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Machine Learning

What is Machine Learning?

Machine learning (ML) is fundamentally a subset of artificial intelligence (AI) that allows the machine to learn automatically. No explicit programs are needed instead of coding you gather data and feed it to the generic algorithm. It is a scientific study of algorithms and statistical models used by computers to perform specific tasks.

The machine builds a logic based on that data. It can access data and teach itself from various instructions, interactions, and queries resolved. ML forms data patterns that help in making better decisions. The machines learn without human interference even in fields where developing a conventional algorithm is not workable. ML includes data mining, data analysis to perform predictive analytics.

Machine learning facilitates the analysis of substantial quantities of data. It can identify profitable opportunities, risks, returns and much more at a very high speed and accuracy. Costs and resources are involved in training the agent to process large volumes of information gathered.

Working of Machine Learning:

Machine Learning algorithm obtains skill by using the training data and develops the ability to work on various tasks. It uses data for accurate predictions. If the results are not satisfactory, we can request it to produce other alternative suggestions. ML can have supervised, semi-supervised, unsupervised or reinforcement learning.

Supervised learning is the machine is trained by the dataset to predict and take decisions. The machine applies this logic to the new data automatically once learned. The system can even suggest new input after adequate training and can even compare the actual output with the intended output. This model learns through observations, corrects the errors by altering the algorithm. The model itself finds the patterns and relationships in the dataset to label the data. It finds structures in the data to form a cluster based on its patterns and uses to increase predictability.

Semi-supervised learning uses labeled and unlabelled data for the training purpose. This is partly supervised machine learning, and it considers labeled data in small quantities and unlabelled data in large quantity. The systems can improve the learning accuracy using this method. If the companies have acquired and labelled data; have skilled and relevant resources in order to train it or learn from it they choose semi-supervised learning.

Unsupervised machine learning algorithms are useful when the information used to train is not classified or labeled. Studies that include unsupervised learning prove how systems can conclude a function to depict a hidden structure from the unlabelled data. The system explores data supposition to describe the obscure structures from the unlabelled data.

Reinforcement machine learning, these algorithms can interact with its environment by generating actions. It can find the best outcome from some trial and errors and the agent earns reward or penalty points to maximize its performance. The model trains itself to predict the new data presented. The reinforcement signal is a must for the agent to find out the best action from the ones its suggestions.

Future of ML

Evolution of Machine Learning:

Machine learning has evolved over a period and experiences continuous growth. It developed the pattern recognition and non-programmed automated learning of computers to perform simple and complex tasks. Initially, the researchers were curious about whether computers can learn with the least human intervention just with the help of data. The machines learn from the previous methods of computations, statistical analysis and can repeat the process for other datasets. It can recommend the users for the product and services, respond to FAQs, notify for subjects of your choice, and even detect fraud.

Machine Learning as of today:

Machine Learning has gained popularity for its data processing and self-learning capacity. It is involved in technological advancements and its contribution to human life is noteworthy. E.g. Self-driving vehicles, robots, chatbots in the service industry and innovative solutions in many fields.

Currently, ML is widely used in :

1. Image Recognition: ML algorithms detect and recognize objects, human faces, locations and help in image search. Facial recognition is widely used in mobile applications such as time punching apps, photo editing apps, chats, and other apps where user authentication is mandatory.

2. Image Processing: Machine learning conducts an autonomous vision useful to improve imaging and computer vision systems. It can compress images and these formats can save storage space, transmit faster. It maintains the quality of images and videos.

3. Data Insights: The automation, digitization, and various AI tools used by the systems provide insights based on an organization’s data. These insights can be standard or customized as per the business need.

4. Market Price: ML helps retailers to collect information about the product, its features, its price, promotions applied, and other important comparatives from various sources, in real-time. Machines convert the information to a usable format, tested with internal and external data sources, and the summary is displayed on the user dashboard. The comparisons and recommendations help in making accurate and beneficial decisions for the business.

5. User Personalisation: It is one of the customer retention tactic used in all the sectors. Customer expectations and company offerings have a commercial aspect attached; hence, personalization is introduced on a wide variety of forms. ML processes massive data of customers such as their internet search, personal information, social media interactions, and preferences stored by the users. It helps companies increase the probability of conversion and profitability with reduced efforts with ML technology. It can help branding, marketing, business growth and improve performance.

6. Healthcare Industry: Machine learning assists to improve healthcare service quality; reduce costs, and increase satisfaction. ML can assist medical professionals by searching the relevant data facts and suggest the latest treatments available for such illnesses. It can suggest the precautionary measures to the patient for better healthcare. AI can maintain patient data and use it as a reference for critical cases in hospitals across the globe. The machines can analyze images of MRI or CT Scan, process clinical procedures videos, check laboratory results, sort patient information and use efficiently. ML algorithms can even identify skin cancer and cancerous tumors by studying mammograms.

7. Wearables: These wearables are changing patient care, with strong monitoring of health as a precaution or prevention of illness. They track the heart rate, pulse rate, oxygen consumption by the muscles and blood sugar level in real-time. It can reduce the chances of heart attack or injury, and can recommend the user for medicine dose, health check-up, type of treatment, and help the faster recovery of the patient. With an enormous amount of data that gets generated in healthcare, the reliance on machine learning is unavoidable.

8. Advanced cybersecurity: Security of data, logins, and personal information, bank and payment details is necessary. The estimated losses that organizations face because of cybercrime are likely to reach $6 trillion yearly. Threat is raising the cybersecurity costs and increasing the burden on the operational expenses of organizations. The ML implementation protects user data, their credentials, saves from phishing attacks and maintains privacy.

9. Content Management: The users can see sensible content on their social media platforms. The companies can draw the attention of the target audience and it reduces their marketing and advertising costs. Based on human interactions these machines can show relevant content.

10. Smart Homes: ML does all mundane tasks for you, maintaining the monthly grocery, cleaning material, and regular purchase lists. It can update the list when there are input and order material on the scheduled date. It increases the security at home by keeping the track of known visitors and barring the other from entering the premise or specifies suspicious activities.

11. Logistics: Machine learning can keep track of the user’s choices for delivery and can suggest based on the instructions and addresses they use often. The confirmations, notifications, and feedback about the delivery is processed by the machines more efficiently and in real-time.

Future of ML:

Do not be surprised if we are found learning dance, music, martial arts, and academic subjects from the Bots. We will shortly experience improved services in travel, healthcare, cybersecurity, and many other industries as the algorithms can run throughout with no break, unlike humans. They not only deal but respond and collect feedback in real-time.

Researchers are developing innovative ways of implementing machine-learning models to detect fraud, defend cyberattacks. The future of transportation is great with the wide-scale adoption of autonomous vehicles.

The voice, sound, image, and face recognition, NLP is creating a better understanding of customer requirements and can serve better through machine learning.

Autonomous Vehicles like self-driving cars can reduce traffic-related problems like accidents and keep the driver safe in case of a mishap. ML is developing powerful technologies to let us operate these autonomous vehicles with ease and confidence. The sensors use the data points to form algorithms that can lead to safe driving.

Deeper personalization is possible with ML as it highlights the possibilities of improvement. The advertisements will be of user choice as more data is available from the collective response of each user for the text or video they see.

The future will simplify the machine learning by extracting data from the devices directly instead of asking the user to fill the choices. The vision processing lets the machine view and understands the images in order to take action.

You can now expect cost-effective and ingenious solutions that will alter your choices and change your set of expectations from the companies and products.

According to the survey by Univa 96% of companies think there will be outbursts in Machine Learning projects by 2020. Two out of ten companies have ML projects running in production. 93% of companies, which participated in the survey, have commenced ML projects. (344 Technology and IT professionals were part of survey)

Approximately 64% of technology companies, 52% of the finance sector, 43% of healthcare, 31% of retail, telecommunications, and manufacturing companies are using ML and overall 16 industries are already using machine-learning processes.

Final Thoughts:

Machine Learning is building a new future that brings stability to the business and eases human life. Sales data analysis, streamlining data, mobile marketing, dynamic pricing, and personalization, fraud detection, and much more than the technology has already introduced, we will see new heights of technology.

8 resources to get free training data for ml systems

The current technological landscape has exhibited the need for feeding Machine Learning systems with useful training data sets. Training data helps a program understand how to apply technology such as neural networks. This is to help it to learn and produce sophisticated results.

The accuracy and relevance of these sets pertaining to the ML system they are being fed into are of paramount importance, for that dictates the success of the final model. For example, if a customer service chatbot is to be created which responds courteously to user complaints and queries, its competency will be highly determined by the relevancy of the training data sets given to it.

To facilitate the quest for reliable training data sets, here is a list of resources which are available free of cost.

Kaggle

Owned by Google LLC, Kaggle is a community of data science enthusiasts who can access and contribute to its repository of code and data sets. Its members are allowed to vote and run kernel/scripts on the available datasets. The interface allows users to raise doubts and answer queries from fellow community members. Also, collaborators can be invited for direct feedback.

The training data sets uploaded on Kaggle can be sorted using filters such as usability, new and most voted among others. Users can access more than 20,000 unique data sets on the platform.

Kaggle is also popularly known among the AI and ML communities for its machine learning competitions, Kaggle kernels, public datasets platform, Kaggle learn and jobs board.

Examples of training datasets found here include Satellite Photograph Order and Manufacturing Process Failures.

Registry of Open Data on AWS

As its website displays, Amazon Web Services allows its users to share any volume of data with as many people they’d like to. A subsidiary of Amazon, it allows users to analyze and build services on top of data which has been shared on it.  The training data can be accessed by visiting the Registry for Open Data on AWS.

Each training dataset search result is accompanied by a list of examples wherein the data could be used, thus deepening the user’s understanding of the set’s capabilities.

The platform emphasizes the fact that sharing data in the cloud platform allows the community to spend more time analyzing data rather than searching for it.

Examples of training datasets found here include Landsat Images and Common Crawl Corpus.

UCI Machine Learning Repository

Run by the School of Information & Computer Science, UC Irvine, this repository contains a vast collection of ML system needs such as databases, domain theories, and data generators. Based on the type of machine learning problem, the datasets have been classified. The repository has also been observed to have some ready to use data sets which have already been cleaned.

While searching for suitable training data sets, the user can browse through titles such as default task, attribute type, and area among others. These titles allow the user to explore a variety of options regarding the type of training data sets which would suit their ML models best.

The UCI Machine Learning Repository allows users to go through the catalog in the repository along with datasets outside it.

Examples of training data sets found here include Email Spam and Wine Classification.

Microsoft Research Open Data

The purpose of this platform is to promote the collaboration of data scientists all over the world. A collaboration between multiple teams at Microsoft, it provides an opportunity for exchanging training data sets and a culture of collaboration and research.

The interface allows users to select datasets under categories such as Computer Science, Biology, Social Science, Information Science, etc. The available file types are also mentioned along with details of their licensing.

Datasets spanning from Microsoft Research to advance state of the art research under domain-specific sciences can be accessed in this platform.

GitHub.com/awesomedata/awesomepublicdatasets

GitHub is a community of software developers who apart from many things can access free datasets. Companies like Buzzfeed are also known to have uploaded data sets on federal surveillance planes, zika virus, etc. Being an open-source platform, it allows users to contribute and learn about training data sets and the ones most suitable for their AI/ML models.

Socrata Open Data

This portal contains a vast variety of data sets which can be viewed on its platform and downloaded. Users will have to sort through data which is currently valid and clean to find the most useful ones. The platform allows the data to be viewed in a tabular form. This added with its built-in visualization tools makes the training data in the platform easy to retrieve and study.

Examples of sets found in this platform include White House Staff Salaries and Workplace Fatalities by US State.

R/datasets

This subreddit is dedicated to sharing training datasets which could be of interest to multiple community members. Since these are uploaded by everyday users, the quality and consistency of the training sets could vary, but the useful ones can be easily filtered out.

Examples of training datasets found in this subreddit include New York City Property Tax Data and Jeopardy Questions.

Academic Torrents

This is basically a data aggregator in which training data from scientific papers can be accessed. The training data sets found here are in many cases massive and they can be accessed directly on the site. If the user has a BitTorrent client, they can download any available training data set immediately.

Examples of available training data sets include Enron Emails and Student Learning Factors.

Conclusion

In an age where data is arguably the world’s most valuable resource, the number of platforms which provide this is also vast. Each platform caters to its own niche within the field while also displaying commonly sought after datasets.  While the quality of training data sets could vary across the board, with the appropriate filters, users can access and download the data sets which suit their machine learning models best. If you need a custom dataset, do check us out here, share your requirements with us, and we’ll more than happy to help you out!

8 common myths about machine learning

Artificial Intelligence and the idea of it has always been around be it research or sci-fi movies. But the advances in AI wasn’t drastic until recently. Guess what changed? The focus moved from vast AI to components of AI such as machine learning, natural language processing, and other technologies that make it possible.

Learning models which form the core of AI started being used extensively. This shift of focus to Machine Learning gave rise to various libraries and tools which make ML models easily accessible. Here are some common myths surrounding Machine Learning:

Machine Learning, Deep Learning, Artificial Intelligence are all the same

In a recent survey by TechTalks, it was discovered that more than 30% of the companies wrongly claim to use Advance Machine Learning models to improve their operations and automate the process. Most people use AI and ML synonymously. How different are AI, ML and Deep Learning?

