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A Guide to Free Ebooks on Statistics and Machine Learning

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A Guide to Free Ebooks on Statistics and Machine Learning

Machine Learning is an acquired knowledge science. It has to be taught and studied. For this, it is imperative to have the best books on the subject at hand. However, most books on the subject are expensive and not easily accessible. This is only fair given the amount of hard word that goes into writing these books.

In a situation this critical, it is best to rely on the good old Internet for assistance. There are some good Samaritans who have chosen to make their works freely available to all. Here is a great guide to free ebooks available online so you can brush up on your concepts and be industry ready at the earliest.

Think Stats – Probability and Statistics for Programmers by Allen B Downey

For the free ebook click here http://www.greenteapress.com/thinkstats.

This is an introduction to statistics and probability for those who have a basic grounding in Python programming. “It’s based on a Python library for probability distributions (PMFs and CDFs). To make things easier for the reader, most of the exercises have short programs,” says a report.

Bayesian Reasoning and Machine Learning by David Barber

For the free ebook click here http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/091117.pdf.

When it comes to Bayesian statistics, this book is a classic. “This takes a Bayesian statistics approach to machine learning.”This is a book worth checking out for anyone getting into the machine learning field and trying to make a career out of the subject.

An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

For the free ebook click here http://faculty.marshall.usc.edu/gareth-james.

This popular entry is an introduction to data science through machine learning. “This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to this field. It’s filled with practical real-world examples of where and how algorithms work. For those with an inclination towards R programming, this book even has practical examples in R.”

Understanding Machine Learning by ShaiShalev-Shwartz and Shai Ben-David

For the free ebook click here https://www.cse.huji.ac.il/~shais/UnderstandingMachineLearning/index.html.

“This book gives a structured introduction to machine learning. It looks at the fundamental theories of machine learning and the mathematical derivations that transform these concepts into practical algorithms. Following that, it covers a list of ML algorithms, including…stochastic gradient descent, neural networks, and structured output learning.”

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A Programmer’s Guide to Data Mining by Ron Zacharski

For the free ebook click here http://guidetodatamining.com.

This book has chapters covering recommendation systems. “It takes a…visually entertaining look at social filtering and item-based filtering methods and how to use machine learning to implement them. Other concepts like Naive Bayes and Clustering are also covered. There is a chapter on Unstructured Text and how to deal with it, in case you are thinking about getting into Natural Language Processing. Examples in Python are also available in case you want to practice.”

For more on Machine Learning do peruse the DexLab Analytics website. DexLab Analytics is a premiere institute offering Machine Learning courses in Delhi.

 


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How AI Is Facilitating Digital Marketing

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How AI Is Facilitating Digital Marketing

Artificial Intelligence has transformed the world of digital marketing by making it ultra intelligent and intuitive. Almost every platform used by the digital marketer is powered by some form of AI or an AI-powered machine learning model.

If we were to define what AI marketing is, according to a report by Forbes, it is a method of leveraging technology to improve the customer journey. It can also be used to boost the return on investment (ROI) of marketing campaigns.

How AI Works In Marketing Strategies

AI plays a very important role in eliminating guesswork when it comes to customer interactions online like in email marketing. Big Data Analytics, machine learning and other related processes gain insights into target audience behaviour. “With these insights, you can create more effective customer touch points.”

Moreover, it is gradually automating processes that were once dependent on human beings. Content generation, PPC ads, and even web design and video marketing are all possible applications for AI marketing.

Marketing Campaigns

AI, in the world of digital marketing, can streamline and optimize marketing campaigns. “It can also eliminate the risk of human error”. It acts as a support system to shore up efforts born out of human ingenuity with its data driven reports and analyses.

While AI might be able to launch a marketing campaign all on its own, human attributes like empathy, compassion and the art of storytelling are still needed to shape up the soul of an online marketing campaign.

Content Generation And Curation

“At present, content marketing has ballooned into a global industry. It’s so prevalent that some refer to it as the only type of marketing.” Moreover, AI powered content marketing strategies are also becoming a rage.

