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Tag: Artificial Intelligence courses

How Much Do AI Researchers Make?

Posted on by Imarticus Learning
Reading Time: 3 minutes

What is Artificial Intelligence?

Also known as Machine Intelligence, Artificial Intelligence deals with the automation of machines so that they can perform human-like activities. Artificial Intelligence is being used in a lot of data-oriented industries such as insurance, healthcare, retail, technology, automotive, etc.

It also finds use in the finance sectors where various credit frauds and identity thefts need to be traced. Artificial Intelligence makes use of various machine learning algorithms to perform a specific set of tasks. Artificial Intelligence training is shaping our new world. It aims at achieving a specific goal by rationalizing the processes involved and then taking actions accordingly.

Who are AI-Researchers?

AI Researchers are those who apply the fundamentals of artificial intelligence to various data sets to draw out conclusions and various business insights. The job of AI Researchers includes language processing, algorithm building, development of data sorting mechanisms, etc. Also, it includes the movement and manipulation of data from various channels so that an efficient and effective transformation of data takes place with the utmost ease and efficiency.

AI researchers usually have a great understanding of various computer programming languages, hence making them proficient in what they do. Their expertise lies in building machines which reduces the human effort to a great extent. Artificial Intelligence researchers are in great demand owning it to the fast-paced technological environment and this ever-growing need for constant change. Thus, making AI Research a lucrative career path. People with less experience are also trying their hands in this area, given the financial attractiveness of this field.

How much do AI Researchers make?

Artificial Intelligence has become one of the most important elements of this data-oriented world. Companies are moving towards automation, making the best use of artificial intelligence and its ability to analyze, compile and assess huge data within a given time frame. The application of artificial intelligence requires expertise, thus demanding a pedestal in the corporate world. And this pedestal converts to a huge salary when converted into financial terms.

Artificial Intelligence is an area with a high demand for skilled personnel and less supply of the same, making it a really expensive affair for the companies looking forward to hiring AI Researchers and experts. The pay is the sweet spot for AI Researchers as the job role comes with huge money. The average salary for an AI Researcher in Silicon Valley is somewhere around $100,000 and $150,000 as it involves a lot of brainstorming and this pay is further increasing with the increase in applications of artificial intelligence.

Also, with increasing applications of Artificial Intelligence, the complexity of the operations is increasing, thus making the job of an AI Researcher a hotspot.

AI training and commands have such huge salaries as it provides a practical approach and puts the theoretical knowledge to some good use. Also, one can teach oneself and be ready for the market but understanding AI takes sufficient time and is not a cakewalk.

It is rapidly growing and the ones who are catching up with this growth are being rewarded in the form of really high salaries which keep them motivated to stay on the path as this growth graph of Artificial Intelligence is constantly going up and will not become flat anytime soon. As per Glassdoor, the average base pay of an AI Researcher is $111,118 per year which is pretty high when compared to other sectors of the economy.

Conclusion

Artificial Intelligence has made the world more dynamic than it was ever before. It is evolving at a very fast pace thus giving rise to a huge demand for AI professionals and the salary associated with the field is making it even more attractive for the generations to come.

We at Imarticus Learning offer Analytics and Artificial Intelligence courses at our centers in Mumbai, Thane, Pune, Bangalore, Chennai, Delhi, Gurgaon, Noida, Ahmedabad, and Jaipur.

What Are Important Ways That AI Is Helping E-Commerce Stores?

Posted on by Imarticus Learning
Reading Time: 3 minutes

 

The Ecommerce Industry

The e-commerce industry has proved to be a boon for all the shopaholics who are too lethargic for a regular brick and motor engagement. Growing in double digits the expansion in the e-commerce industry is unmatched by any other and with the potential to grow multiple folds in the coming years it has set new highs.

In a broad sense of things, the concept behind the e-commerce world is simple, creating on the online market place with multiple stores available to shop anytime using the means of smartphones and other computerized devices that support web surfing.

The virtual market is not bounded by geography, having its customer base all across the world. What’s different about this shopping escapade is that it makes the entire store available for you to facilitate your shopping spree, all with a few clicks. I wonder how many times it happens that I am not sure about what exactly I need to purchase unless acquainted with the varieties available.