Machine Learning is a branch of Artificial Intelligence which has learning algorithms powered by annotated data which learn through experiences. There are primarily two types of learning algorithms.

Supervised Learning algorithms draw patterns based on the input and output values of the datasets. It starts predicting the outputs from the training data sets with possible input and output values.

Unsupervised learning models look at all the data fed into the model and find out patterns in the data. It uses unstructured and unlabeled data sets.

Artificial Intelligence, on the other hand, is a very broad area of Computer Science, where robust engineering and technological advances are used to build systems that need minimal or no human intelligence. Everything from the auto-player in video games to predictive analytics used to forecast sales fall under the same roof using some Machine Learning algorithms

Deep Learning uses a set of ML algorithms to model abstraction in data sets with system architecture. It is an approach used to build and train neural networks.

All data is useful to train a Machine Learning model

Another common myth around Machine learning models is that all the data is useful to improve the outputs of the model. The raw data is never clean and representative of the outputs.

To train the Machine Learning models to learn the accurate outputs expected, data sets need to be labeled with relevance. Irrelevant data needs to be removed.

The accuracy of the model is directly correlated to the quality of the data sets. The quality of the trained data sets results in better accuracy rather than a huge amount of raw/unlabelled data.

Building an ML system is easy with unsupervised learning and ‘Black Box Models’

The most business decision will require very specific evaluation, to make strategic data-driven decisions. Unsupervised and ‘Black Box’ models use algorithms randomly and highlight data patterns making it biased towards patterns which aren’t relevant.

The usability and relevance of these patterns to the objective the business the focus is on are a lot less when these models are used. Black box systems do not reveal what patterns they have used to arrive at certain conclusions. Supervised or Reinforcement learning trained with curated, labeled data sets can surgically investigate the data and give us the desired outputs.

ML will replace people and kill jobs

The usual notion around any advanced technology is that it will replace people and make people jobless. According to Erik Brynjolfsson and Daniel Rock, with MIT, and TomMitchell of Carnegie Mellon University, ML will kill the automated or painfully redundant tasks, not jobs.

Humans will spend more time on decision making jobs rather than repetitive tasks which ML can take care of. The job market will see a significant reduction in repetitive job roles but the wave of ML, AI will create a new sector of jobs to handle the data, train it and derive outcomes based on the ML systems.

Machine Learning can only discover correlations between objects and not causal relationships

A common perception of Machine Learning is that it discovers easy correlations and not insightful outputs. Machine Learning used in conjunction with thematic roles and relationship models of NLP will provide rich insights. Contrary to common belief, ML can identify causal relationships. This is commonly used to try out different use cases and observing the consequences of the cases.

Machine learning can work without human intervention

Most decisions from the ML models will need human intelligence and intervention. For examples, an airlines company may adopt ML algorithms to get better insights and influence best ticket prices. Data sets are constantly updated and complex algorithms may be run on it.

But, to decide the price of a flight by the system itself has a lot of loopholes, the company will hire an analyst who will analyze the data and sets prices with the help of models and their analytical skills, not just relying on the model alone.

The reasoning behind the decision making is still a human intelligence one. Complete control should not be rested on models for optimal results.

Machine Learning is the same as Data mining

Data mining is a technique to examine databases and discover the properties of data sets. The reasons its often confused is because Data Analytics uses these data sets using data visualization techniques. Whereas, Machine Learning is a subfield which uses curated data sets to teach systems the desired outputs and make predictions.

There is similarity when unsupervised learning Ml models use datasets to draw insights from them, which is precisely what data mining does. Machine Learning can be used for data mining.

The common confusion between the two arises due to a new term being used extensively, Data Science. Most Data mining-focused professionals and companies are leaning towards using Data science and analytics now causing more confusion.

ML takes a few months to master and is simple

To be an efficient ML Engineer, a lot of experience and research is needed. Contrary to the hype, ML is more than importing existing libraries in languages and using Tensor Flow or Keras. These can be used with minimal training but takes an experienced hand to provide accuracy.

A lot of intense Machine Learning focussed products require intense research on topics and even coming up with approaches using methods that are in discussion at a university or research level. Already existing libraries solve very generic problems people are trying to solve and not really insightful data. A deeper understanding of algorithms is needed to create an accurate model with an improved f1(accuracy) score.

To sum up, there is an overlap of concepts and models in Machine Learning, Artificial Intelligence, Data Science and Deep Learning. However, the goal and science of the subfields vastly vary. To build completely automated AI systems, all the fields become crucial and play a distinct role.

