Learn how to develop, deploy and iterate on production-grade ML applications.

A web app implemeted with ML algo to make prediction on stock data, made on Django framework.(Stock-Market-predictor)

It is a web app tutorial project made with streamlit, a ML web app tool. It has some super cool features that can eliminate the need of any web framework. So, a data scientist can focus entirely on the analytics part rather than worrying about managing frontend and backend with any sort of framework. Do check out this project!

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So this a Discord Bot that i made for fun to use it With Friends , so basically i did it in 20 mins since he havent any ai or Ml yet , i will try to improve it in next ipcoming months ,

Obstacle avoiding self driving car made with Unity ML Agents.

Tau LLM made with Unity 6 ML Agents

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.

EAN-17 On-Device Barcode Scanner in Flutter made with camera and firebase_ml_vision packages.

Dermatological Issues/disorders are most commonly spread worldwide. This can be caused by various fungal, bacterial, or skin allergies. Effective use of Emerging technologies like AI/ML can recognize such diseases. Computer Vision is one such platform that made the possibility of detecting the cause accurately through Images.The problem here is to develop an Application Programming Interface which can be easily integrated with Android app to detect the skin disease without any physical interaction with a Dermatologist.

EVE Playing Ball - Reinforcement learning demo made with Unity ML-Agents

A Python Web App, frontend made with Streamlit, which allows easy implementation of ML models and techniques on user dataset with GUI and no coding.

Made With ML Summer 2020 Incubator

An app made with firebase_ml_vision capable of extracting text out from images and performing actions with them.

A Streamlit based web app which targets on converting voices into different languages (Hindi to English (for now)) keeping the voice intact. Many translators provide conversion but the voice is robotic. It is made with the help of combination of different ML models like Facebook m2m 100, IBM Watson etc. It is based on Tacotron: An end-to-end speech synthesis system by Google.

In the project, I started off by doing a Humming Bird tutorial from Unity-Learn to get an idea about ML-Agents, reinforcement learning, and training a model. Simultaneously, I got the chance to explore the Unity Editor and tried out building the entire scene. While training the bird’s model, I understood a lot about hyperparameters which I then tweaked and tuned to try out new models, the trial and testing sometimes gave horrible results making me realize how much a change in a parameter could affect the performance of a model. Once I was through the tutorial and had a basic understanding of how ML-Agents work, I decided to build other games and empower them with ML-Agents. I chose Air Hockey where wrote my own observations and change the config file to train the model in such a way that it performs best. Initially, I was lost, but then gradually figured out how to train the player similar to the hummingbird. It didn't work out great at the beginning, then I added our own observations, rewards, made some code changes, and provided a scoring UI to finally create an amazing ML-Agent that was smart enough to never let us win!!

In this project, we focus on the researches applying natural language processing (NLP) technologies in the finance domain.

An Icon classifier made with TFLite Model Maker and deployed to Android with ML Model Binding.

an ML based AI assistant🤖 made to help you with lots of features😃

A inteligent Agent made with Unity's open-source ML-Agents project (The Unity Machine Learning Agents Toolkit) to navigate trough levels recreated from the game Hades.

Parkinson disease patients suffer from "Freezing of Gait"(FOG) events during daily normal activities. Medical research has shown Rhythmic auditory cueing can recover normal gait when freezing events occur. The occurence of FOG events are detected using body worn accelerometer based systems. This system can be made more cheaper and scalable with implementation of low sized ML models.

In this project I developed a local Django web app that could detect and distinguish twitter social bots using Benford law and ML algorithms in conjunction with the twitter API. Made using Python, ML and Django

