Category Archives: Data Science
Machine Learning Models used in Facebook
This post quickly represents machine learning projects and related machine learning models. The above diagram represents the usage of the following learning algorithms: Support Vector Machines (SVM) Gradient-boosted decision trees Multi-layer Perceptron (MLP): Used for ranking and personalizing news feeds, ads, search etc. Convolutional neural networks (CNN): Recurrent neural networks (RNN): Used for language translation, speech recognition, content understanding References
13 Programming Languages used for Machine Learning
In this post, you will learn about different programming languages which can be used to create (train) machine learning models to solve supervised and unsupervised learning problems. Here are the top 13 programming languages used for machine learning: R Language: R is one of the most popular programming language and environment for statistical computing and graphics. Python: There are some of the following Python libraries which makes it easy to create machine learning/deep learning models: Scikit-learn library (Classical machine learning models): Packages such as NumPy, SciPy, Pandas are very useful and helpful in creating supervised and unsupervised learning models. Deep learning models using python libraries provided by Tensorflow, PyTorch, Theanos, CNTK, …
Linear Regression Explained with Real Life Example
In this post, linear regression concept in machine learning is explained with multiple real-life examples. Both types of regression (simple and multiple linear regression) is considered for sighting examples. In case you are a machine learning or data science beginner, you may find this post helpful enough. The following topics got covered in this post: What is linear regression? Simple linear regression example Multiple linear regression example Before we get into looking at some examples of regression, lets quickly get up-to-speed with understanding the concepts related to linear regression. Also, here are some of my other posts in relation to linear regression: Linear regression explained with python examples: The concepts …
Top 5 Machine Learning Introduction Slides for Beginners
In this post, you will get to know a list of introduction slides (ppt) for machine learning. These slides could help you understand different types of machine learning algorithms with detailed examples. One or more slides from the following list could be used for making presentations on machine learning. If you are looking out for topics to be included in the machine learning course for your internal training purpose in your organization, the details presented below might turn out to be very helpful. If you are starting on learning data science, these could be good slides. Machine Learning Overview Machine Learning: An Overview: The slides present introduction to machine learning …
Andrew NG Machine Learning Coursera Videos
In this post, you will get to know the list of Andrew NG Machine Learning Coursera Videos. Here is the information: Youtube playlist of machine learning videos which are same as that of Andrew NG machine learning course on Coursera. One could use Internet Download Manager (IDM) to download these videos. Use Coursera-dl script found on Github to download the machine learning course. The script makes it easier to batch download lecture resources (e.g., videos, ppt, etc) for Coursera classes. Given one or more class names and account credentials, it obtains week and class names from the lectures page, and then downloads the related materials into appropriately named files and directories. Use AcademicTorrents website …
MIT OCW Machine Learning Courses Information
In this post, you get the information related to MIT OCW machine learning course from MIT OpencourseWare (OCW). They use Matlab as the primary programming environment. The documentation for Matlab could be found on this page, Matlab Documentation. The course is provided by Electrical Engineering and Computer Science department. Other related courses which could be useful for data scientist / machine learning engineers are some of the following: Introduction to probability (Video lectures, Lecture notes) Introduction to computational thinking and data science (Video lectures, Lecture notes) Lecture Notes – Machine Learning Course Lecture notes could be found on the following topics: Introduction, linear classification, perceptron update rule (PDF) Perceptron convergence, generalization (PDF) …
Machine Learning – Insurance Applications Use Cases
In this post, you will learn about some of the following insurance applications use cases where machine learning or AI-powered solution can be applied: Insurance advice to consumers and agents Claims processing Fraud protection Risk management AI-powered Insurance Advice to Consumers & Agents Insurance Advice to Consumers: Machine learning models could be trained to recommend the tailor made products based on the learning of the consumer profiles and related attributes such as queries etc from the past data. Such models could be integrated with Chatbots (Google Dialog flow, Amazon Lex etc) applications to create intelligent digital agents (Bots/apps) which could understand the intent of the user, collect appropriate data from the user (using prompts) …
AWS reInvent – Top 7 New Machine Learning Services
In this post, you will learn about some great new and updated machine learning services which have been launched at AWS re:Invent Conference Nov 2018. My personal favorite is Amazon Textract. Amazon Personalize Amazon Forecast Amazon Textract Amazon DeepRacer Amazon Elastic inference AWS Inferentia Updated Amazon Sagemaker Amazon Personalize for Personalized Recommendations Amazon Personalize is a managed machine learning service by Amazon with the primary goal to democratize recommendation system benefitting smaller and larger companies to quickly get up and running with the recommendation system thereby creating the great user experience. Here is the link to Amazon Personalize Developer Guide. The following are some of the highlights: Helps personalize the user experience using some of …
Guidelines for Creating an Ethical AI Framework