Understanding the difference between AI, ML & NLP models

Technology has revolutionized our lives and is constantly changing and progressing. The most flourishing technologies include Artificial Intelligence, Machine Learning, Natural Language Processing, and Deep Learning. These are the most trending technologies growing at a fast pace and are today’s leading-edge technologies.

These terms are generally used together in some contexts but do not mean the same and are related to each other in some or the other way. ML is one of the leading areas of AI which allows computers to learn by themselves and NLP is a branch of AI.

What is Artificial Intelligence?

Artificial refers to something not real and Intelligence stands for the ability of understanding, thinking, creating and logically figuring out things. These two terms together can be used to define something which is not real yet intelligent.

AI is a field of computer science that emphasizes on making intelligent machines to perform tasks commonly associated with intelligent beings. It basically deals with intelligence exhibited by software and machines.

While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. AI finds application in very many fields, from assisting cameras, recognizing landscapes, and enhancing picture quality to use-cases as diverse and distinct as self-driving cars, autonomous robotics, virtual reality, surveillance, finance, and health industries.

History of AI

The first work towards AI was carried out in 1943 with the evolution of Artificial Neurons. In 1950, Turing test was conducted by Alan Turing that can check the machine’s ability to exhibit intelligence.

The first chatbot was developed in 1966 and was named ELIZA followed by the development of the first smart robot, WABOT-1. The first AI vacuum cleaner, ROOMBA was introduced in the year 2002. Finally, AI entered the world of business with companies like Facebook and Twitter using it.

Google’s Android app “Google Now”, launched in the year 2012 was again an AI application. The most recent wonder of AI is “the Project Debater” from IBM. AI has currently reached a remarkable position

The areas of application of AI include

  • Chat-bots – An ever-present agent ready to listen to your needs complaints and thoughts and respond appropriately and automatically in a timely fashion is an asset that finds application in many places — virtual agents, friendly therapists, automated agents for companies, and more.
  • Self-Driving Cars: Computer Vision is the fundamental technology behind developing autonomous vehicles. Most leading car manufacturers in the world are reaping the benefits of investing in artificial intelligence for developing on-road versions of hands-free technology.
  • Computer Vision: Computer Vision is the process of computer systems and robots responding to visual inputs — most commonly images and videos.
  • Facial Recognition: AI helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.

What is Machine Learning?

One of the major applications of Artificial Intelligence is machine learning. ML is not a sub-domain of AI but can be generally termed as a sub-field of AI. The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.

Implementing an ML model requires a lot of data known as training data which is fed into the model and based on this data, the machine learns to perform several tasks. This data could be anything such as text, images, audio, etc…

 Machine learning draws on concepts and results from many fields, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity and control theory. ML itself is a self-learning algorithm. The different algorithms of ML include Decision Trees, Neural Networks, SEO, Candidate Elimination, Find-S, etc.

History of Machine Learning

The roots of ML lie way back in the 17th century with the introduction of Mechanical Adder and Mechanical System for Statistical Calculations. Turing Test conducted in 1950 was again a turning point in the field of ML.

The most important feature of ML is “Self-Learning”. The first computer learning program was written by Arthur Samuel for the game of checkers followed by the designing of perceptron (neural network). “The Nearest Neighbor” algorithm was written for pattern recognition.

Finally, the introduction of adaptive learning was introduced in the early 2000s which is currently progressing rapidly with Deep Learning is one of its best examples.

Different types of machine learning approaches are:

Supervised Learning uses training data which is correctly labeled to teach relationships between given input variables and the preferred output.

Unsupervised Learning doesn’t have a training data set but can be used to detect repetitive patterns and styles.

Reinforcement Learning encourages trial-and-error learning by rewarding and punishing respectively for preferred and undesired results.

ML has several applications in various fields such as

  • Customer Service: ML is revolutionizing customer service, catering to customers by providing tailored individual resolutions as well as enhancing the human service agent capability through profiling and suggesting proven solutions. 
  • HealthCare: The use of different sensors and devices use data to access a patient’s health status in real-time.
  • Financial Services: To get the key insights into financial data and to prevent financial frauds.
  • Sales and Marketing: This majorly includes digital marketing, which is currently an emerging field, uses several machine learning algorithms to enhance the purchases and to enhance the ideal buyer journey.

What is Natural Language Processing?

Natural Language Processing is an AI method of communicating with an intelligent system using a natural language.

Natural Language Processing (NLP) and its variants Natural Language Understanding (NLU) and Natural Language Generation (NLG) are processes which teach human language to computers. They can then use their understanding of our language to interact with us without the need for a machine language intermediary.

