advanced-prompt-engineering-techniques-3817061

This repo is for LinkedIn Learning course: Advanced Prompt Engineering Techniques

ai-102-aiengineer

Lab files for AI-102 - AI Engineer

ailab

Experience, Learn and Code the latest breakthrough innovations with Microsoft AI

airlinepassengerprediction

Need to predict how many passengers are going to opt for the airline base on the historical information provided by the Airlines. Using various Time series techniques predicted the number of passengers

arm

Basic ARM Templates

artificial-nose

Instructions, source code, and misc. resources needed for building a Tiny ML-powered artificial nose.

azure-computer-vision-in-a-day-workshop

Azure Computer Vision workshop in a day

best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

carprice_r

Basic Linear Regression model in R language

carpriceprediction

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know: Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the Americal market.

creditcardfrauddetection

Although digital transactions in India registered a 51% growth in 2018-19, their safety remains a concern. Fraudulent activities have increased severalfold, with around 52,304 cases of credit/debit card fraud reported in FY'19 alone. Due to this steep increase in banking frauds, it is the need of the hour to detect these fraudulent transactions in time in order to help consumers as well as banks, who are losing their credit worth each day. Machine learning can play a vital role in detecting fraudulent transactions. Imagine you get a call from your bank, and the customer care executive informs you that your card is about to expire in a week. Immediately, you check your card details and realise that it will expire in the next 8 days. Now, in order to renew your membership, the executive asks you to verify a few details such as your credit card number, the expiry date and the CVV number. Will you share these details with the executive? In such situations, you need to be careful because the details that you might share with them could grant them unhindered access to your credit card account.The aim of this project is to predict fraudulent credit card transactions using machine learning models. The data set that you will be working on during this project was obtained from Kaggle. It contains thousands of individual transactions that took place over a course of two days and their respective labels.

creditdefaultanalysis

This case study aims to identify patterns which indicate if a client has difficulty paying their instalments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. This will ensure that the consumers capable of repaying the loan are not rejected. Identification of such applicants using EDA is the aim of this case study. In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment. To develop your understanding of the domain, you are advised to independently research a little about risk analytics - understanding the types of variables and their significance should be enough).

dogshoespricing

Simple linear regression,exporting model and using the same to alert users

dogstemparatureprediction

This notebook helps us predict the temperature of the dogs using simple regression, multiple regression and polynominal regression

dollinearregressioninsideout

Case study covered during Microsoft Devices Day of Learning : Linear Regression Inside - Out session

eda_py

This notebook is a lab from the Microsoft learning courses.

feature-engineering-for-machine-learning

Code repository for the online course Feature Engineering for Machine Learning

fnltweetanalysis

Supply chain as a industry saw 6X more disruptions in 2021 than any of the previous years.  Deloitte says 56 % of the companies are already using external data to understand how the external factors are influencing the industry they are in and its also said that the companies are planning to increase investments in getting and analyzing the external data This project is one such attempt to enable Supply Chain team of Microsoft Devices with the latest happenings around the world that could disrupt the flow of operations and cause delays in our supply chain using external data. Whenever a user has posted on public social media like twitter.com using any of the general keywords which we track, the tweet would be scraped immediately with in 5 mins. Once the tweet is fed into the data pool, the tweet gets analyzed for the language, sentiment, key phrases, translate if not in English, and finally gets relayed into a real time Power BI report which is readily available for the Supply chain team   

fundprioritzation

HELP International is an international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. It runs a lot of operational projects from time to time along with advocacy drives to raise awareness as well as for funding purposes. After the recent funding programmes, they have been able to raise around $ 10 million. Now the CEO of the NGO needs to decide how to use this money strategically and effectively. The significant issues that come while making this decision are mostly related to choosing the countries that are in the direst need of aid. And this is where you come in as a data analyst. Your job is to categorise the countries using some socio-economic and health factors that determine the overall development of the country. Then you need to suggest the countries which the CEO needs to focus on the most. The datasets containing those socio-economic factors and the corresponding data dictionary are provided below.

housepriceprediction

A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below. The company is looking at prospective properties to buy to enter the market. You are required to build a regression model using regularisation in order to predict the actual value of the prospective properties and decide whether to invest in them or not. The company wants to know: Which variables are significant in predicting the price of a house, and How well those variables describe the price of a house.

indiagdpanalysis

Analysis of the India GDP over years and provide the understanding of contribution of different sectors to the GDP

mastering-github-copilot-for-paired-programming

A 6 Lesson course teaching everything you need to know about harnessing GitHub Copilot and an AI Paired Programing resource.

ml-for-beginners

12 weeks, 24 lessons, classic Machine Learning for all

multivariate-lstm

natbot

Drive a browser with GPT-3

neuralnetworksanddeeplearning

A introduction to neural networks and the process that goes behind them using a image classification problem

openai-cookbook

Examples and guides for using the OpenAI API

python-training

Python training for business analysts and traders

sakusuma

salesleadscoring

An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses. The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%. Now, although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, say, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone.