Anjuum Khanna-Machine Learning as discussed in our previous post is a tool that has the power to solve almost every critical problem that occurs through heaps of data. Not only is Machine Learning extremely efficient, with the right amount of effort it is highly rewarding too.

That is probably one of the best features of Machine Learning. Despite being a complex concept, it is available to anyone and everyone thanks to the internet and ML enthusiasts.

So in this week’s Anjuum Khanna’s tech blog, we are going to list out the top 10 Machine Learning software and tools that are available on the market to be accessed by anyone willing to invest time in ML.

Top 5 Machine Learning Tools 2020

Most Machine Learning tools are free but some have paid plans as well. In today’s post, we will only be mentioning the free ones.

PyTorch

Coded in Python, C, and C++, PyTorch is wonderful software to learn ML modules as a beginner. Though it does not have many features, it is easy to operate and comes in handy for new students.

It provides algorithms for the NN Module, the Autograd Module, and the Optim Module for Windows, Mac OS, and Linux. PyTorch is completely free to use and can be downloaded at its official website PyTorch.

TensorFlow

TensorFlow is a Javascript library provider that will help you in building your models. TensorFlow’s APIs can be used in constructing neural networks and training models as well. It is coded on CUDA, Python, and C++ and is available for Windows, Linux, and Mac OS.

Though TensorFlow is free and can be utilized for other ML extension software as well, due to its vastness and complexity a beginner can find it difficult to handle.

Despite its minute shortcomings, TensorFlow is a well-rounded tool that can prove to be of much help to an ML developer. Download TensorFlow here.

Keras.io

Keras.io is a cross-platform Machine Learning tool that was developed using Python. Its API can be extensively used in developing neural networks. Keras.io is an extremely user-friendly ML tool that supports the combination of dual networks, has the feature of convolution networks, and can be run using the CPU or GPU.

It is completely free and is a modular software but needs other library providers like TensorFlow to run. Keras.io is an extensive ML tool that every developer should have.

Install Keras.io from its official website here.

Colab

A cloud service is essential in Machine Learning. That is why Colab has been put up in this list of top 5 ML tools as it is one of the most popular cloud services out there.

Google Colab is free software that can be run on python and helps you in research as well as education in Machine Learning. It supports the libraries of many above mentioned ML tools like PyTorch, TensorFlow, and Keras.io. Google Colab can also be accessed through your Google drive!

Check out Google Colab here.

Shogun

Shogun is a Machine Learning Library provider and has many useful data structures as well as algorithms under its belt. It is user friendly and has a variety of features like Clustering, Regression, Classification, Dimensionality reduction, and Online Learning.

Machine Learning Tools

It has been developed using C++ and is available for Linux, Windows, and Mac OS. It is also free and can be downloaded using the link provided below:

About Anjuum Khanna

Anjuum Khanna is a passionate tech blogger based in India and has been a market professional for over 19 years. He has had several successful tech ventures all over India and the Middle East bringing in mountains of profits. Ajnuum Khanna is now working with India’s top-notch Fintech company and an avid advocate of people-oriented management.

To know more about Anjuum Khanna, visit his tech blog, and read up more posts below!

4 Ways to Improve Customer Experience

Why Artificial Intelligence Matters

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Anjuum Khanna– Artificial Intelligence or better known as AI has been of major significance in the modern generation. AI has been the center of wonder and fear amongst tech enthusiasts as well as traditionalists. But there is one more buzzword that keeps floating around in the air of the market which most people confuse with artificial intelligence. And that is called Machine Learning.

Machine Learning has been around for a short period with us but has grown immensely popular, though it has often been overshadowed by the buzzworthy Artificial Intelligence. Well, in this blog post we will cover brief differences between Machine Learning and Artificial Intelligence, and their potential as well as their existing applications.

What is the Core Difference Between Machine Learning and Artificial Intelligence?

To put it in layman’s terms, Machine Learning is identifying patterns to solve future problems that will have the same patterns, and Artificial Intelligence is the art of a computer in absorbing knowledge and applying it according to different situations.

Artificial Intelligence as the name suggests is at present an inferior machine compliment of the human brain which can absorb and even synthesize effective procedures that are better suited in varied situations and completely new environments. It does not rely much on data but critical thinking.

Machine Learning is the art of understanding the patterns in enormous amounts of data and developing an algorithm that can efficiently solve problems of the future in a similar environment. Both AI and ML are very similar yet complete opposites of each other. One relies on patterns to form similar future solutions and the other relies on gaining knowledge that can be diverse.