Now if we have to walk by several stores to find out what could be bought it will be tiresome, to say the least. Let’s assume that we somehow managed to step into each of them, how will we compare all the available products in real-time? That’s where the e-commerce industry adds value and steals the show with convenience.

The e-commerce stores not only help to bring everything together but also helps to search select and choose by providing valuable suggestions and insightful product descriptions. It also lets you read into the feedback provided by the users of the products that might help you buy better.

In the tangible world, we have a shop for every need, we have shopping complexes for multiple segments. This evolution went a little further in the era of the internet with e-commerce where we have all the product segments from all the known brands under a few keystrokes.

AI applications in the e-commerce industry

While shopping at stores with a physical address on the map, what attracts the most apart from quality goodies is the presentation and organization of the products.

Similarly when buying goods online what helps increase engagement and purchase? The answer is better to search for tools and classified product segments. This is where AI fits into the e-commerce must-have tools.

The high-tech AI-enabled solutions can also help in searching product descriptions and other relevant details to form a variety of keywords that might match the user’s search and help discover the product better. This doesn’t stop here, the AI-powered solutions also help with product selection by asking some intelligent questions and narrowing down the list for us.

At times it so happens that we know what we are looking for but the name is unknown to us and thus we feed in a variety of keywords to complete our search. The predictive search mechanism provided by Artificial Intelligence training uses the past search and purchases history helping us identify what we might be looking for with relative ease saving a lot of time and keystroke efforts.

Arrangement of products and tidiness are some of the key drivers of customers in the traditional brick and motors store, how do you implicate this approach online? Well, the answer doesn’t require a brainstorming session, it is through the website design.

Making the website aesthetic needs a well-planned web design that not only looks good but also goes along with the objective of the website. From optimized website design testing to improving decisions with auto traffic analysis & better sales funnel structuring, AI delivers on all aspects of customer conversions and engagement.

In present-day scenario conversational chatbots are mainstream for better customer servicing, it could also be seen as a norm, whatever site you visit for your purchase you are bound to be greeted by a bot. This evolution has propelled further with a new wave of intelligent sales chatbot. This new AI by-product is hyper-personal in their functioning, providing customized recommendations and suggestions for better conversion.

Conclusion

AI has improved the e-commerce industry to a great extent by providing better search options for product searches to suggesting an optimized website layout for better conversions. Apart from the mainstream chatbots for customer servicing this new AI wave has welcomed the trendy sales chatbot that uses customer preferences data for good by providing customized and hyper-personal shopping experience.

What Are The Important Ways That AI Is Helping E-Commerce Stores?

Posted on by Imarticus Learning
Reading Time: 3 minutes

What Are The Important Ways That AI Is Helping E-Commerce Stores?

The e-commerce industry has proved to be a boon for all the shopaholics who are too lethargic for a regular brick and motor engagement. Growing in double digits the expansion in the e-commerce industry is unmatched by any other and with the potential to grow multiple folds in the coming years it has set new highs.

In a broad sense of things, the concept behind the e-commerce world is simple, creating on an online marketplace with multiple stores available to shop anytime using the means of smartphones and other computerized devices that support web surfing.

The virtual market is not bounded by geography, having its customer base all across the world. What’s different about this shopping escapade is that it makes the entire store available for you to facilitate your shopping spree, all with a few clicks. I wonder how many times it happens that I am not sure about what exactly I need to purchase unless acquainted with the varieties available.

Now if we have to walk by several stores to find out what could be bought it will be tiresome, to say the least. Let’s assume that we somehow managed to step into each of them, how will we compare all the available products in real-time? That’s where the e-commerce industry adds value and steals the show with convenience.

The e-commerce stores not only help to bring everything together but also helps to search select and choose by providing valuable suggestions and insightful product descriptions. It also lets you read into the feedback provided by the users of the products that might help you buy better.

In the tangible world, we have a shop for every need, we have shopping complexes for multiple segments. This evolution went a little further in the era of the internet with e-commerce where we have all the product segments from all the known brands under a few keystrokes.

AI applications in the e-commerce industry

While shopping at stores with a physical address on the map, what attracts the most apart from quality goodies is the presentation and organization of the products.

