Today we are going to learn about the new releases from Scikit-learn version 0.22, a machine learning library in Python. We, through this video tutorial, aim to learn about the much talked about new release called Plotting API. Prior to this version, Scikit-learn did not have a function to plot the ROC curve.
A new plotting API is available for creating visualizations. The new API allows for quickly adjusting the visuals of a plot without involving any recomputation. It is also possible to add different plots to the same figure. In this tutorial we are going to study the plotting of the ROC curve.
The code sheet is provided in a Github repository here.
We will attempt to plot the ROC curve on two different algorithms and compare which one is a better function. First we choose to make a classification data. Then we go on to plot the ROC curve using SVC classifier and then further plot the curve using a random forest classifier.
On May 30, 2020 a SpaceX Falcon 9 rocket carrying Crew Dragon was launched at 3:22 p.m. US Eastern Time from the Kennedy Space Center, this being the first time a space mission was launched by NASA since it decommissioned its ageing and unsafe Space Shuttle fleet in 2011. The rocket was successful in deploying the vehicle into orbit and safely returning to Earth.
The mission
Aboard the Crew Dragon are astronauts Bob Behnken and Doug Hurley who are to be launched into the International Space Station. The mission marks the first time a private company has pulled off a crewed mission into low Earth orbit, a report said. The Crew Dragon and its self-landing, reusable Falcon 9 rocket is owned by SpaceX, who’s founder and CEO is Elon Musk. NASA just rents the spacecraft and the rocket at a cost of around $55 million per passenger, said another report.
AI in SpaceX’s mission
But did you know this historic mission is powered by the cutting edge technology of artificial intelligence? Yes! A sophisticated AI autopilot steers the cone-shaped Crew Dragon that is on its way to the ISS. Once the Crew Dragon reaches within 60 feet of the space station, the astronauts will maneuver the vehicle to the ISS and remain in space for weeks on end, depending on when they are called back. In 2018 too, a SpaceX rocket flew into space with the first robot powered by artificial intelligence.
Image Source: businessinsider.in
AI in Indian space missions
India too has been generating indigenous AI technology to power its space missions. Take for example its Chandrayaan-2 mission that was launched in July 2019. Scientists integrated AI technology with Chandryaan-2’s rover – Pragyan. A report said, the Indian Space Research Organisation delivered Pragyan – a solar-powered robotic vehicle that was to explore the lunar surface on six wheels. Moreover, “the artificial intelligence algorithm could also help the rover detect traces of water and other minerals on the lunar surface.”
How AI powers space exploration
AI helps analyze the huge amounts of data emanating from space exploration and this helps advance space exploration with each passing day. Moreover, AI is making it possible for rovers currently roving the atmosphere of Mars to take decisions independent of the mission. The NASA Curiosity rover can dodge obstacles on its route by itself and determine the best route possible. Data received from space is mainly in the form of images that are studied through machine learning techniques at the NASA Frontier Development Lab that has roped is the services of tech giants like IBM and Microsoft.
Infact, machine learning is helping in solar storm damage detection, atmosphere measurement, and determining the ‘space weather’ of a given planet through the magnetosphere and atmosphere measurement. Reports say the same technology is used in resource discovery in space. Moreover, AI applications “can optimize planetary tracking systems, enable smart data transmission, and nullify the risk of human error (by using predictive maintenance),” said this report.
So artificial intelligence is finding advanced applications is all sectors of the economy and it is clearly becoming indispensable to our lives. In India, institutes are trying hard to inculcate the science of artificial intelligence in the Indian workforce, an effort that is directly resulting in the founding of a great many challenging courses like artificial intelligence certification in Delhi NCR.
DexLab Analytics is proud to announce that its CMO, Vivek Debuka, was the Key Speaker at a webinar hosted by the Eastern Institute for Integrated Learning in Management (EIILM), Kolkata on “Changing Trend in Business in the Post COVID-19 World”.