Machine learning vs Artificial Intelligence

Which is More Useful, Machine Learning, or Artificial Intelligence?

It would not do justice to anyone of the two by saying the other is better. Both Machine Learning and Artificial Intelligence have existing as well as potential applications that can prove to be revolutionary.

Though it is interesting to note that, due to the development of AI being a considerably difficult task than Machine Learning, Machine Learning and the explosion of big data has to lead to ML being very valuable to current corporations.

ML with the help of terabytes of data that floats around us every minute has advanced quite down the line and has proved to be a valuable asset for huge market companies in increasing their efficiency and solving critical problems.

Artificial Intelligence is certainly not behind though. The suggestions that we get for paths on our journeys to cut down our time are made by Google’s AI for Google Maps. Siri is one of the most popular and accessible AI out there which is used by millions of people. And ironically, with the ardent of technology, the AI of Google Maps communicates with the AI of Tesla cars to take paths that can save time.

So we do already have AI’s mixing up with each other and communicating! Even if it is in a controlled and technical manner.

The Real World Applications of Artificial Intelligence and Machine Learning

AI has seen a resurgence in today’s modern era after its development was questioned by traditionalists. But keeping the idea of AI taking over the world aside, it has found many real-world applications today!

AI has seeped into many parts of our life. Most rivals in a game are powered by AI, all social media platforms use artificial intelligence to recommend content for your feed, and various chatbots and assistants are now controlled by AI.

As mentioned previously, Google Duplex, Siri, Alexa, and other mobile assistants are all AI-powered applications that are accessible to us with a flick of the wrist.

Machine Learning on the other hand has more subsidiary applications that have proved to be extremely valuable to corporations. Facebook’s ‘tag a friend feature’ works based on Machine Learning and going through photos of users to provide accurate recommendations.

Machine Learning is also present in apps that offer services. For example, apps like Uber present a personalized interface to the users for a better experience and virtual maps produce an ETA based on analyzing data of other cars through Machine Learning.

Machine Learning and Artificial Intelligence are the two most popular buzzwords in the tech field right now, but despite being buzzwords, they have deeply seeped into the lives of the average consumer. If you would look around yourself, you would realize just how much you are surrounded by the science fiction of yesterday.

Who is Anjuum Khanna?

Anjuum Khanna is a tech enthusiast who is always on the lookout for the future. An experienced tech blogger and a market leader, Anjuum Khanna lives, and breathes tech.

To find out more about Anjuum Khanna and his blogs, click here!

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Why Artificial Intelligence Matters

What is Machine Learning

Anjuum Khanna-today I will talk about most commonly used technology disruptor about which we heard a lot. But I always mention in my blogs that we hear about what technology has done to our world, in the same we should also look forward to unfold future for more opportunities.

So, in Anjuum Khanna’s simple words let’s define AI. As the name speaks it is known as “artificial intelligence” or “machine intelligence”. So Artificial intelligence (AI) is a special feature of machines, in comparison to the natural intelligence displayed by humans and other animals. In computer science, AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. So when a machine is involved in a function like “problem-solving” or “learning” it is also known as artificial intelligence.

As intelligence is a step above the common task so a task which is common is not intelligence. As per me, this word is a word which is full of disputes. So intelligence requires frequent innovation. Let us understand this with a small example. As optical character recognition is frequently excluded from “artificial intelligence”, has become a routine technology. At one point in time, this was the part of Artificial Intelligence. Right now these technologies are defined as artificial intelligence understanding human speech, competing at the highest level in strategic game systems (such as chess), autonomous cars, intelligent routing in the content delivery network and military simulations.

After this explanation let’s go to history, where we will see how and when AI was defined. Back in the 1950s, the fathers of the field Minsky and McCarthy described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. This is a very simple definition which in Anjuum Khanna’s words communicate that any task which is done with intelligence by the human being is performed by machine can be called as artificial intelligence. So after many disputes in history, we have settled on few criteria like planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity which undoubtedly belong to AI.

Type of AI

As per me (Anjuum Khanna), segregation is required to see the development stages of any product or technology. We can easily define AI into two categories:-

• Narrow AI is what we see all around us in computers today. Intelligent systems that have been taught or learned how to carry out specific tasks without being explicitly programmed how to do so.

Let me explain through few examples this type of machine intelligence is evident in the speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, in the recommendation engines that suggest products you might like based on what you bought in the past. Unlike humans, these systems can only learn or be taught how to do specific tasks, which is why they are called narrow AI.

