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The AI and machine learning industries are booming like never before. As of 2021, the increase in AI usage across businesses will create US$2.9 trillion in business value. AI has automated many industries across the globe and changed the way they operate. Most large companies incorporate AI to maximize productivity in their workflow, and industries like marketing and healthcare have undergone a paradigm shift due to the consolidation of AI.

Time-series forecasting is a machine learning technique used very often in the industry. The use of past data to predict future sales has a large number of business use cases. The Kaggle Demand Forecasting dataset can be used to practice this project. This dataset has 5 years of sales data, and you will need to predict sales for the next three months. There are ten different stores listed in the dataset, and there are 50 items at each store. To predict sales, you can try out various methods ARIMA, vector autoregression, or deep learning. One method you can use for this project is to measure the increase in sales for each month and record it. Then, build the model on the difference between the previous month and the present month sales. Taking into account factors like holidays and seasonality can improve the performance of your machine learning model.

A customer service chatbot uses AI and machine learning techniques to reply to customers, taking the role of a human representative. A chatbot should be able to answer simple questions to satisfy customer needs.

There are presently three kinds of chatbots that you can build:

An NLP chatbot is an interesting machine learning project idea. You will need an existing corpus of words to train your model on, and you can easily find Python libraries to do this. You can also have a pre-defined dictionary with a list of question-and-answer pairs youd like to train your model.

If you live in an area with frequent wild-animal sightings, it is helpful to implement an object detection system to identify their presence in your area. Follow these steps to build a system like this:

Microsoft has built an Image Recognition API using data collected from wildlife cameras. They released an open-source pre-trained model for this purpose called a MegaDetector. You can use this pre-trained model in your Python application to identify wild animals from the images collected. It is one of the most exciting ML projects mentioned so far and is pretty simple to implement due to the availability of a pre-trained model for this purpose.

Spotify uses AI to recommend music to its users. You can try building a recommender system based on publicly available data on Spotify. Spotify has an API that you can use to retrieve audio data you can find features like the year of release, key, popularity, and artist. To access this API in Python, you can use a library called Spotify. You can also use the Spotify dataset on Kaggle that has around 600K rows. Using these datasets, you can suggest the best alternative to each users favorite musician. You can also come up with song recommendations based on the content and genre preferred by each user. This recommender system can be built using K-Means clustering similar data points will be grouped. You can recommend songs with a minimal intra-cluster distance between them to the end-user. Once you have built the recommender system, you can also turn it into a simple Python app and deploy it. You can get users to enter their favorite songs on Spotify, then display your model recommendations on the screen that have the highest similarity to the songs they enjoyed.

Market Basket Analysis can help companies identify hidden correlations between items that are frequently bought together. These stores can then position their items in a way that allows people to find them easier. You can use the Market Basket optimization dataset on Kaggle to build and train your model. The most commonly used algorithm used to perform Market Basket analysis is the Apriori algorithm.

The dataset has variables that include start and end coordinates of a taxi trip, time, and the number of passengers. The goal of this ML project is to predict trip duration with all these variables. It is a regression problem. Variables like time and coordinates need to be pre-processed appropriately and converted into an understandable format. This project isnt as straightforward as it seems. This dataset also has some outliers that make prediction more complex, so you will need to handle this with feature engineering techniques. The evaluation criteria for this NYC Taxi Trip Kaggle Competition is RMSLE or the Root Mean Squared Log Error. The top submission on Kaggle received an RMSLE score of 0.29, and Kaggles baseline model has an RMSLE of 0.89. You can use any regression algorithm to solve this Kaggle project, but the highest performing competitors of this challenge have either used gradient boosting models or deep learning techniques.

In this project, you can use machine learning techniques to distinguish between spam (illegitimate) and ham (legitimate) messages. To achieve this, you can use the Kaggle SMS spam collection dataset. This dataset contains a set of approximately 5K messages that have been labeled as spam or ham. To build the machine learning model, you first need to pre-process the text messages present in Kaggles SMS spam collection dataset. Then, convert these messages into a bag of words so that they can easily be passed into your classification model for prediction.

You can create an app to predict a users personality type based on what they say. The Myers-Briggs type indicator categorizes individuals into 16 different personality types. It is one of the most popular personality tests in the world. If you try to find your personality type on the Internet, you will find many online quizzes. After answering around 2030 questions, you will be assigned to a personality type. However, in this project, you can use machine learning to predict anyones personality type just based on one sentence.

Here are the steps you can take to achieve this:

You can build an app that recognizes a users mood based on live web footage and a movie suggestion based on the users expression.

To build this, you can take the following steps:

In this project, you can create a dashboard analyzing the overall sentiment of popular YouTubers. Over 2 billion users watch YouTube videos at least once a month. Popular YouTubers garner hundreds of billions of views with their content. However, many of these influencers have come under fire due to controversies in the past, and public perception is constantly changing. You can build a sentiment analysis model and create a dashboard to visualize sentiments around celebrities over time.