The webinar was held on 30th May, 2020 from 5pm – 6pm. Students of the Eastern Institute for Integrated Learning in Management, the chairman and director of the EIILM Dr R P Banerjee said, were excited and eager to attend the webinar, especially because the topic was an emerging one and relevant to their corporate career goals.
On May 27, EIILM posted a Facebook post that read – “EIILM’s initiative for enriching young minds with post COVID-19 business trends!!!! The Covid era has brought about a lot of uncertainties that have resulted in a new thought process in the ever-changing world of business. To orient our budding managers with the dynamic business trends, EIILM – KOLKATA Family has scheduled a Webinar on 30 May 2020, from 5-6 pm under the title “Changing Trend in Business in the Post Covid 19 World”.
DexLab Analytics is a leading data science training institute in India with a vast array of state-of-the-art analytics courses, attracting a large number of students nationwide. It offers high-in-demand professional courses like Big Data, R Programming, Python, Machine Learning, Deep Learning, Data Science, Alteryx, SQL, Business Analytics, Credit Risk modeling, Tableau, Excel etc. to help young minds be data-efficient. It has its headquarters in Gurgaon, NCR.
Even as the coronavirus pandemic rages on and India is living through a strict lockdown to abate the spread of the novel virus, a disastrous spell of a plague of crop destroying locusts has struck Rajasthan, Gujarat and parts of Madhya Pradesh.
Threatening to balloon into an agrarian crisis, the destruction of crops on this scale is being seen as one “worst in decades”. In fact, such large scale breeding of locusts and an attack by them is the worst in 27 years, government officials said.
In such frightening circumstances, what we can truly bank upon to detect and fight locust attacks is advanced technology like machine learning techniques. This essay aims to demystify how machine learning can be used to detect locust breeding patterns by studying soil moisture through remote sensing.
The Study
Image Source
: spiedigitallibrary.org
A study called “Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture” shows how researchers have “used two machine learning algorithms (generalized linear model and random forest) to evaluate the link between hopper presences and SM (Soil Moisture) conditions under different time scenarios…It was found that an area becomes suitable for breeding when the minimum SM values are over 0.07 m3 / m3 during 6 days or more. These results demonstrate the possibility to identify breeding areas in Mauritania by means of SM, and the suitability of ESA (European Space Agency) CCI (Climate Change Initiative) SM product to complement or substitute current monitoring techniques based on precipitation datasets.”
The Findings
The study found that “it is widely assumed” that rainfall over 25 mm in two consecutive months is conducive to locust breeding. Likewise, various soil moisture conditions affect breeding patterns greatly. So, the study finds that it is important to have “variable creation as a previous step to modeling”. Different time intervals of locust breeding were tested by the researchers for model creation. Also, different soil moisture values were considered.
Image Source: spiedigitallibrary.org
It was found that the “highest performance was acquired by the RF (Random Forest) algorithm when dividing the whole survey time into ranges of 6 days, and selecting the minimum SM as the variable value.” GLMs of Generalised Linear Models, however, did not work well according to the study.
The applied methodology of machine learning offers promising results to accurately identify breeding areas based on data pertaining to 30 years of SM values. The ESA CCI soil moisture data is one of the most authoritative ones in the world. Thus the researchers who conducted this study are confident that their results signify a breakthrough in locust monitoring technique prevalence in the world.
Conclusion
This study, thus, proposes a machine learning approach based on SM time series “to predict breeding areas, by means of remote sensing”. Artificial Intelligence and Machine Learning will help future researchers and scientists to study and produce better warning systems based on the results of this study. In this study only soil moisture data has been used but more variables like temperatures can also be taken into account to accurately predict breeding grounds in the future.
For more on machine learning applications, do peruse the Dexlab Analytics website today. This article was brought to you by DexLab Analytics, a premier institute offering Machine Learning courses in Delhi.
AI is transforming our world in more ways than one with the advent of self-checkout cash registers to security checks at airports. However, in this essay, we look at how AI is transforming the world of e-Commerce.
Already, tech giants are investing heavily in AI and machine learning systems to power their startups and new sources of business. A report, infact, shows that “a recent study by Business Insider suggests that as much as 85% of customer interactions will be managed without a human by as soon as 2020.”