AI can be used potentially for both curating and generating content. Already, companies are using AI for automated content generation at a basic level. But in the long run, “AI could generate viable topics for writers, or even develop initial drafts of content based on certain parameters”.

Digital Advertising

AI is also gradually transforming the way businesses advertise. In fact, today’s digital advertising strategies all have a basic level of AI powered models processing them.

AI works with the help of algorithms in its systems. “These systems operate autonomously, placing the right kinds of ads in front of the right kinds of people based on complex algorithms and big data.” This feature service is known as “programmatic advertising.”

Chatbots

Chatbots have become the latest game changer when it comes to the marketing industry. They are the first interface customers encounter on many websites today, giving the website a human touch, excelling at answering customers’ frequently asked questions.

“The key fascination with chatbots is the impact they can have on the customer experience. For some businesses, there aren’t enough employees or hours in the day to answer customer queries quickly. Chatbots allow customers to help themselves.”

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Behavior Analysis And Predictive Analytics

More and more companies are beginning to hire data scientists and programmers for their marketing departments. There “are so many data sets (on the Internet) that humans alone can’t possibly hope to analyze them all.”

“Using machine learning and big data analysis, AI is able to provide businesses with deep insights into their customer(s’ behaviour). Not only will businesses be able to hyper-personalize interactions, but…they’ll also be able to predict future customer behaviours based on the data collected.”

For more information on AI powered systems, do peruse the DexLab Analytics website today. DexLab Analytics is a premiere institute that offers artificial intelligence certification in Delhi NCR.

 


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Bayes’ Theorem – Application in R and Python

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Bayes’ Theorem – Application in R and Python

Bayes’ theorem, named after 18th century (1763) British mathematician Thomas Bayes, is a mathematical formula for determining conditional probability.  In the discussion of conditional probability we indicated that revising probability when new information is obtained is an important phase of probability analysis. Often, we begin our analysis with initial or prior probability estimates for specific events of interest. Then, from sources such as a sample, a special report, a product test, etc we obtain some additional information about the events. Given this new information, we update the prior probability values by calculating revised probabilities, referred to as posterior probabilities.

The steps involved in this probability revision process are depicted in the digram below:

  • Theorem:

An event A can occur only if one of the mutually exclusive and exhaustive set of events B1, B2,… ,Bn occurs. Suppose that the unconditional probabilities

And the conditional probabilities

are known. Then the conditional probability of a specified event Bi, when A is stated to have actually occurred, is given by

This is known as Bayes’ Theorem.

  • Proof:

An event A can happen in mutually exclusive ways, B1 A, B2A,… Bn A, i.e. either when has occurred, or. So by the theorem of total probability

 

Again,

Since the events ABi and BiA are equivalent, their probabilities are also equal.

Hence

So that

Substituting for P(A) from above, the theorem is proved.

Equation (1) is also known as “Bayes” formula for calculating probabilities of hypothesis. Because B1, B2,…Bn may be considered as hypothesis which account for the occurrence of A. The probabilities P(B1),P(B2 ),…P(Bn) are called ‘a prior’ probabilities of the hypothesis.

While are known as a‘a posteriori’ probabilities of the same hypothesis.

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For more on this, do peruse the Dexlab Analytics website today. Dexlab Analytics is a premiere institute for R programming courses in Gurgaon.

 


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Top Six Applications of Natural Language Processing (NLP)

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Top Six Applications of Natural Language Processing (NLP)

Words are all around us – in the form of spoken language, texts, sound bytes and even videos. The world would have been a chaotic place had it not been for words and languages that help us communicate with each other.

Now, if we were to enhance language with the attributes of artificial intelligence, we would be working with what is known as Natural Language Processing or NLP – the confluence of artificial intelligence and computational linguistics.

In other words, “NLP is the machine’s ability to process what was said to it, structure the information received, determine the necessary response and respond in a language that we understand”.

Here is a list of popular applications of NLP in the modern world.