To build this, you can take the following steps:

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Top 10 Machine Learning Projects to Boost Your Resume - Analytics Insight

Google and Amazon Web Services (AWS) have highlighted their respective work on machine-learning (ML) models that may help nations deal with environmental crises happening with increasing regularity across the world.

The companies flagged up their efforts to tackle climate change effects such as floods and wildfires as the UN Climate Change Conference UK 2021 (COP26) wraps up this week.

Google has published a non-peer-reviewed paper about its flood forecasting system with machine-learning models that it claims provide "accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large, gauged rivers". The paper was written by researchers at Google Research and the Hebrew University of Jerusalem in Israel.

SEE: Report finds startling disinterest in ethical, responsible use of AI among business leaders

Google's flood-forecasting initiative, launched in 2018, sends alerts to smartphones of people in flood-affected areas. It's part of Google's Crisis Response program, which works with front-line and emergency workers to develop technology.

Since 2018, the program has expanded to cover much of India and Bangladesh, encompassing an area populated by some 220 million people. As of the 2021 monsoon season, this has further expanded to cover an area where 360 million people live.

"Thanks to better flood prediction technology, we sent out over 115 million alerts -- that's about triple the amount we previously sent out," says Yossi Matias, Google's VP engineering and crisis response lead,in a blogpost.

Google's alerts don't just indicate how many centimetres a river will rise. Thanks to its new machine-learning models that use Long Short-Term Memory (LTSM) deep neural networks, it can now provide "inundation maps" that show the extent and depth of flooding as a layer on Google Maps.

The researchers contend that "LSTM models performed better than conceptual models that were calibrated to long data records in every basin".

"While previous studies provided encouraging results, it is rare to find actual operational systems with ML models as their core components that are capable of computing timely and accurate flood warnings," Google's researchers said.

AWS, meanwhile, has been working with AusNet, an energy company based in Melbourne, Australia, to help mitigate bushfires in the region.

AusNet has 54,000 kilometres of power lines that distribute energy to about 1.5 million homes and businesses in Victoria. It's estimated that 62% of the network is in high bushfire risk areas.

AusNet has been using cars equipped with Google Maps-style LiDAR cameras and Amazon SageMaker machine learning to map out the state's vegetation areas that need to be trimmed to stem bushfire threats. Its previous system relied on a GIS (Geographic Information System) and used custom tools to label LiDAR points.

AusNet worked with AWS to automate the classification of LiDAR points by using AWS's managed deep-learning models, GPU instances and S3 storage.

AusNet and AWS built a semantic segmentation model that accurately classified 3D point cloud data for conductors, buildings, poles, vegetation, and other categories, AWS notes in a blogpost.

SEE:What is digital transformation? Everything you need to know about how technology is reshaping business

"The team was able to train a model at a rate of 10.8 minutes per epoch on 17.2 GiB of uncompressed data across 1,571 files totaling approximately 616 million points. For inference, the team was able to process 33.6 GiB of uncompressed data across 15 files totaling 1.2 billion points in 22.1 hours. This translates to inferencing an average of 15,760 points per second including amortized startup time," AWS states.

"Being able to quickly and accurately label our aerial survey data is a critical part of minimizing the risk of bushfires," says Daniel Pendlebury, a product manager at AusNet.

"Working with the Amazon Machine Learning Solutions Lab, we were able to create a model that achieved 80.53% mean accuracy in data labeling. We expect to be able to reduce our manual labeling efforts by up to 80% with the new solution."

The rest is here:
Google and AWS harness the power of machine learning to predict floods and fires - ZDNet

COLUMBIA, Md. & DENVER, November 15, 2021--(BUSINESS WIRE)--BigBear.ai, a leading provider of artificial intelligence, machine learning, big data analytics, and cyber solutions, and Palantir Technologies Inc. (NYSE: PLTR), a software company that builds enterprise data platforms for use by organizations with complex and sensitive data environments, today announced that they have entered into a commercial partnership under which BigBear.ais and Palantirs products will be integrated to extend the operating system for the modern enterprise with data and AI that provide advice and other actionable insights for complex business decisions.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20211115005507/en/

As part of the integrated product offering, Palantirs Foundry platform will be integrated with BigBear.ais Observe, Orient and Dominate products, creating powerful machine learning extensions for the Palantir ecosystem that will provide global data collection, generate actionable insights and deliver anticipatory intelligence at enterprise scale to address high-growth federal and commercial verticals including space, retail, logistics and energy.

BigBear.ai will have an opportunity to extend Palantirs products with its forecasting, course of action optimization, conflation, computer vision, natural language processing, and other predictive analytics via low-code interfaces. Building upon the agility and scalability of Palantirs Foundry data and analytics fabric, BigBear.ais products will enable businesses to achieve return on investment faster with out-of-the-box optimization solutions for pricing, inventory and asset allocation, facility and operations management, and customer targeting all built to be sensitive to todays connected economy through the inclusion of BigBear.ais global data for situational awareness and competitive intelligence.

The parties also will explore taking joint products to market, which the companies anticipate would rapidly increase Palantirs addressable opportunities and accelerate BigBear.ais roadmap and sales channel. For example, exploring how BigBear.ais commercial space solutions could be deployed together with Palantir products in the federal government space. BigBear.ais near real-time observations of places, events, and other entities could be easily disseminated to Palantir customers and tied into business process automations and analytics.

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Brian Frutchey, BigBear.ai Chief Technology Officer, said, "We are thrilled to partner with Palantir to deliver a more robust range of capabilities to our respective customer bases at a time in which demand for AI and ML solutions is growing rapidly. We are confident that this partnership will accelerate BigBear.ais penetration into high growth markets, including commercial markets and the Federal government, and help us expand our existing customer relationships as well as attract new customers at this critical stage of expansion for BigBear.ai."

Akash Jain, President of Palantir USG, said, "We see immense opportunities to deliver more, faster for customers by partnering with cutting edge companies who can leverage Foundry as Infrastructure in their offerings. BigBears unique AI capabilities can achieve scalable distribution across government and commercial customers alike through Apollo and Foundry."

About BigBear.ai

A leader in decision dominance for more than 20 years, BigBear.ai operationalizes artificial intelligence and machine learning at scale through its end-to-end data analytics platform. The Company uses its proprietary AI/ML technology to support its customers decision-making processes and deliver practical solutions that work in complex, realistic and imperfect data environments. BigBear.ais composable AI-powered platform solutions work together as often as they stand alone: Observe (data ingestion and conflation), Orient (composable machine learning at scale), and Dominate (visual anticipatory intelligence and optimization).

BigBear.ais customers, which include the U.S. Intelligence Community, Department of Defense, the U.S. Federal Government, as well as customers in the commercial sector, rely on BigBear.ais high value software products and technology to analyze information, identify and manage risk, and support mission critical decision making. Headquartered in Columbia, Maryland, BigBear.ai has additional locations in Virginia, Massachusetts, Michigan, and California. For more information, please visit: http://bigbear.ai/ and follow BigBear.ai on Twitter: @BigBearai.

About Palantir Technologies Inc.

Palantir Technologies Inc. builds and deploys operating systems for the modern enterprise. Additional information is available at http://www.palantir.com.

Who dares, wins.

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements may relate to, but are not limited to, Palantirs expectations regarding the strategy, terms, and the expected benefits of the commercial partnership, product development or integration efforts, and customer opportunities. Forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified. Forward-looking statements are based on information available at the time those statements are made and were based on current expectations as well as the beliefs and assumptions of each partys management as of that time with respect to future events. These statements are subject to risks and uncertainties, many of which involve factors or circumstances that are beyond the parties control. These risks and uncertainties include the parties ability to meet the unique needs of their respective or joint customers; the parties ability to successfully market or sell their products and services to new or existing customers; the failure of the parties products, individually or as integrated, to satisfy their customers or perform as desired; the frequency or severity of any software and implementation errors; the reliability of the parties products, including any integrated product offerings; the ability to modify or terminate the parties commercial partnership; and customers ability to modify or terminate their contracts. Additional information regarding these and other risks and uncertainties with respect to Palantir is included in the filings Palantir makes with the Securities and Exchange Commission from time to time. Except as required by law, the parties do not undertake any obligation to publicly update or revise any forward-looking statement, whether as a result of new information, future developments, or otherwise.

View source version on businesswire.com: https://www.businesswire.com/news/home/20211115005507/en/

Contacts

For BigBear.ai ReevemarkPaul Caminiti/Delia Cannan/Pam Greene212-433-4600bigbear.ai@reevemark.com

or

Lambert & Co.Jennifer Hurson(845) 507-0571jhurson@lambert.com

Caroline Luz203-656-2829cluz@lambert.com

For Palantir Lisa Gordonmedia@palantir.com

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BigBear.ai And Palantir Announce Strategic Partnership, Combining AI-powered Products With Next Generation Operating Platform - Yahoo Finance

Shankar Arumugavelu is what you might call a Verizon lifer. He was a director at telecom GTE when Bell Atlantic acquired it in 2000 to form Verizon. Today hes SVP and global CIO of Verizon, where hes helping to drive the companys adoption of emerging technologies like AI and machine learning in service of creating competitive advantage and improving customer experience.

As we look at emerging technologies, AI is a big area of focus, Arumugavelu says. You have disciplines within AI as well, whether its NLP or computer vision, robotic process automation, cognitive decisioning, etc. We have work going on across every single one of those disciplines to see how we can leverage that to drive a competitive advantage.

Arumugavelu and his team evaluate technologies based on multiple criteria, but the ability to drive operational efficiency and to deliver a differentiated customer experience are two of the most important factors.

When we talk AI and machine learning, these are technologies that have been there for many, many years. Its just that now the time has come, he says.

Data is the raw material that powers all these technologies, and Arumugavelu says Verizon has no paucity of it. Along with the growing volumes of data, theres been a steady decrease in the cost of compute, greater accessibility of AI and machine learning research and algorithms, and increasing availability of tools to help democratize data.

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Verizon CIO Shankar Arumugavelu on putting emerging technologies to work - CIO