Here is a short list of ways in which businesses are using AI to better understand their customers, generate new leads and provide an enhanced customer experience.
Customer-centric search
Companies like Twiggle are using NLP to “narrow, contextualise and ultimately improve search results for online shoppers.” This is being done to prevent customers from abandoning searches due to a high volume of irrelevant search results showing up on screens.
Also, with enhanced visual capabilities of AI powered software, “AI is enabling shoppers to discover complementary products (by) size, colour, shape, fabric or even brand”.
Retargeting customers
Many “businesses are overloaded with unmanageable customer data that they do little or nothing with. This is an incredible goldmine of intelligence that could be used to enhance the sales cycle.”
As AI develops, it can detect customer dwell time on e-Commerce platforms and strategize marketing offers for customers. “In other words, omni-channel retailers are starting to make progress in their ability to remarket to customers.”
More efficient sales process
Customers are spending more and more time on television and social media. So, if you “want to tailor your problem-solving solutions and create a strong sales message that reaches consumers at the right time on the right platform, then integrating AI into your CRM is the way to go.”
Many AI systems enable NLP and voice input systems like Siri or Alexa that allow them to answer customer queries, solve problems and also identify new arenas of opportunities for the sales team.
Personalization across multiple devices
Personalization is the crux of e-Commerce, like you must have experienced on platforms like Amazon. However, with each passing day, AI systems are becoming more sophisticated with deeper levels of personalization penetrating the ecommerce world.
“Whether it is a mobile application, the website, or an email campaign, the AI engine is continuously monitoring all devices and channels to create a universal customer view. This unified customer view enables e-Commerce retailers to deliver a seamless customer experience across all platforms.”
Chatbots
Moving from mass-market sales to individualized marketing, many e-Commerce retailers are becoming more sophisticated with their AI capabilities “in capturing attention, and one approach widely developing is known as ‘conversational commerce’.”
“In the e-Commerce world, this is the confluence of visual, vocal, written and predictive capabilities. ”Chatbots are one such simulation system that goes a long way in building customer service relations. To read more about chatbots, do go through our previous blog on the subject.
The Covid-19 pandemic has struck India like it has scores of countries across the world. As of May 27, over 1,51,000 Indians have been tested positive for the novel virus and over 4000 people have died due to the contagious disease. India has been under lockdown for over two months now in an attempt at abating the spread of the virus due to movement and contact.
With all offices closed and work from home decreed across numerous sectors of the economy, professionals have been forced to adapt to a new mode of work and training. With more time on hand since they are working from home, professionals are upgrading their skills by taking up online training modules and classes. A recent LinkedIn survey throws light on this phenomenon.
LinkedIn’s Work Force Confidence Index
India’s foremost social networking site that helps individuals network with professional peers and find jobs and appointments has conducted a survey called Work Force Confidence Index. As per the survey conducted between April 27 and May 3, “India’s professionals are logging learning hours for not just knowledge acquisition but also to increase productivity. About half of respondents from mid-market firms joined courses that help them manage time better, improve prioritisation or stay organised”.
93% respondents to upskill online in next two weeks
According to LinkedIn News India, 1040 professionals were surveyed by LinkedIn and 93% of them said “their time spent on e-learning will either increase or remain the same over the next two weeks”. Moreover, 60% of the respondents of which 74% were from the engineering domain said e-learning was a conduit to furthering industry knowledge. “Advancing in one’s career was a driver for 57% of all respondents and 3 in 10 active job seekers undertook e-learning to make a career pivot,” said LinkedIn News India.
What respondents learnt
Of the respondents, 45% said they hoped to learn to collaborate with peers through online learning in lockdown. Also, 43% said they wished to learn to manage time and prioritise and stay organised. Moreover, 40% said they hoped to learn something unrelated to work through online platforms. Becoming a leader and managing personal finances were pegged at 37% and 32% respectively by the study as goals and 24% said e-learning could actually lead to a change in career paths for them.