History of NLP

NLP was introduced mainly for machine translation. In the early 1950s attempts were made to automate language translation. The growth of NLP started during the early ’90s which involved the direct application of statistical methods to NLP itself. In 2006, more advancement took place with the launch of IBM’s Watson, an AI system which is capable of answering questions posed in natural language. The invention of Siri’s speech recognition in the field of NLP’s research and development is booming.

Few Applications of NLP include

  • Sentiment Analysis – Majorly helps in monitoring Social Media
  • Speech Recognition – The ability of a computer to listen to a human voice, analyze and respond.
  • Text Classification – Text classification is used to assign tags to text according to the content.
  • Grammar Correction – Used by software like MS-Word for spell-checking.

What is Deep Learning?

The term “Deep Learning” was first coined in 2006. Deep Learning is a field of machine learning where algorithms are motivated by artificial neural networks (ANN). It is an AI function that acts lie a human brain for processing large data-sets. A different set of patterns are created which are used for decision making.

The motive of introducing Deep Learning is to move Machine Learning closer to its main aim. Cat Experiment conducted in 2012 figured out the difficulties of Unsupervised Learning. Deep learning uses “Supervised Learning” where a neural network is trained using “Unsupervised Learning”.

Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several ‘nodes’ that process information like how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning. The different activation functions used in Deep Learning include linear, sigmoid, tanh, etc.…

History of Deep Learning

The history of Deep Learning includes the introduction of “The Back-Propagation” algorithm, which was introduced in 1974, used for enhancing prediction accuracy in ML.  Recurrent Neural Network was introduced in 1986 which takes a series of inputs with no predefined limit, followed by the introduction of Bidirectional Recurrent Neural Network in 1997.  In 2009 Salakhutdinov & Hinton introduced Deep Boltzmann Machines. In the year 2012, Geoffrey Hinton introduced Dropout, an efficient way of training neural networks

Applications of Deep Learning are

  • Text and Character generation – Natural Language Generation.
  • Automatic Machine Translation – Automatic translation of text and images.
  • Facial Recognition: Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.
  • Robotics: Deep learning has also been found to be effective at handling multi-modal data generated in robotic sensing applications.

Key Differences between AI, ML, and NLP

Artificial intelligence (AI) is closely related to making machines intelligent and make them perform human tasks. Any object turning smart for example, washing machine, cars, refrigerator, television becomes an artificially intelligent object. Machine Learning and Artificial Intelligence are the terms often used together but aren’t the same.

ML is an application of AI. Machine Learning is basically the ability of a system to learn by itself without being explicitly programmed. Deep Learning is a part of Machine Learning which is applied to larger data-sets and based on ANN (Artificial Neural Networks).

The main technology used in NLP (Natural Language Processing) which mainly focuses on teaching natural/human language to computers. NLP is again a part of AI and sometimes overlaps with ML to perform tasks. DL is the same as ML or an extended version of ML and both are fields of AI. NLP is a part of AI which overlaps with ML & DL.

Understanding training data and how to build high-quality training data for ai/ml models

We are living in one of the most exciting times, where faster processing power and new technological advancements in AI and ML are transcending the ways of the past. From conversational bots helping customers make purchases online to self-driving cars adding a new dimension of comfort and safety for commuters. While these technologies continue to grow and transform lives, what makes them so powerful is data.

Tons and tons of data.

Machine Learning systems, as the name suggests, are systems that are constantly learning from the data being consumed to produce accurate results.

If the right data is used, the system designed can find relations between entities, detect patterns, and make decisions. However, not all data or datasets used to build such models are treated equally.

Data for AI & ML models can be essentially classified into 5 categories: training dataset, testing dataset, validation dataset, holdout dataset, and cross-validation dataset. For the purpose of this article, we’ll only be looking at training dataset and cover the following topics.

What Is Training Data

Training data also called training dataset or training set or learning set, is foundational to the way AI & ML technologies work. Training data can be defined as the initial set of data used to help AI & ML models understand how to apply technologies such as neural networks to learn and produce accurate results.

Training sets are materials through which an AI or ML models learn how to process information and produce the desired output. Machine learning uses neural network algorithms that mimic the abilities of the human brain to take in diverse inputs and weigh them, to produce neural activations, in individual neurons. These provide a highly detailed model of how human thought process works.

Given the diverse types of systems available, training datasets are structured in a different way for different models. For conversational bots, the training set contains the raw text that gets classified and manipulated.