1. Machine Translation

When a machine translates from one language to another, “we deal with “Machine” Translation. The idea behind MT is simple — to develop computer algorithms to allow automatic translation without any human intervention. The best-known application is probably Google Translate.”

2. Voice and Speech Recognition

Though voice recognition technology has been around for 50 years, it is only in the last few decades, owing to NLP, have scientists achieved significant success in the field. “Now we have a whole variety of speech recognition software programs that allow us to decode the human voice,”be it in mobile telephony, home automation, hands-free computing, virtual assistance and video games.

3. Sentiment Analysis

“Sentiment analysis (also known as opinion mining or emotion AI) is an interesting type of data mining that measures the inclination of people’s opinions. The task of this analysis is to identify subjective information in the text”. Companies use sentiment analysis to keep abreast of their reputation and customer satisfaction.

4. Question Answering

Question-Answering concerns building systems that “automatically answer questions posed by humans in a natural language”. The real examples of Question-Answering applications are: Siri, OK Google, chat boxes and virtual assistants.

5. Automatic Summarization

Automatic Summarization is the process of creating a short, accurate, and fluent summary of a longer text document. The most important advantage of using a summary is it reduces the time taken to read a piece of text. Here are some applications – Aylien Text Analysis, MeaningCloud Summarization, ML Analyzer, Summarize Text and Text Summary.

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6. Chatbots

Chatbots currently operate on several channels like the Internet, web applications and messaging platforms. “Businesses today are interested in developing bots that can not only understand a person but also communicate with him at one level”.

While such applications celebrate the use of NLP in modern computing, there are some glitches that arise in systems that cannot be ignored. “The very nature of human natural language makes some NLP tasks difficult…For example, the task of automatically detecting sarcasm, irony, and implications in texts has not yet been effectively solved. NLP technologies still struggle with the complexities inherent in elements of speech such as similes and metaphors.”

To know more, do take a look at the DexLab Analytics website. DexLab Analytics is a premiere institute that trains professionals in NLP deep learning classification in Delhi.

 


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How Pharma Companies Are Using Machine Learning and AI to Fight Against COVID-19

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How Pharma Companies Are Using Machine Learning and AI to Fight Against COVID-19

In the midst of a crisis as big as COVID-19 which has as per the current update taken more than 80,000 lives around the world and caused nations to go under a lockdown, machine learning and artificial intelligence has played a vital role in detecting COVID-19 positive cases and helping fast track the tests and analysis of drugs that can be a solution to the problem.

Countries around the world are building algorithms to better understand the molecular structure of the diseaseso that the so-called protein around the SARS-CoV-2, the virus that causes COVID-19, can be blocked.

Use of AI and machine learning around the world

The legendary story of a Canada-based AI named Bluedot is not something one can forget. Buledot is a low cost AI based tool that predicted the outbreak of the disease in December 2019 and also predicted the top 20 destinations where passengers from Wuhan, the origin of pandemic, would arrive.

Deargen a South-Korean company built an AI tool named MT-DTI to test and analyze the molecular bond of the SARS-CoV-2 protein and recommend medication on the basis of the same. Even though their recommendations are yet to be reviewed and tested properly, their find might prove to be a super solution to reduce the number of death counts and covid positive cases.

Insilico Medicine, a Hong-Kong based company instead of building a platform to recommend the currently available medication to break the protein of covid-19 virus and stop it from replicating, built an AI based tool to formulate and test the potency of new and chemically advanced medication to break the chain.

Benevolent AI, a British startup based on its machine learning tool discovered six important covid-19 virus fighting compounds that can prove to be helpful in curing and limiting the spread of the disease. Out of the six compounds discovered, ‘baricitinib’, a compound used to cure rheumatoid arthritis according to the sources, proves to be the safest compound that can be tested on the covid positive volunteers.

Use of AI and machine learning in India

The spread of the covid-19 pandemic in India began by the end of January 2020 and till date more than 5000 cases have been reported. The Government of India declared a lockdown by mid-March 2020 and in the middle of all this when misinformation and rumors were rising day by day, to tackle the problem the Government of India launched its covid-19 tracking app called Aarogya Setu that means ‘a bridge of health’.