Discord.JS fan-made bot created to interact with chai.ml chatbots

Problem Statement Introduction So far, in this course, you have learned about the Hadoop Framework, RDBMS design, and Hive Querying. You have understood how to work with an EMR cluster and write optimised queries on Hive. This assignment aims at testing your skills in Hive, and Hadoop concepts learned throughout this course. Similar to Big Data Analysts, you will be required to extract the data, load them into Hive tables, and gather insights from the dataset. Problem Statement With online sales gaining popularity, tech companies are exploring ways to improve their sales by analysing customer behaviour and gaining insights about product trends. Furthermore, the websites make it easier for customers to find the products they require without much scavenging. Needless to say, the role of big data analysts is among the most sought-after job profiles of this decade. Therefore, as part of this assignment, we will be challenging you, as a big data analyst, to extract data and gather insights from a real-life data set of an e-commerce company. In the next video, you will learn the various stages in collecting and processing the e-commerce website data. Play Video2079378 One of the most popular use cases of Big Data is in eCommerce companies such as Amazon or Flipkart. So before we get into the details of the dataset, let us understand how eCommerce companies make use of these concepts to give customers product recommendations. This is done by tracking your clicks on their website and searching for patterns within them. This kind of data is called a clickstream data. Let us understand how it works in detail. The clickstream data contains all the logs as to how you navigated through the website. It also contains other details such as time spent on every page, etc. From this, they make use of data ingesting frameworks such as Apache Kafka or AWS Kinesis in order to store it in frameworks such as Hadoop. From there, machine learning engineers or business analysts use this data to derive valuable insights. In the next video, Kautuk will give you a brief idea on the data that is used in this case study and the kind of analysis you can perform with the same. Play Video2079378 For this assignment, you will be working with a public clickstream dataset of a cosmetics store. Using this dataset, your job is to extract valuable insights which generally data engineers come up within an e-retail company. So now, let us understand the dataset in detail in the next video. Play Video2079378 You will find the data in the link given below. https://e-commerce-events-ml.s3.amazonaws.com/2019-Oct.csv https://e-commerce-events-ml.s3.amazonaws.com/2019-Nov.csv You can find the description of the attributes in the dataset given below. In the next video, you will learn about the various implementation stages involved in this case study. Attribute Description Download Play Video2079378 The implementation phase can be divided into the following parts: Copying the data set into the HDFS: Launch an EMR cluster that utilizes the Hive services, and Move the data from the S3 bucket into the HDFS Creating the database and launching Hive queries on your EMR cluster: Create the structure of your database, Use optimized techniques to run your queries as efficiently as possible Show the improvement of the performance after using optimization on any single query. Run Hive queries to answer the questions given below. Cleaning up Drop your database, and Terminate your cluster You are required to provide answers to the questions given below. Find the total revenue generated due to purchases made in October. Write a query to yield the total sum of purchases per month in a single output. Write a query to find the change in revenue generated due to purchases from October to November. Find distinct categories of products. Categories with null category code can be ignored. Find the total number of products available under each category. Which brand had the maximum sales in October and November combined? Which brands increased their sales from October to November? Your company wants to reward the top 10 users of its website with a Golden Customer plan. Write a query to generate a list of top 10 users who spend the most. Note: To write your queries, please make necessary optimizations, such as selecting the appropriate table format and using partitioned/bucketed tables. You will be awarded marks for enhancing the performance of your queries. Each question should have one query only. Use a 2-node EMR cluster with both the master and core nodes as M4.large. Make sure you terminate the cluster when you are done working with it. Since EMR can only be terminated and cannot be stopped, always have a copy of your queries in a text editor so that you can copy-paste them every time you launch a new cluster. Do not leave PuTTY idle for so long. Do some activity like pressing the space bar at regular intervals. If the terminal becomes inactive, you don't have to start a new cluster. You can reconnect to the master node by opening the puTTY terminal again, giving the host address and loading .ppk key file. For your information, if you are using emr-6.x release, certain queries might take a longer time, we would suggest you use emr-5.29.0 release for this case study. There are different options for storing the data in an EMR cluster. You can briefly explore them in this link. In your previous module on hive querying, you copied the data to the local file system, i.e., to the master node's file system and performed the queries. Since the size of the dataset is large here in this case study, it is a good practice to load the data into the HDFS and not into the local file system. You can revisit the segment on 'Working with HDFS' from the earlier module on 'Introduction to Big data and Cloud'. You may have to use CSVSerde with the default properties value for loading the dataset into a Hive table. You can refer to this link for more details on using CSVSerde. Also, you may want to skip the column names from getting inserted into the Hive table. You can refer to this link on how to skip the headers.

A repo containing content made for students to help them learn Azure Machine Learning, integration with Jupyter Notebooks and Publishing web services in Azure ML. This content focuses around the Python programming language

This repository features Reinforcement Learning environments that are made with Unity ML-Agents to approach industrial use-cases.

Data and utilites, generated in process of appliyng ML to plant recognition task. It includes data in .csv format, scirpts for data conversion and scripts made with scikit-leatn python package.

Made with Cerebra and part of 'Beyond-transformers" This is a ML model which predicts Default risks, Very useful in the financial world, easy to run and Gives an astonishing 90%+ accuracy!

This is project management and project tracking system made in c# with the help of many libraries like Material Design, libgit, ML.NET (Machine Learning Library) and so on. Sentiment Analysis is used on this project using text classification techique

this is a simple project with html css and js. i made it just to give my little brothers a website where they can enjoy watching the last HunterxHunter episodes with no ads and no stupid redirections, it is simple and easy to use. you can check the result here https://jinatoons.ml