blog-post will give you an idea of where to start.

Ultimately, the best algorithm to use for making ML to work will depend on the specifics of the problem, the nature and quality of the data, and the available computing resources. It's often a good idea to experiment with multiple algorithms and compare their performance on a test set to determine which one works best for a particular task.

We will also provide a quick decision cheat sheet to help you make informed decisions when working with machine learning models.

Whether you are a beginner or an experienced data scientist, this blog post will provide valuable insights and practical tips to help you unlock the power of machine learning. So, let's get started!

A Machine Learning (ML) algorithm is a mathematical model or a set of rules that is used to learn patterns from data. It is a part of the broader field of Artificial Intelligence (AI) and is designed to enable machines to learn and make predictions or decisions based on data.

Machine learning algorithms use statistical techniques to identify patterns in data and learn from those patterns to make predictions or decisions about new data. These algorithms can be broadly categorized into four types: 

• Supervised learning 

• Unsupervised learning

• Reinforcement learning and 

• Semi-Supervised Learning

Supervised Learning: This type of algorithm involves providing the model with labelled data and training it to learn from that data. The purpose of supervised learning is to build a model that can make accurate predictions on new, unseen data. Examples: Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, Neural Networks.

Unsupervised Learning: This type of algorithm involves providing the model with unlabelled data and allowing it to learn from the inherent patterns in that data. The purpose of unsupervised learning is to discover hidden structures or groupings in the data. Examples: K-Means Clustering, Hierarchical Clustering, Principal Component Analysis, t-SNE, Autoencoders.

Reinforcement Learning: This type of algorithm involves training a model to make decisions based on rewards and penalties received through interacting with an environment. The purpose of reinforcement learning is to optimize a model's decision-making abilities over time. Examples: Q-Learning, Deep Reinforcement Learning, Monte Carlo Tree Search.

Semi-Supervised Learning: This type of algorithm involves training a model on a combination of labelled and unlabelled data. The purpose of semi-supervised learning is to leverage the unlabelled data to improve the model's accuracy on the labelled data. Examples: Self-Training, Co-Training.

Each of these types of machine learning algorithms has its own set of tools, languages and libraries. Here are few:

Python: Python is a popular programming language for machine learning, with several powerful libraries such as Scikit-learn, TensorFlow, and PyTorch.

R: R is another programming language commonly used for machine learning, with popular libraries such as caret, mlr, and randomForest.

MATLAB: MATLAB is a numerical computing environment often used for machine learning, with popular tool boxes such as Statistics and Machine Learning Toolbox and Neural Network Toolbox.

Weka: Weka is a Java-based machine learning toolkit that provides a graphical interface for implementing and testing machine learning algorithms.

KNIME: KNIME is a data analytics platform that provides a visual interface for building machine learning workflows, and includes several built-in machine learning algorithms.

Machine Learning (ML) Model

Types of Machine Learning Models: 

• machine learning classification, which assigns the response to a particular set of classes, and 

• machine learning regression, which assigns a continuous response.

Deciding on the appropriate machine learning model can be daunting, as there are numerous classification and regression models, each with a distinct learning approach. The procedure necessitates weighing the trade-offs, such as model speed, accuracy, and complexity, and may require experimentation to determine the most effective choice.

Machine Learning Regression Models:

In regression analysis, the input variables are used to create a mathematical model that predicts the value of the output variable. The model is typically represented as a linear or nonlinear function that relates the input variables to the output variable. Once the model is trained on a set of data, it can be used to make predictions on new data.

There are several types of regression models in machine learning, including:

1. Linear Regression: A simple, linear model that uses a straight line to model the relationship between the input and output variables.

2. Polynomial Regression: A model that uses a higher degree polynomial function to fit the data, to capture non-linear relationships between the input and output variables.

3. Ridge Regression: A regularized linear regression model that adds a penalty term to the cost function, to prevent overfitting.

4. Lasso Regression: A regularized linear regression model that uses L1 regularization to shrink the coefficients of less important features to zero.

5. Elastic Net Regression: A regularized linear regression model that uses a combination of L1 and L2 regularization, to balance between feature selection and feature shrinkage.

6. Decision Tree Regression: A tree-based model that recursively splits the data into subsets based on the feature values, to predict the continuous output variable.

7. Random Forest Regression: An ensemble model that uses multiple decision trees to improve accuracy and reduce overfitting.

8. Gradient Boosting Regression: An ensemble model that combines multiple weak learners to improve accuracy and reduce bias, by fitting the residuals of previous models.

9. Support Vector Regression (SVR): A model that uses a hyperplane to predict a continuous output variable, by maximizing the margin between the predicted output and the actual output.