This AI-based app uses bluetooth and the mobile location of the user and informs them if there is any person covid positive within 6 feet of their location or not. The app checks the user’s data in their own database and in case the data matches a corona positive patient then it only notifies their presence keeping their identity anonymous.

India has also launched a whatsapp chatbot and any one using the whatsapp platform can access the chatbot by simply typing “MyGov Corona Helpdesk” and get the relevant and authentic information relating to the symptoms and how they can get help from direct sources.

Apart from the above initiatives to tackle the pandemic, medical practitioners, data analysts and data scientists from around the world are trying to find ways and means and analyzing the database to capture the trends and are working day and night to find best possible cure to fight the disease.

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To know more, do read DexLab Analytics previous blogs on how AI is helping fight the coronavirus pandemic here. DexLab Analytics is a premiere artificial intelligence training institute in Gurgaon that gives Machine Learning training in Gurgaon.

 


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AI’s Fight Against Coronavirus Continues

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AI’s Fight Against Coronavirus Continues

The world has come to a grinding halt with the spread of the deadly novel coronavirus (COVID-19), one of the most contagious diseases to affect us as a people of late.

In little over three months since the virus was first detected in China’s Wuhan late last year, it has spread to more than 90 countries, infected almost a million people, and taken about48,000 lives as of Thursday, April 2, 2020.

Scientists, governments and health organizations are doing the best they can to contain and fight the disease and they are trying to take all the help they can. Including the help of technology – and Artificial Intelligence – in tracking, diagnosing cases, disinfecting areas and speeding up the hunt for a cure.

Here is a short essay on how artificial intelligence is assisting in the fight against COVID-19.“Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak,” says a report.

Tracking the virus outbreak with machine learning

In December last year, a Canada-based artificial intelligence platform that tracks the spread of infectious diseases around the world, detected a cluster of “unusual pneumonia” cases reported in China’s Wuhan. You can read about the detection of the disease here.

The Toronto-based start-up uses Natural Language Processing or NLP and machine learning algorithms to scour tons of information on infectious diseases from sources like statements from health organizations, commercial flights and live stock health reports. It alerted its clients about the outbreak of the disease almost a week before international health organizations declared the outbreak of COVID-19 in China.

Computer Vision being used to detect coronavirus infection

As of today, authorities the world over are checking temperatures of their citizens at airports and railway stations or other crowded places through thermal guns and manually screening them for signs of fever, cough and breathing difficulties.

However, computer vision algorithms can accelerate this crucial screening process by equipping cameras with computer vision technology.

In China, a tech giant has invented a thermal scanner that uses computer vision and infrared sensors to check people’s temperatures in public places. The system invented can check up to 200 people per minute.

Another tech giant has invented a system that uses AI to detect coronavirus in chest CT scans (in 20 seconds as opposed to the 15 minutes spent by human health workers) with a 96 per cent accuracy rate. The system is reportedly being adopted by 100 hospitals in China.

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AI is speeding up drug research

Developing new drugs and vaccines, in the race to win the fight against coronavirus, is proving long and tedious. Some reports suggest it can take anywhere near 12 years at a cost of billions of dollars.

However, that said, AI will help speed up the process of drug discovery to some extent. “DeepMind, the AI research lab acquired by Google in 2014, recently declared that it has used deep learning to find new information about the structure of proteins associated with COVID-19”, a process that could have taken many more months.

“Understanding protein structures can provide important clues to the coronavirus vaccine formula. DeepMind is one of several organizations engaged in the race to unlock the coronavirus vaccine. It has leveraged the result of decades of machine learning progress as well as research on protein folding.”

To know more about how AI is helping in the fight against the novel coronavirus, do read DexLab Analytics’ previous blog on the topic. DexLab Analytics is a premiere Machine Learning institute in Gurgaon.

 


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How AI is used in Banking

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How AI is used in Banking

Artificial Intelligence has revolutionized the banking sector, transforming back end processes into faster mechanisms, making money transfers safer and back-end operations more efficient.