Advantages of e-learning
Travelling to work and back is taxing and time consuming. When you are working from home, you save on energy and time that can be used for something productive like e-learning training modules. They are easy on the pocket, accessible from absolutely anywhere you are and convenient to absorb and retain information and new things learnt. Moreover, there is a large online community to help you out with study material and guidance.
The Amphan super cyclone last week ravaged West Bengal in India and parts of Bangladesh, killing scores of people, damaging houses, uprooting trees and electricity poles and flooding large swathes of land. The category 2 storm, with wind speeds over 150 km per hour was predicted several days earlier and this helped the concerned governments evacuate thousands of people before it struck.
Thus, we can hardly emphasise the importance of weather forecasting and prediction in today’s world, especially when climate change is at it severest. By some estimates, for instance, in 2016 there were nearly 800 weather related disaster events, thrice the number recoded in 1980. And even if all countries adhere to the Paris climate pledges, by 2100, it is likely that average global temperatures will be higher than pre industrial times by 3 degrees.
Artificial Intelligence
It is therefore imperative for us to study how Artificial Intelligence and its many branches like machine learning, deep learning and neural networks are helping predict weather events. For instance, researchers at Rice University have developed a deep learning model that can predict heat waves and winter storms, i.e. extreme weather conditions. The model was trained by studying hundreds of maps that showed surface temperatures and air pressures. After training, the model was used to read maps it had never seen before and it predicted weather conditions with upto 85 percent accuracy.
Machine Learning
Similarly, Microsoft is investing huge sums of money in ‘AI for Earth’, its flagship project committed to developing machine learning models to predict weather conditions. It has given grants to Columbia University professors to study the pattern of tree distribution in storm affected areas of America by processing thousands of images. In turn, the prediction model studies how much Carbon Dioxide decomposed vegetation is emitting into the atmosphere resulting in global warming.
Deep Neural Networks
Deep neural networks (DNNs) are gradually replacing physics based models of weather forecasting that have been in use for decades now. DNNs are being used to supplant parameterization of physical schemes in the traditional weather forecasting model in the US. DNNs help save on computational complexity in the forecasting process and help in scalability without compromising the model’s prediction accuracy. Humidity, wind velocity, temperatures and much more can be studied and predicted using DNNs.
In India
Last year the Ministry of Earth Sciences organised an event wherein officials said artificial intelligence will be used to predict extreme weather conditions in India. In an article, it was reported that a top ministry official said, “Society needs information about extreme weather events at least 7 days in advance which we are yet to achieve. We are working to provide impact based forecast to society that will tell people about how it will impact their lives. To achieve this, we are going to use artificial intelligence and machine learning to help in improving our understanding of weather and climate phenomena and their forecasting.”
Thus, it is no surprise that more and more institutes in India are gearing themselves for the AI revolution in the country. DexLab Analytics is one such artificial intelligence training institute in Gurgaon to look out for.
The world has seen a transformation in its economic activities since the coronavirus pandemic broke out. Economies have come to a grinding halt and manufacturing has dipped. Now what nations need is resilience and strength to carry on production in all sectors. What they are most depending on is the power of Artificial Intelligence to enhance the manufacturing process and help save money and drive down costs.
Here are some examples of how AI is powering the manufacturing sector in 2020.
AI is being used to transform machinery maintenance and quality in manufacturing operations today, according to Capgemini.
Caterpillar’s Marine Division is using machine learning to analyze data on how often its shipping equipment should be cleaned helping it save thousands of dollars.
The BMW Group is using AI to study manufacturing component images in and spot deviations from the standard production procedure in real-time.
In fact, a study shows that in the four earlier global economic downturns companies using AI were actually successful in increasing both sales and profit margins. Companies are all striving to utilize human experience, insights and AI techniques to give manufacturing a fillip in these times of a crisis.
Manufacturing using AI in real-time
Real-time monitoring of the manufacturing process is advantageous because it translates to sorting out production bottlenecks, tracking scrap rates and meeting customer deadlines among other things. The huge cache of data used can be utilized to build machine learning models.