Regression Algorithm	Libraries / Frameworks	Application Areas
Linear Regression	TensorFlow, Scikit-learn, Statsmodel, NumPy, Pandas, MATLAB,  R Linear Models	Economics: Supply vs Demand, Price vs Quantity, Economy and Employment Finance:  Stock Prices, Interest Rates Marketing: Ad Spending vs Sales, Impact of marketing campaigns, Healthcare: Health outcome Social Science: Income vs education, social policies on different populations
Polynomial Regression	TensorFlow, Scikit-learn, NumPy, MATLAB,  R Polynomial Regression	Engineering: Force and Displacement, Strain and Stress Physics: Velocity and Time, Distance and Time Biology: Body Weight with Height, Bloodsugar vs Insulin Geology: Relationship between temperature and depth or between pressure and time Environmental Studies: Relationship between rainfall and temperature or between air quality and temperature
Ridge Regression	TensorFlow, Scikit-learn, Statsmodels, PyTorch	Finance: Finance: Forecast Stock price, interest rate  Marketing: Impact of advertising on Sales, Identify effective marketing Strategy Economics: Relationship between GDP, inflation and unemployment Healthcare: Predict the risk of developing disease Environmental Science: Pollution and climate and impact on human health system Social Science: Education, Income, Demographics and impact on crimea rates, voting behavior and social mobility
Lasso Regression	TensorFlow, Scikit-learn, Statsmodels, PyTorch	Feature Selection, Signal Processing, Gene Classification, Influence customer behavior to develop targeted marketing strategies, Portfolio optimization, Object extraction and image classification from images
Elastic Net Regression	TensorFlow, Scikit-learn, Statsmodels, PyTorch	Identify Genetic variants, identify important factors that would influence customers and targeted marketing, Portfolio optimization, Image Processing, Identify the environmental factors that affect biodiversity and ecosystem functioning, identify the social factors that changes the crime rate, voting behavior and social mobility
Support Vector Regression (SVR)	TensorFlow, Scikit-learn, LIBSVM, sklearn.svm	Finance: Forecast stock price, exchange rates Healthcare: Disease progression, health outcome factors Energy: Predict Energy consumption vs Demand, Optimize energy production and distribution Engineering: Predicting strength of materials and designing control system Environmental Science: Predict effects of pollution and climate change on various ecosystems  Transportation” Predict traffic flow and congestion and optimize transport systems
Decision Trees	TensorFlow, Scikit-learn, DecisionTreeRegressor, PyTorch, H2O	Finance: Predict Stock Price, Interest Rate Marketing: Plan marketing strategy , such as advertising spend, promotional offers and pricing Real Estate: Price of property based on location size, amenities Healthcare: Predict length of hospital stay, cost of medical treatment, and health outcome Energy: Energy consumption vs demand, production and distribution optimization Engineering: Predict performance of materials and system and optimize designs
Random Forest	TensorFlow, Scikit-learn, PyTorch, RandomForestRegressor, H2O,  Spark MLib	Finance: Predict Stock Price, Interest Rate Healthcare: Predict the risk of developing certain disease, health outcome patterns Marketing: Predict customer behavior such as bing patterns Natural Language Processing: Sentiment Analysis, Text classification and text summarization Image Processing: Image classification, Object detection, and image segmentation Fraud Detection: Detect fraudulent transactions such as credit fraud, insurance fraud Environmental Science: Predict the effects of pollution and climate change on various ecosystems
Gradient Boosting Machines (GBM)	XGBoost, LightGBM, CatBoost, H2O	Finance: Predict Stock Price, Interest Rate Healthcare: Predict the risk of developing certain disease, health outcome patterns Marketing: Predict customer behavior such as bing patterns Natural Language Processing: Sentiment Analysis, Text classification and text summarization Image Processing: Image classification, Object detection, and image segmentation Fraud Detection: Detect fraudulent transactions such as credit fraud, insurance fraud Search Engines: Rank Search result to improve search relevance
Neural Networks	TensorFlow, Keras, PyTorch, Caffe,  MXNet	Image and Speech Recognition: Facial recognition, Handwriting recognizations, Pattern recognition and speech-to-text Natural Language Processing: Language translation, chatbots and sentiment analysis Finance: Predict Stock Price, Interest Rate Healthcare: Predict the risk of developing certain disease, health outcome patterns Robotics and Automation: Object detection, navigation and control Gaming: Game AI, Strategy and decision making Cybersecurity: Intrusion detection, malware analysis and fraud detection

Machine Learning Classification Models:

Classification models are algorithms that are trained on labeled data to predict the class or category of new, unlabeled data. The goal of a classification model is to identify the underlying pattern or relationship between the input data and the output classes.

During model training, the model learns from the labeled data by adjusting its internal parameters and minimizing the error between the predicted output and the actual output. Once the model is trained, it can be used to predict the class of new, unlabeled data.

Classification models are evaluated based on metrics such as accuracy, precision, recall, and F1 score, which indicate how well the model is able to predict the correct class. The choice of a classification model depends on various factors such as the size and complexity of the data, the number of classes, and the desired level of accuracy.

There are several types of classification models in machine learning, including:

1. Binary Classification: A model that classifies data into two classes. Examples include spam detection, fraud detection, and sentiment analysis.

2. Multiclass Classification: A model that classifies data into more than two classes. Examples include image recognition, speech recognition, and medical diagnosis.

3. Multi-label Classification: A model that assigns multiple labels to each data point. Examples include text classification, where a document may belong to multiple categories such as politics, sports, and entertainment.

4. Imbalanced Classification: A model that deals with imbalanced classes, where one class has significantly fewer instances than the other(s). Examples include disease diagnosis, where the number of healthy individuals is much larger than the number of diseased individuals.

5. Hierarchical Classification: A model that organizes classes into a hierarchy, where each class is a subset of another. Examples include taxonomy classification, where each class represents a level in a hierarchy of biological classification.

6. Ensemble Classification: A model that combines multiple classification models to improve accuracy and reduce overfitting. Examples include Random Forest and Gradient Boosting.

Classification Algorithm	Libraries / Frameworks	Application Areas
Logistic Regression	TensorFlow, Scikit-learn, Statsmodels, glm, Caret, MATLAB, Weka	Medical diagnosis: Predict possibility of a patient getting particular disease Fraud detection: Fraud detection based on patterns and anomalies in data Marketing: Predict possibility of customer purchasing a product or responding to a campaign Credit risk analysis: Borrower defaulting on a loan or credit card payment Image classification: Identifying objects or people in an image Natural language processing: Sentiment analysis and predict given text carries positive or negative thought
Decision Trees	TensorFlow, Scikit-learn, XGBoost, Weka, MATLAB	Finance: To determine credit scores and risk level of individual or businesses Healthcare: To identify the presence of disease based on symptoms Marketing: Buying behaviors and targeted marketing campaigns Fraud Detection: Transaction fraud and fraud insurance claims detection Customer Service: Automated customer service Manufacturing: To identify root cause of defects Environmental Science: To identify the impact of various interventions on the environment
Random Forest	TensorFlow, Scikit-learn, XGBoost, randomForest, caret, Weka, MATLAB	Image Classification: Classify images based on features such as texture, color, shape etc Bioinformatics: Predict functions of proteins based on their amino acid sequence and structure Fraud Detection: To detect fraudulent credit card transactions based on patterns Finance: Predict stock prices or to classify financial statements as fraudulent or not. Customer: Predict whether a customer is likely to churn or not based on their demographics and behavioral data Sentiment analysis: Classify the sentiment of text data, such as product reviews or social media posts
Support Vector Machines (SVM)	TensorFlow, Scikit-learn, e1071, caret, Weka, MATLAB	Text classification: Content based classification like identifying spam emails, categorizing articles etc. Image Classification: Classify images based on features such as texture, color, shape etc Bioinformatics: Classify protein sequences, predict gene expression levels and analyze gene interactions Finance: Classify financial statements as fraudulent or not. Face Recognition and Speech Recognition
K-Nearest Neighbors (KNN)	TensorFlow, Scikit-learn, class, caret, Weka, MATLAB	Product or Service Recommendation,  Image Recognition and classification,  Medical Diagnosis,  Credit risk assessment and Financial Fraud detection,  Text classification and  Marketing Segmentation
Naive Bayes	TensorFlow, Scikit-learn, e1071, caret, Weka, MATLAB	Text Classification, Email Filtering Sentiment Analysis Medical Diagnosis Fraud Detection Weather Forecasting based on historical weather data Market basket analysis: Analyze customer purchases and identify to recommend associated product
Neural Networks	TensorFlow, PyTorch, Keras, Deeplearning4j, MATLAB	Image classification Natural Language processing Speech Recognition Fraud detection Medical Diagnosis Finance: Stock market prediction, Trend analysis, Financial data analysis Autonomous Vehicles: To control the movements of self-driving cars, including steering, acceleration and braking

Pre-defined ML Models:

Regression models are typically not something that you can simply download and use in the same way as software or other applications. Regression models are statistical models that are built using data, and they require specific data inputs to produce accurate predictions or estimates.

If you are looking for pre-built regression models for a specific application or problem, you may be able to find pre-trained models or model templates through online marketplaces or specialized machine learning platforms. For example, few popular sources for pre-trained image models include:

• TensorFlow Hub: A repository of pre-trained models for TensorFlow, including a wide range of image models.

• PyTorch Hub: A repository of pre-trained models for PyTorch, including many image models.

• Model Zoo by Caffe: A repository of pre-trained models for Caffe, a deep learning framework focused on computer vision.

• Model Zoo by MXNet: A repository of pre-trained models for MXNet, a deep learning framework with a focus on scalability.

Keep in mind that pre-trained models may not always be the best solution for your specific needs, and may require additional customization or training to produce accurate results.

As you embark on your own machine learning journey, remember to always keep an open mind, be willing to learn and experiment, and seek out resources and guidance when needed. With the right tools, algorithms, and mindset to experiment, you can unlock the full power of machine learning and use it to transform your own work and industry. 

Good luck!!!

Cheers,

Venkat Alagarsamy

Epilogue

If you would like a Excel cheat sheet on machine learning algorithms, please email your request.