Category Archives: AI

Machine Learning – Cloud-native Model Deployments

AWS ECS Cloud-native Model Deployments

In this post, we are going to learn about the cloud-native machine learning model deployments.  Cloud-native Deployments First and foremost, let’s understand the meaning of cloud-native deployments? If we are building an application or a service and we can deploy this application or the service on any cloud platform without much ado, it could be said as cloud-native deployment. And the way it is made possible is through the container technologies such as Dockers. What basically is required to be done is to wrap the applications or the services within the containers and move the containers images onto the cloud services such as AWS ECS, AWS EKS or Google Kubernetes …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , , , .

What, When & Why of Regularization in Machine Learning?

Regularization for regression models

In this post, we will try and understand some of the following in relation to regularizing the regression machine learning models to achieve higher accuracy and stable models: Background What is regularization? Why & when does one need to adopt/apply the regularization technique? Background At times, when one is building a multi-linear regression model, one uses the least squares method for estimating the coefficients of determination or parameters for features. As a result, some of the following happens: Often, the regression model fails to generalize on unseen data. This could happen when the model tries to accommodate for all kind of changes in the data including those belonging to both …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , , .

Unit Tests & Data Coverage for Machine Learning Models

Unit testing for Machine Learning Models

This post represents thoughts on what would it look like planning unit tests for machine learning models. The idea is to perform automated testing of ML models as part of regular builds to check for regression related errors in terms of whether the predictions made by certain set of input data vectors does not match with expected outcomes. This brings up some of the following topics for discussion: Why unit testing for machine learning models? What would unit tests for machine learning models mean? Data coverage or code coverage? Why unit testing for Machine Learning models? Once a model is built, the challenge is to monitor the performance metrics of the models …

Continue reading

Posted in AI, Data Science, Machine Learning, QA, Testing. Tagged with , , , , .

Confusion Matrix Explained with Python Code Examples

confusion matrix for classification model

In this post, you will learn about the confusion matrix with examples and how it could be used as performance metrics for classification models in machine learning. Let’s take an example of a classification model which is used to predict whether a person would default on a bank loan. To build this classification model, let’s say, a historical data set of 10000 records got chosen for building the model. As part of building the model, all of the 10,000 records got labeled where each record represented a person and got labeled as “Yes” or “No” based on whether they defaulted (Yes) or not defaulted (No). Out of 10,000 labeled records, …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , , .

Machine Learning Cheat sheet (Stanford)

Here is a great set of cheat sheet on some of the following topics: Supervised learning Unsupervised learning Deep learning Probability and statistics Linear algebra Tips and tricks including performance metrics https://stanford.edu/~shervine/teaching/cs-229/ Hope you liked the cheat sheets on different topics of machine learning and data science.

Posted in AI, Machine Learning. Tagged with , .

Machine Learning Models used in Facebook

machine learning models at 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

Posted in AI, Data Science, Machine Learning. Tagged with , , .

13 Programming Languages used for Machine Learning

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, …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , , .

Top 5 Machine Learning Introduction Slides for Beginners

Machine learning neural network slides

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 …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , , .

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 …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , , .

MIT OCW Machine Learning Courses Information

MIT Opencourseware Machine Learning

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) …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , , .

Machine Learning – Insurance Applications Use Cases

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) …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , , , .

AWS reInvent – Top 7 New Machine Learning Services

Amazon Forecast Technology Architecture

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 …

Continue reading

Posted in AI, AWS, Cloud, Data Science, Machine Learning. Tagged with , , , , , .

Guidelines for Creating an Ethical AI Framework

Ethical AI Framework Components

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 …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , , .

Ethical AI Principles – IBM, Google, Intel, Microsoft

microsoft ethical ai principles

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 …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , , .

IEEE Bookmarks on Ethical AI Considerations

ethical ai design ieee

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 …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , .

AI-powered Project Baseline to Map Human Health

project baseline

In this post, you will learn about technologies and data gathering strategy for Project Baseline, an initiative by Google. Project Baseline is an IOT-based AI-powered initiative to map human health. Different kinds of machine learning algorithms including deep learning etc would be used to understand different aspects of human health and make predictions for overall health improvements and precautionary measures. This would require a very large volume of data to be gathered and processed before being fed into AI models. The following represents the data gathering strategies for Project Baseline: Diagnostic tests covering blood-related tests; specialized tests such as ECG, chest X-ray, eyesight check Doctor examination leading to the collection of data related to health …

Continue reading

Posted in AI, Data Structure, Machine Learning. Tagged with , , .