On the other hand, for convolution models using image processing and computer vision, the training set consists of a large volume of images. Given the complexity and sophistication of these models, it uses iterative training on each image to eventually understand the patterns, shapes, and subjects in a given image.

In a nutshell, training sets are labeled and organized data needed to train AI and ML models.

Why Are Training Datasets Important

When building training sets for AI & ML models, one needs huge amounts of relevant data to help these models make the most optimal decision. Machine learning allows computer systems to tackle very complex problems and deal with inherent variations of hundreds and thousands or millions of variables.

The success of such models is highly reliant on the quality of the training set used. A training set that accounts for all variations of the variables in the real world would result in developing more accurate models. Just like in the case of a company collecting survey data to know about their consumer, larger the sample size for the survey is, more accurate the conclusion will be.

If the training set isn’t large enough, the resultant system won’t be able to capture all variations of the input variables resulting in inaccurate conclusions.

While AI & ML models need huge amounts of data, they also need the right kind of data, as the system learns from this set of data. Having a sophisticated algorithm for AI & ML models isn’t enough when the data used to train these systems are bad or faulty. Training a system on a poor dataset or a dataset that contains wrong data, the system will end up learning wrong lessons, and generate wrong results. And eventually, not work the way it is expected to. On the contrary, a basic algorithm using a high-quality dataset will be able to produce accurate results and function as expected.

For example, in the case of a speech recognition system. The system can be made on a mathematical model to train the system on textbook English. However, this system is bound to show inaccurate results.

When we talk about language, there is a massive difference between textbook English and how people actually speak. To this add the factors – such as voice, dialects, age, gender – varying among speakers. This system would struggle to handle any cases or conversations that stray from the textbook English used to train it. For inputs having loose English or a different accent or use of slang, the system would fail to function for the purpose it was created.

Also, in a case, such a system is used to comprehend a text chat or email it would throw unexpected results. As a system trained in textbook English would fail to account for abbreviations and emojis used, which are commonly used among people in everyday conversations.

So, to build an accurate AI or ML model, it’s essential to build a comprehensive and high-quality training dataset. To help these systems learn the right lessons and formulate the right responses. While it’s a substantial task to generate such a high volume of data, it is necessary to do so.

How To Build A Training Dataset

Now, that we have understood why training data are integral to the success of an AI or ML model, it’s necessary to know how to build a training dataset.

The process of building a training dataset can be classified into 3 simple steps: data collection, data preprocessing, and data conversion. Let’s take a look at each of these steps and how it helps in building a high-quality training set.

Data Collection

The first step in making a training set is choosing the right number of features for a particular dataset. The data should be consistent and have the least amount of missing values. In case a feature has 25% to 30% of missing values, then this feature should not be considered to be part of the training set.

However, there might be instances when such features might be closely related to another feature. In such a case, it’s advisable to impute and handle the missing values correctly to achieve desired results. At the end of the data collection step, you should clearly know how to handle preprocessing data.

Data Preprocessing

Once the data has been collected, we enter the data preprocessing stage. In this step, we collect the right data from the complete data set and build a training set. The steps to be followed here are:

  • Organize and Format: If the data is scattered across multiple files or sheets, it’s necessary to compile all this data to form a single dataset. This includes finding the relation between these datasets and preprocess to form a dataset of required dimensions.
  • Data Cleaning: Once all the scattered data is compiled to a single dataset, it’s important to handle the missing values. And, remove any unwanted characters from the dataset.
  • Feature extraction: The final step in the data preprocessing step deals with finalizing the right number of features required for the training set. One has to analyze and find out features that are absolutely important for the model to function accurately and select them for faster computations and low memory consumption.

Data Conversion

The data conversion stage consists of the following steps,

  • Scaling: Once the data is placed, it’s necessary to scale the data as per a definite value. For example, a bank application containing transaction amount being important, then it’s required to scale the data on transaction value to build a robust model.
  • Disintegration and composition: There might be certain features in the training data that can be better understood by the model when split. For example, time-series function, where days, month, year, hour, minutes, and seconds can be split for better processing.
  • Composition: While some features can be better utilized when disintegrated, other features can be better understood when combined with another.

This covers the necessary steps to be taken to build a high-quality training set for AI & ML models. While this might help you formulate a framework that helps you build training sets for your system, here’s how you can put these frameworks into action.

Dedicated In-house Team

One of the easiest way for you could be to hire an intern to help you with the task of collecting and preprocessing data. You can also set up a dedicated ops team to help with your training set requirements. While this method provides you with greater control over the quality, it isn’t scalable, and you’ll be forced to look for more efficient methods eventually.

