Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.

AI Resume Analyzer is an NLP- and ML-based system that extracts resume text, applies TF-IDF and supervised models for job-role and experience prediction, and performs resume–JD similarity scoring with skill gap visualization using Streamlit.

Interactive ML/AI web application implemented via Dash. This app allows the end user to modify parameters for a vector valued function of the logarithmic spiral as well as adjust parameters for an on-the-fly supervised trained Support Vector Machine for Regression (SVR) model. Demonstrates the advantage of leveraging Docker for data science development without installing multiple software tools and frameworks with reproducible environments.

Credit Risk Classification – Built Random Forest model to predict loan risk deployed using Flask API with web form UI. Laptop Price Prediction – Developed regression model with data preprocessing and feature engineering; deployed via Streamlit. California Housing Prices – Trained regression models (Ridge, RF) with log-scaling and model evaluation

Second project as part of AI Bootcamp implementing supervised or unsupervised ML Model

Built ML models to segment customers using behavioral data and predict churn. Applied clustering and supervised learning techniques, with Explainable AI to interpret predictions and identify key drivers of customer churn.

Built an AI-driven ML model using Python to simulate intelligent agent interactions within various AI tools. This program leverages supervised and unsupervised learning, model training, and reinforcement learning principles to optimize agent decision-making and adaptability.

A collection of my personal AI/ML projects showcasing predictive modeling, deep learning, and cloud deployment. Covers supervised/unsupervised learning, NLP, and time-series forecasting. Includes real-world case studies, hands-on implementations in Python, and cloud-based deployment.

Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

Sign language recognition project using Python, OpenCV, and MediaPipe to extract hand landmarks from webcam frames, then Scikit‑Learn models to classify static signs. Built as a beginner-friendly computer vision and machine learning project to practice data preprocessing, feature extraction, and supervised learning for ML/AI preparation.

Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

Built a tool to classify and predict whether a patient is prone to heart failure depending upon multiple attributes through the AI/ML prediction model. It is a binary classification with multiple numerical and categorical features. A brief description of Time Series Analysis and supervised Learning has also been included.

Supervised ML project to predict patient obesity level based on lifestyle habits, with full pipeline: data cleaning, feature engineering, exploratory data analysis, data preprocessing, model training, comparison, evaluation, and deployment. Developed in Konecta's AI/ML internship program.

A collection of supervised and reinforcement learning models to explore ML / AI

Supervised and unsupervised ML models trained on a raw 2000-sample dataset for AI internship.

MSc Data Science & AI dissertation project: An adaptive AI tutoring system using NLP, clustering (KMeans), reinforcement learning (DQN), and supervised ML models for personalised learning.

AI/ML-powered DeFi transaction anomaly detection platform using supervised and unsupervised models to identify high-risk blockchain activity in real time.

AI/ML-powered DeFi transaction anomaly detection platform using supervised and unsupervised models to identify high-risk blockchain activity in real time.

Fraud Detection System using AI/ML — Built a model to identify and flag fraudulent transactions using supervised learning algorithms.

The World's Most Advanced Compliance, Risk & AML Intelligence Platform Powered by cutting-edge AI/ML models combining Supervised, Unsupervised, and Reinforcement Learning

Custom-built AI/ML/DL library implementing key models for supervised, unsupervised, and deep learning, emphasizing efficiency, accuracy, and adaptability for data science applications.

AI-powered Customer Behavior Profiling system for fraud detection. Implements data collection, preprocessing, behavior profiling with clustering, and anomaly detection using supervised and unsupervised ML models.

A collection of AI/ML models, experiments, and utilities using Python, Scikit-learn, TensorFlow, and PyTorch. Covers supervised, unsupervised, and reinforcement learning with real-world datasets.

## 🧠 AI-Powered RPA Trading Strategy This repo contains a full-stack trading system using: - Supervised (LSTM) and self-supervised (CPC) ML models - RPA automation for trading - Reinforcement Learning (PPO, DQN) - TensorFlow, Keras, and Alpaca API

🎓 ML-based Placement Prediction model developed during a 54+ hour AI & ML training program. Uses supervised learning to forecast student placement outcomes based on academic and skill metrics.

An AI/ML project that combines risk segmentation using clustering with supervised regression models (Random Forest, XGBoost) to predict optimal, explainable pricing from financial and behavioral data.

End-to-end AI Network Intrusion Detection System featuring supervised ML models (Random Forest, XGBoost), multi-stage classification, and a modern Streamlit dashboard for real-time intrusion analysis.

Key learnings from Day 1 of Oracle’s AI Foundations course, covering AI, ML, DL, generative AI, neural networks, language & vision models, supervised & reinforcement learning, and practical tasks like text/image classification and content generation.

A collection of AI and machine learning projects completed during my Scaler journey - including supervised/unsupervised models, deep learning experiments, NLP pipelines, and end-to-end ML applications.