From fraud detection to customer service chatbots, AI is powering several banking wings. Here is a list of operations AI has been facilitating in banks across the world.

Customer Support & Front Office

Millennials have rendered brick and mortar banks dispensable. According to Business Insider, 40 per cent of this generation does not use the services extended in physical bank offices.

AI has come to the rescue, however, by powering chatbots and voice assistants in most major financial institutions. Kasisto, for example, is one such neo-banking institution that has been using AI to the hilt.

“Kasisto’s major contribution is its conversational AI platform, KAI, which banks can use to build their own chatbots and virtual assistants. It’s rooted in AI reasoning and natural-language understanding and generation, which means it can handle sophisticated questions about finance management that other bank customer-service digital assistants…can’t”, says a report.

Kasisto has supported and shored up AI assistants for several reputed banking institutions including the UAE-based digital bank Liv., DBS Bank, Standard Chartered Bank and TD.

“The bank’s KAI-based bot walks customers through how to make international transfers, block credit card charges and transfer you to human help when the bot hits a wall.”

Fraud Protection & Middle Office

Artificial Intelligence has truly transformed “middle office functions” – where banks manage risk and protect themselves from bad actors. These functions include fraud detection, anti-money laundering initiatives and customer identity verification.

“And sometimes that means incorporating AI into legacy, rules-based anti-fraud platforms. But some the most innovative and secure countermeasures are other, from-the-ground-up models, built by companies like the ones below.”

“Up to $2 trillion is laundered every year — or five percent of the global GDP, according to UN estimates.” The sheer number of investigations across the globe, coupled with the complexity of data and reliance on human involvement makes anti-money laundering (AML) difficult work.

AML processes also cost a lot. Ayasdi’s AI-powered AML incorporates three key advancements: “intelligent segmentation, or optimizing the data-sifting process to produce the fewest number of false positives; an advanced alert system, which auto-categorizes alert priorities; and advanced transaction monitoring, which uses machine learning to spot suspicious anomalies”.

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Lending And Risk Management

“AI holds real promise for more equitable credit underwriting — as long as practitioners remain diligent about fine-tuning the algorithms. 

Beyond credit scoring and lending, AI has also influenced the way banks assess and manage risk and how they build and interpret contracts.”

Denying credit to persons because of a class or racial bias is something that ails the banking industry across the world. ZestFinance’s“AI-based software purportedly generates fairer models, essentially by downgrading credit data that it has “learned” results in unfair decisions”.

For more on this, do peruse the DexLab Analytics website. DexLab Analytics is the premiere most artificial intelligence training institute in Gurgaon.

 


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How AI is Helping Tackle Climate Change

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How AI is Helping Tackle Climate Change

While the spread of the COVID-19 pandemic has become a bane for economies across the world, slowing down or bringing to a halt markets and trade, the series of lockdowns declared by states has had a positive impact on the environment.

According to China’s Ministry of Ecology and Environment, data recorded between January and March 2020 reflects an 84.5 per cent increase in days with good air quality in 337 cities, and satellite data from the United States National Aeronautics and Space Administration shows a decline in nitrogen dioxide over China.

This piece of news is certainly welcome. Climate change is one of the biggest crises ailing our world today, with scientists and stakeholders worried. However, technological advancements like those in the field of Artificial Intelligence are to a large extant helping tackle the crisis of climate change. Here is how.

Improved climate predictions

At the intersection of data science and climate science is the piece of technology called climate informatics.

It includes areas like“improving prediction of extreme events such as hurricanes, paleoclimatology, like reconstructing past climate conditions using data collected from things like ice cores, climate downscaling, or using large-scale models to predict weather on a hyper-local level, and the socio-economic impacts of weather and climate.”

AI can also uncover new insights from the massive amounts of complex climate simulations generated “by the field of climate modeling, which has come a long way since the first system was created at Princeton in the 1960s.”

Better predictions can help officials make informed climate policy, allow governments to prepare for change, and potentially uncover areas that could reverse some effects of climate change.