Outsource Training Set Creation

If having an in-house team doesn’t cut it, it would be a smarter move to outsource it, right? Well, not entirely.

Outsourcing your training set creation has its own set of troubles. Right from training people to ensuring quality is maintained to making sure people aren’t cutting slack.

Training Data Solutions Providers

With AI & ML technologies continuing to grow and more companies joining the bandwagon to roll out AI-enabled tools. There are a plethora of companies that can help you with your AI/ML training dataset requirement. We at Bridged.co have served prominent enterprises delivering over 50 million datasets.

And that is everything you need to know about training data, and how to go about creating one that helps you build powerful, robust, and accurate systems.

The need for quality training data | Blog | Bridged.o

What is training data? Where to find it? And how much do you need?

Artificial Intelligence is created primarily from exposure and experience. In order to teach a computer system a certain thought-action process for executing a task, it is fed a large amount of relevant data which, simply put, is a collection of correct examples of the desired process and result. This data is called Training Data, and the entire exercise is part of Machine Learning.

Artificial Intelligence tasks are more than just computing and storage or doing them faster and more efficiently. We said thought-action process because that is precisely what the computer is trying to learn: given basic parameters and objectives, it can understand rules, establish relationships, detect patterns, evaluate consequences, and identify the best course of action. But the success of the AI model depends on the quality, accuracy, and quantity of the training data that it feeds on.

The training data itself needs to be tailored for the end-result desired. This is where Bridged excels in delivering the best training data. Not only do we provide highly accurate datasets, but we also curate it as per the requirements of the project.

Below are a few examples of training data labeling that we provide to train different types of machine learning models:

2D/3D Bounding Boxes

2D/3D bounding boxed | Blog | Bridged.co

Drawing rectangles or cuboids around objects in an image and labeling them to different classes.

Point Annotation

Point annotation | Blog | Bridged.co

Marking points of interest in an object to define its identifiable features.

Line Annotation

Line annotation | Blog | Bridged.co

Drawing lines over objects and assigning a class to them.

Polygonal Annotation

Polygonal annotation | Blog | Bridged.co

Drawing polygonal boundaries around objects and class-labeling them accordingly.

Semantic Segmentation

Semantic segmentation | Blog | Bridged.co

Labeling images at a pixel level for a greater understanding and classification of objects.

Video Annotation

Video annotation | Blog | Bridged.co

Object tracking through multiple frames to estimate both spatial and temporal quantities.

Chatbot Training

Chatbot training | Blog | Bridged.co

Building conversation sets, labeling different parts of speech, tone and syntax analysis.

Sentiment Analysis

Sentiment analysis | Blog | Bridged.co

Label user content to understand brand sentiment: positive, negative, neutral and the reasons why.

Data Management

Cleaning, structuring, and enriching data for increased efficiency in processing.

Image Tagging

Image tagging | Blog | Bridged.co

Identify scenes and emotions. Understand apparel and colours.

Content Moderation

Content moderation | Blog | Bridged.co

Label text, images, and videos to evaluate permissible and inappropriate material.

E-commerce Recommendations

Optimise product recommendations for up-sell and cross-sell.

Optical Character Recognition

Learn to convert text from images into machine-readable data.


How much training data does an AI model need?

The amount of training data one needs depends on several factors — the task you are trying to perform, the performance you want to achieve, the input features you have, the noise in the training data, the noise in your extracted features, the complexity of your model and so on. Although, as an unspoken rule, machine learning enthusiasts understand that larger the dataset, more fine-tuned the AI model will turn out to be.

Validation and Testing

After the model is fit using training data, it goes through evaluation steps to achieve the required accuracy.

Validation & testing of models | Blog | Bridged.co

Validation Dataset

This is the sample of data that is used to provide an unbiased evaluation of the model fit on the training dataset while tuning model hyper-parameters. The evaluation becomes more biased when the validation dataset is incorporated into the model configuration.

Test Dataset

In order to test the performance of models, they need to be challenged frequently. The test dataset provides an unbiased evaluation of the final model. The data in the test dataset is never used during training.

Importance of choosing the right training datasets

Considering the success or failure of the AI algorithm depends so much on the training data it learns from, building a quality dataset is of paramount importance. While there are public platforms for different sorts of training data, it is not prudent to use them for more than just generic purposes. With curated and carefully constructed training data, the likes of which are provided by Bridged, machine learning models can quickly and accurately scale toward their desired goals.

Reach out to us at www.bridgedai.com to build quality data catering to your unique requirements.