• Artificial general intelligence is a futuristic intelligence and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets, or to reason about a wide variety of topics based on its accumulated experience. This is the sort of AI more commonly seen in movies, but this technology doesn’t exist today.

As per the survey conducted by AI developers in between 2040 & 2050 this technology will start developing and by 2075 will achieve 90% of development. However few groups are still confused about its development as till the time we don’t have the hold on the functionality of the human brain we can’t even start with general intelligence.

For the better understanding of Artificial intelligence as per me (Anjuum Khanna) we should understand few basic technologies of this concept.

Machine Learning: –

In Anjuum Khanna’s definition, machine learning is a computer system which can feed large amounts of data, which is then used by the machine to learn how to carry out a specific task, such as understanding speech or captioning a photograph.

Neural networks:-

These are brain-inspired networks of interconnected layers of algorithms, called neurons, that feed data into each other, and which can be trained to carry out specific tasks by modifying the importance attributed to input data as it passes between the layers.

These are two supplementary topics which need to understand with artificial intelligence. One more of AI research is evolutionary computation. This is basically natural selection and sees genetic algorithms undergo random mutations and combinations between generations in an attempt to evolve the optimal solution to a given problem. This approach has even been used to help design AI models, effectively using AI to help build AI.

The most important question that comes to our mind is how AI will change this world. And I m (Anjuum Khanna) having my own thought process on the same. So let’s understand this with an example.

All of the major cloud platforms such as Amazon Web Services, Microsoft Azure and Google Cloud Platform provide access to GPU arrays for training and running machine learning models, with Google also gearing up to let users use its Tensor Processing Units — custom chips whose design is optimized for training and running machine-learning models.

All of the necessary associated infrastructure and services are available from the big three, the cloud-based data stores, capable of holding the vast amount of data needed to train machine-learning models, services to transform data to prepare it for analysis, visualization tools to display the results clearly, and software that simplifies the building of models.

These cloud platforms are even simplifying the creation of custom machine-learning models, with Google recently revealing a service that automates the creation of AI models, called Cloud AutoML. This drag-and-drop service builds custom image-recognition models and requires the user to have no machine-learning expertise.

Cloud-based, machine-learning services are constantly evolving, and at the start of 2018, Amazon revealed a host of new AWS offerings designed to streamline the process of training up machine-learning models.

For those firms that don’t want to build their own machine learning models but instead want to consume AI-powered, on-demand services — such as voice, vision, and language recognition — Microsoft Azure stands out for the breadth of services on offer, closely followed by Google Cloud Platform and then AWS. Meanwhile, IBM, alongside its more general on-demand offerings, is also attempting to sell sector-specific AI services aimed at everything from health care to retail, grouping these offerings together under its IBM Watson umbrella — and recently investing $2bn in buying The Weather Channel to unlock a trove of data to augment its AI services.

AI applications

To know more about AI we need to learn through examples. Here are some examples to see its impact on all major industries.

AI in healthcare: – This is the most critical industry as it requires precision and accuracy. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best-known healthcare technologies is IBM Watson. It understands natural language and is capable of responding to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema.

AI in business: – Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Machine learning algorithms are being integrated into analytics and CRM platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers.

AI in education: – AI can automate grading, giving educators more time. AI can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. AI could change where and how students learn, perhaps even replacing some teachers. It can find out the gaps and help in resolving them.

AI in finance: – AI in personal finance applications, such as Mint or Turbo Tax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice. Other programs, such as IBM Watson, have been applied to the process of buying a home. Today, the software performs much of the trading on Wall Street.

AI in law:- The discovery process, sifting through documents, in law is often overwhelming for humans. Automating this process is a more efficient use of time. Startups are also building question-and-answer computer assistants that can sift programmed-to-answer questions by examining the taxonomy and ontology associated with a database.

AI in manufacturing: – This is an area that has been at the forefront of incorporating robots into the workflow. Industrial robots used to perform single tasks and were separated from human workers, but as the technology advanced that changed.

Here we have seen many directions in which AI has worked and has improved deliverables. This technology is growing day by day and showing improvement in many fields.

I, Anjuum Khanna, would give some insights on what Machine Learning. In simpler words machine learning is an application of artificial intelligence that enables the system to learn automatically and the best part of this application is you don’t need to program explicitly. Anjum Khanna also defines it in a different way that it focuses on the development of computer programs that can access relevant data and use it learn for themselves.

This learning process starts with data or observations (stated and observed), which we can provide in terms of examples or instruction. This learning can also be gathered through direct experience. Primary aim of machine learning is to allow computers to learn automatically without any human intervention or assistance. Machines should also adjust their actions accordingly.