Revealing the effects of extreme weather

AI is helping scientists reveal to common persons the effects of extreme weather conditions so they can work towards reversing the effects.

“To make it (the effects) more realistic for more people, researchers from Montreal Institute for Learning Algorithms (MILA), Microsoft, and ConscientAI Labs used GANs, a type of AI, to simulate what homes are likely to look like after being damaged by rising sea levels and more intense storms.”

This was done to inculcate in people habits that are eco-friendly and ecologically sustainable.

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Measuring sources of carbon

By monitoring coal plant emissions with satellite imagery, Carbon Tracker, an independent financial think-tank, can use the data it gathers to convince the finance industry that carbon plants aren’t profitable.

“A grant from Google is expanding the nonprofit’s satellite imagery efforts to include gas-powered plants’ emissions and get a better sense of where air pollution is coming from.”

AI can help make analysis of power plants images automated to get regular updates on emissions. “It also introduces new ways to measure a plant’s impact, by crunching numbers of nearby infrastructure and electricity use. That’s handy for gas-powered plants that don’t have the easy-to-measure plumes that coal-powered plants have.”

For more on AI and its algorithms or related sciences, do peruse the DexLab Analytics website today. DexLab Analytics is a premiere artificial intelligence training institute in Gurgaon, India.

 


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5 Problems Machine Learning Can Solve

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5 Problems Machine Learning Can Solve

Machine Learning, a subset of Artificial Intelligence, has taken the world by storm. A method of data analysis, it is a system that is equipped with expertise to learn from data, identify patterns and take decisions with minimal human intervention.

From clearing our email inboxes of spam to tagging our friends’ faces in social media photograph uploads, Machine Learning is crucial to all aspects of our lives. Here are some problems that Machine Learning can easily take care of.

Manual Data Entry

The problem of inaccuracy and duplication of data that business houses wish to avoid when automating their processes can be tackled with the help of Machine Learning (ML). A report says, “ML programs use the discovered data to improve the process as more calculations are made. Thus machines can learn to perform time-intensive documentation and data entry tasks.”

Moreover, ML knowledge workers can, nowadays spend more time solving problems of higher-value while ML takes care of repetitive work. “Arria, an AI based firm has developed a natural language processing technology which scans texts and determines the relationship between concepts to write reports.”

Detecting Spam

Spam detection, one of the earliest tasks for ML systems, has upgraded.“Four years ago, email service providers used pre-existing rule-based techniques to remove spam. But now the spam filters create new rules themselves using ML.”This is because of ‘neural networks’ installed in spam filters, “Google now boasts of 0.1 percent of spam rate.”

Neural Networks fitted in spam filters can teach themselves to learn to recognize junk mail and phishing messages “by analyzing rules across an enormous collection of computers. In addition to spam detection, social media websites are using ML as a way to identify and filter abuse.”

Product Recommendation

Unsupervised learning enables companies to put in place a product based recommendation system. By studying purchase history of a customer and a correspondingly large inventory of products, ML models can identify certain products in which a customer is likely to be interested.

“The algorithm identifies hidden pattern among items and focuses on grouping similar products into clusters. A model of this decision process would allow a program to make recommendations to a customer and motivate product purchases.”

Medical Diagnosis

Machine Learning in the medical field is touted to improve patients’ health with minimum costs. “Use cases of ML are making near perfect diagnoses, recommend best medicines, predict readmissions and identify high-risk patients. These predictions are based on the dataset of anonymized patient records and symptoms exhibited by a patient.”

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Computer Vision

Computer vision “produces numerical or symbolic information from images and high-dimensional data. It involves machine learning, data mining, database knowledge discovery and pattern recognition.” Potential business applications of image recognition technology can be found in healthcare and automobiles. A tech giant has produced a computer vision powered earpiece that can narrate its interpretation of the outside world to a visually impaired person.

Machine Learning has many applications in industries the world over. For more on this, or a related subject, do peruse the DexLab Analytics website. DexLab Analytics is the best Machine Learning course in Delhi.


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