Supervised and unsupervised machine learning algorithms can study multiple production shifts’ real-time data within seconds and predict processes, products, and workflow patterns that were not known before. A report suggests 29% of AI implementations in manufacturing are for maintaining machinery and production assets.
Detecting Outages
It was found that the most popular use of AI in manufacturing is predicting when equipment are likely to fail and suggesting optimal times to conduct maintenance. Companies like General Motors analyze images of its robots from cameras mounted above to spot anomalies and possible failures in the production line and thus preempt outages.
Optimizing Design
General Motors uses AI algorithms to give and produce optimized product design. General Motors can achieve the goal of rapid prototyping with the help of AI and ML algorithms. Designers provide definitions of the functional needs, raw materials, manufacturing methods and other constraints and the company along with AutoDesk has customized Dreamcatcher to optimize for weight and other vital criterion. In this way, AI comes together with human endeavor to produce a-class product designs that cost lesser.
Inconsistencies
Nokia has begun using a video application that takes the help of machine learning to alert an assembly operator if there are inconsistencies in the production process in one of its factories in Oulu, Finland. It alerts a machine operator about inconsistencies in the production of electronic items and this helps preempt poor production process and helps the company save on a lot of money and capital.
There are many other production processes AI is helping revolutionize. Only time will tell how much of AI will power the manufacturing sector. But this technological advancement is surely making an impact on economies worldwide. Meanwhile, for more details, do peruse the DexLab Analytics website. DexLab Analytics is a premiere machine learning institute in Gurgaon.
If you Google searched for “artificial intelligence” and somehow came across this article, you just made use of artificial intelligence. Or, if you hailed a cab through an app like Uber or Ola, you just made use of artificial intelligence. The science of AI is all around us, in the smallest aspects of our lives. We take a look at how AI impacts our everyday lives in amazing ways.
Google Maps and Ride-Hailing Applications
Till only recently GPS (Satellite-based navigation) was guiding us through. But now artificial intelligence has come in to revolutionize the game, enabling systems like Google Maps to know exact directions, the optimal route and even road barriers and traffic congestion. Cab hailing apps have also made use of this technology.
Face Detection and Recognition
Making use of virtual filters when taking pictures and using face ID to unlock phones are two of the applications of AI in everyday lives. The former uses face detection and the latter uses face recognition. Smart machines are taught to identify facial coordinates in pictures of faces to enable face detection and recognition features.
Text Editors or Autocorrect
When we type out something onto a word document, inbuilt auto-correct tools begin perusing our script for spelling mistakes or grammatical anomalies.
Artificially intelligent algorithms also use machine learning, deep learning, and natural language processing to identify incorrect usage of language and suggest corrections.
Search and Recommendation Algorithms
Smart recommendations systems, that power our music applications and ecommerce websites, learn user behavior and interests from online activities.
“The personalized experience is made possible by continuous training. The data is collected at the frontend (from the user), stored as big data and analyzed through machine learning and deep learning. It is then able to predict your preferences by recommendations that keep you entertained without having to search any further,” a report says.
Chatbots
Answering questions can be time consuming, especially if they are coming from a customer. Chatbots are taught to impersonate the conversational styles of human beings through NLP (Natural Language Processing) so they can answer customer queries and take and track orders. They will give the impression of a customer representative when, in fact, they are just another example of artificial intelligence.
Digital Assistants
The latest digital assistants are well acquainted with human language and incorporate advanced NLP and ML. “They understand complex command inputs and give satisfactory outputs. They have adaptive capabilities that can analyze your preferences, schedules, and habits. This allows them to systemize, organize and plan things for you in the form of reminders, prompts and schedules.”
Social Media
Various social media applications are using the support of AI to control problems like cyber crime, cyber bullying, and hate speech. “AI algorithms can spot and swiftly take down posts containing hate speech a lot faster than humans could. This is made possible through their ability to identify hate keywords, phrases, and symbols in different languages. These have been fed into the system, which has the additional capability to add neologisms to its dictionary. The neural network architecture of deep learning is an important component of this process.”