As per author’s definition we can also say that machine learning is subfield of AI. We can see many examples of machine learning such as Siri, Netflix, Google maps, Uber etc. This can tell that how machine learning has upgraded our living.

We can learn more about it by knowing more about machine learning methods. As per Anjum Khanna below methods can tell better about machine learning:-

1. Supervised machine learning algorithms: – It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher is supervising learning process. We know the correct answers, the algorithm makes predictions on the basis of training data and it gets corrected by the teacher. Learning stops when the algorithm achieves an acceptable level in terms of performance. In Anjum Khanna’s words we can further learn this by few real life examples as

2. Unsupervised machine learning algorithms: – This type of machine learning is more closely aligned with artificial intelligence. On further analysis we can say that in this kind of machine learning a computer can learn to identify complex processes and patterns without a human guidance. Although unsupervised learning is prohibitively complex for some simpler enterprise use cases, but it is more effective while we need to solve problems that humans normally find difficult to tackle. In Anjum Khanna’s words we can further learn this by few real life examples as

I, Anjum Khanna has a very positive vibes that machine learning will do wonders in future. There are few predictions which will be true in future for machine learning:-

Usage in applications: – In next few years, machine learning will become part of almost every software application. Soon all of our devices will be embed with capabilities of machine learning. After that our personal device will become personalized device.

Usage in service industry: – As machine learning becomes increasingly valuable and the technology matures, more businesses will start using the cloud to offer machine learning as a service. This will allow a wider range of organizations to take advantage of machine learning without making large hardware investments or training their own algorithms. As in cutting throat competition personalized service is required in service industry and machine learning will resolve that issue.

So these are only few examples, there will be a great revolution with machine learning. But one trend is consistent across all of these predictions. As this technology advances, more businesses will embrace the AI revolution.

Anjuum Khanna – In 2017, United Airlines learned a hard and expensive lesson in customer experience. The airline lost over $1 billion dollars in value because a passengers’ customer experience went viral on social media.

In this digital age, customer experience is more important than ever. With the presence of social media and the consequent ability of a customers’ experience(s) going viral in a matter of minutes, more companies and organizations have recognized that the way a customer feels about their interaction with the brand can make or may the business.

In view of the importance of customer experience, how can you improve this vital aspect of your business?

1. Respond

Everyone wants to be heard and appreciated. Your customers are no different. Hence, listening and responding to the concerns of your customers is one of the most important steps in improving your customer’s experience. It might be hard work, but endeavor to respond to all the feedback you get from your customers. Doing this can work wonders for your customer retention.

2. Be Proactive

According to a report in the Harvard Business Review, your customers are left satisfied and develop a sense of loyalty when they spend as little time and effort as possible in getting their problems resolved. Thus, to improve your customer experience, ensure ease of access to help. Actively seek ways to reduce the time taken to receive help. Constantly be on the lookout for product and services that can make their lives easier. Your customer feedback is a great means of identifying new needs and generating more ideas.

3. Personalize

Customers appreciate customized experiences. Although technology is a great tool in business and service delivery, customized service build relationships that last. Your customers want to feel like they are your only customers; they want to feel valued and special. Hence, bulk, imprecise emails will not help. Give them personalized content and watch your bond grow stronger.

4. Reward loyalty

Rewarding loyal customers is a great way of improving customer experience. You can show that you appreciate their loyalty in various ways from personalized giveaways to sending greeting card to promotions and discount offers. If your customers feel that you are saying ‘thank you for your service’ this increases the chances of retention. Moreover, rewarded customers often become advocates for your business and can attract more customers.

Customer experience is an important facet of your brand. Use the tips above to improve it and enjoy the immense benefits it can bring!

Reach out to me or read more on CX, Automation & Digital Transformation on my blogs and website.

Anjuum Khanna-Customer behavior patterns continue to change. Complicating matters, customer loyalty and lifetime value continue to dip. Folk might wonder why but the answer is that customers simply have several many options before them and desire the best experience. In this scenario, it is imperative to ensure stackability or the loyalty of a customer.

I anjuum Khanna, sharing with you some Important key points:

1. Key to innovation

The rising demands of the customer could mean that they are looking for ease, comfort, or a wide variety of things. Understanding your customer segmentation and attaching them to the appropriate services is vitally important.

The key to innovation is slicing and dicing data to gain a better understanding of your customer and their journey. You have to know which customer base to sell that product to and where that product sells.

For instance, while working for a top DTH service provider, we decided to launch a line of products. In collaborated with Nielsen and after extensive market surveys, we found out the kind of content customers wanted to consume.