In this post, you will learn about how to create an Ethical AI Framework which could be used in your organization. In case, you are looking for Ethical AI RAG Matrix created with Excel, please drop me a message. The following are key aspects of ethical AI which should be considered for creating the framework: Fairness Accountability Transparency Reliability & Safety Data privacy and security Fairness AI/ML-powered solutions should be designed, developed and used in respect of fundamental human rights and in accordance with the fairness principle. The model design considerations should include the impact on not only the individuals but also the collective impact on groups and on society at large. The following represents some …
Ethical AI Principles – IBM, Google, Intel, Microsoft
In this post, you will get a quick glimpse of ethical AI principles of companies such as IBM, Intel, Google, and Microsoft. The following represents the ethical AI principles of companies mentioned above: IBM Ethical AI Principles: The following represents six ethical AI principles of IBM: Accountability: AI designers and developers are responsible for considering AI design, development, decision processes, and outcomes. Value alignment: AI should be designed to align with the norms and values of your user group in mind. Explainability: AI should be designed for humans to easily perceive, detect, and understand its decision process, and the predictions/recommendations. This is also, at times, referred to as interpretability of AI. Simply …
IEEE Bookmarks on Ethical AI Considerations
In this post, you will get to have bookmarks for ethical AI by IEEE (Institute of Electrical and Electronics Engineers) group. Those starting on the journey of ethical AI would find these bookmarks very useful. ML researchers and data scientists would also want to learn about ethical AI practices to apply them while building and testing the models. The following are some bookmarks on ethical AI considerations by IEEE group: The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems: An initiative by IEEE for setting up new standards and solutions, certifications and codes of conduct, and consensus building for ethical implementation of intelligent technologies to ensure that these technologies are …
Machine Learning Models – Bias Mitigation Strategies
In this post, you will learn about some of the bias mitigation strategies which could be applied in ML Model Development lifecycle (MDLC) to achieve discrimination-aware machine learning models. The primary objective is to achieve a higher accuracy model while ensuring that the models are lesser discriminant in relation to sensitive/protected attributes. In simple words, the output of the classifier should not correlate with protected or sensitive attributes. Building such ML models becomes the multi-objective optimization problem. The quality of the classifier is measured by its accuracy and the discrimination it makes on the basis of sensitive attributes; the more accurate, the better, and the less discriminant (based on sensitive attributes), the better. The following are some of …
Facebook Machine Learning Tool to Check Terrorists Posts
In this post, you will learn about details on Facebook machine learning tool to contain online terrorists propaganda. The following topics are discussed in this post: High-level design of Facebook machine learning solution for blocking inappropriate posts Threat model (attack vector) on Facebook ML-powered solution ML Solution Design for Blocking Inappropriate Posts The following is the workflow Facebook uses for handling inappropriate messages posted by terrorist organizations/users. Train/Test a text classification ML/DL model to flag the posts as inappropriate if the posts is found to contain words representing terrorist propaganda. In production, block the messages which the model could predict as inappropriate with very high confidence. Flag the messages for data analysts processing if the …
ML Model Fairness Research from IBM, Google & Others
In this post, you would learn about details (brief information and related URLs) on some of the research work done on AI/machine learning model ethics & fairness (bias) in companies such as Google, IBM, Microsoft, and others. This post will be updated from time-to-time covering latest projects/research work happening in various companies. You may want to bookmark the page for checking out the latest details. Before we go ahead, it may be worth visualizing a great deal of research happening in the field of machine learning model fairness represented using the cartoon below, which is taken from the course CS 294: Fairness in Machine Learning course taught at UC Berkley. IBM Research for ML Model Fairness …
Fairness Metrics – ML Model Sensitivity for Bias Detection
There are many different ways in which machine learning (ML) models’ fairness could be determined. Some of them are statistical parity, the relative significance of features, model sensitivity etc. In this post, you would learn about how model sensitivity could be used to determine model fairness or bias of model towards the privileged or unprivileged group. The following are some of the topics covered in this post: How could Model Sensitivity be used to determine Model Bias or Fairness? Example – Model Sensitivity & Bias Detection How could Model Sensitivity determine Model Bias or Fairness? Model sensitivity could be used as a fairness metrics to measure the model bias towards the privileged or unprivileged group. Higher the …
Data Science Project Folder Structure
Have you been looking out for project folder structure or template for storing artifacts of your data science or machine learning project? Once there are teams working on a particular data science project and there arises a need for governance and automation of different aspects of the project using build automation tool such as Jenkins, one would feel the need to store the artifacts in well-structured project folders. In this post, you will learn about the folder structure using which you could choose to store your files/artifacts of your data science projects. Folder Structure of Data Science Project The following represents the folder structure for your data sciences project. Note that the project structure is created keeping in mind integration with build and automation jobs. …