The majority of customers told us they would love to see acting or dancing and the actors and actresses they preferred. We now had a segmented customer base. We created a range of products with a human angle that the customer could buy as a package and learn from.

2. Collaboration

Where does innovation come from? It comes from Outside-In thinking. Human-Centered Design (HCD) allows products to be designed in collaboration with the customer to resonate more deeply with the audience. HCD tools ensure that the customer remains part of the entire product lifecycle or decision making life cycle.

Once the customer angle is brought in, that is when differentiation is possible. Be it airlines, automotive or consumer durables, when we say innovation, it means a customer demand for a product that needs to be seen to.

3. Customer Journey

When approaching the design of the product, it is vital that the customer be part of the entire product lifecycle. Questions need to be asked, surveys conducted and focus groups need to be conducted to check if the product is on the right or wrong path.

Look at the evolution of BFSI products. Previously, it would take a week or even two for a loan to be disbursed. Someone thought about it and realized it was a very primitive way of working. Enter Fintech. Today, all a customer need do is log in to a Fintech company, upload their documents and, if their loan is approved, receive the money in minutes. That is how innovation has changed the life of a regular banking process.

4. Prioritising Innovation

Understand the design thinking process to solving problems, where the entire leadership of an organization sits and understands the nuances of the customer. You need to be proactive. When coming up with a product, you need to see which pain points are an impediment to the NPS or CSAT scores. When you put your heads together, innovative ideas come about to address any deterrent to a smooth customer experience. Xiaomi is a great example of a positive customer experience. Despite their minimal marketing expenses, they are able to innovate and collaborate with the customers in their product range. Conversely, Meru Cabs, a previous incumbent taxi service, lost their first-mover advantage and market share when they failed to innovate as Ola and Uber stepped in.

5. The Big Winner

Whichever way innovation leads us, it will be the customer who ends up on top because, ultimately, everything we do is for the ease of the end user. Innovation will continue to remove biases from our minds and provides greater earning opportunities but the opportunities might not all be what you expect. When banks innovate, they might not want to hire more accountants. Instead, they might hire an algorithmic trader or creative genius. The future is bright and the possibilities endless as innovation continues to ensure our lives become more comfortable than they are right now.

Anjuum Khanna-Automation is a powerful force that is constantly simplifying the way the world functions. Automation helps remove redundancy and leads to job satisfaction by eliminating repetitive tasks. With the help of robotic machines, industries have been able to achieve what was humanly impossible. Several industries have been revolutionizing their processes using robotics, including the BPO/KPO industry.

The application of robotic machines in the BPO/KPO industry where we perform repetitive tasks can drastically reduce the amount of time taken to perform these tasks as compared to several human counterparts. This reduces the TAT to offer deliverables in an outsourcing industry and leads to an exponential growth.

The Robots/Algorithm can be programmed to perform the tasks with high precision, thereby improving the quality of the deliverables. The programs that are used to run these processes are based on algorithms that can be improvised constantly to accentuate Process Efficiencies. This kind of customization leads to increase in CSAT and thereby increases the customer loyalty and stickiness to the Brand. The robots are capable of performing end to end tasks needing minimal human monitoring in the completion. This reduces errors and improves performance.

Nowadays, robots are not only capable of performing repetitive precision tasks but also the kind of tasks that need decision making based on the available information. This is now possible with the help of AI (artificial intelligence). Combining artificial intelligence with robotics has created a new generation of smart robots that are capable of thinking, feeling, understanding and performing the tasks with their cognizance. These robots are capable of handling complex and challenging tasks in a constantly changing business environment. These advanced analytical skills are speedily closing the gap between the physical and digital world thereby creating a better Phygital world.

This new wave called Robotic Process Automation also known as RPA has swept away the back office tasks and revolutionized BPO industries worldwide. With the combination of robotics, big data and artificial intelligence, the world of business are transforming at a fast pace in terms of efficiency, effectiveness, and profitability. The application of robotic process automation, several BPO services such as regular chat and email jobs have been replaced with Chat Bots and self-help applications that constantly monitor and improve customer relationship with the service provider.

In this new world, the developing countries that were improving the economy by providing services are affected by the digital wave. The RPA has led to a severe reduction in headcount and in talent acquisition. The only way to survive in the digital age is to master the latest technologies and tools to drive them. In the future, BPO/BPMS industries will have fewer people and more robots in their structure. This need not mean to hurt the human workforce but can be taken as an upgrade that simplifies the lives of many and improves the ways of the world.