Similarly when buying goods online what helps increase engagement and purchase? The answer is better to search for tools and classified product segments. This is where AI fits into the e-commerce must-have tools.

The high-tech tech AI-enabled solutions can also help in searching product descriptions and other relevant details to form a variety of keywords that might match with the user’s search and help discover the product better. This doesn’t stop here, the AI-powered solutions also help with product selection by asking some intelligent questions and narrowing down the list for us.

At times it so happens that we know what we are looking for but the name is unknown to us and thus we feed in a variety of keywords to complete our search. The predictive search mechanism provided by AI technology uses our past search and purchase history helping us identify what we might be looking for with relative ease saving a lot of time and keystroke efforts.

Arrangement of products and tidiness are some of the key drivers of customers in the traditional brick motors store, how do you implicate this approach online? Well, the answer doesn’t require a brainstorming session, it is through the website design.

Making the website aesthetic needs a well-planned web design that not only looks good but also goes along with the objective of the website. From optimized website design testing to improving decisions with auto traffic analysis & better sales funnel structuring, Artificial Intelligence Training delivers on all aspects of customer conversions and engagement.

In the present-day scenario conversational chatbots are mainstream for better customer servicing, it could also be seen as a norm, whatever site you visit for your purchase you are bound to be greeted by a bot. This evolution has been propelled further by a new wave of intelligent sales chatbots. This new AI by-product is hyper-personal in its functioning, providing customized recommendations and suggestions for better conversion.

Conclusion

AI has improved the e-commerce industry to a great extent by providing better search options for product searches to suggesting an optimized website layout for better conversions. Apart from the mainstream chatbots for customer servicing this new AI wave has welcomed the trendy sales chatbot that uses customer preferences data for good by providing a customized and hyper-personal shopping experience.

5 ways AI is Utilized in Advancing Cancer Research

Posted on by Imarticus Learning
Reading Time: 2 minutes

When it comes to the health of a person, life and death become a matter of problem. Health care centers and medical professionals all over the world are now leveraging the power of AI, to research a plethora of ailments. One such case is cancer research. Cancer is a disease which results in the uncontrollable division of cells and hence the destruction of body tissues. This problem can be solved with the help of artificial intelligence as it is nowadays providing favorable outcomes in every field. It can help in early detection of cancer and the treatment can prove to be very successful.
 Here’s a list of 5 ways Ai is being utilized in advancing cancer research: 

  1. Machines fed with adequate data and programmed with advanced algorithms can make use of past medical records during surgery of a patient. This is possible only with the help of artificial training. Researchers have found that there are approximately 5 times fewer complications in a robotic procedure of surgery in comparison to surgeons operating alone.
  1. Artificial intelligence can be used to interact with patients by directing them the most effective care, answering the questions, monitoring them and providing quick solutions to their problems. Most applications of virtual nursing include fewer visits to hospitals and 24 hours of care to the patients.
  1. Healthcare providers also make use of artificial intelligence to diagnose patients. Early diagnosis of cancer has now become a necessity, as any delay can cause a difference between life and death.

According to a recent study, artificial learning methods can help to classify the patients into high or low-risk groups. The study further added that AI has a great impact in the area of cancer imaging as artificial intelligence can analyze more than 10000 skin images with higher sensitivity.

  1. Complicated tests and analysis, such as CT scan and internal imaging have turned out to be hassle-free with the help of AI-enabled systems. It reduces the chances of any manual error and helps the doctors to diagnose the condition before it becomes critical.

According to a study AI has proved to be 99% accurate and more than 25 times faster in detecting breast cancer. Artificial intelligence can also be used to find out vertebral fractures if any.

  1. AI has the potential of developing lifesaving drugs and saving billions. Engineers have developed algorithms that can analyze the potency and effectiveness of the medicines developed for treatment. It also helps them to make better decisions related to healthcare.

Most of the people even use wearable technology based on artificial intelligence to check out their sleep patterns and heart rate. Applying artificial intelligence to detect cancer can inform healthcare providers about specific chronic conditions and manage the disease in a better way.
So there are various cases where artificial intelligence can find its application. Artificial intelligence training can help the individual to enhance their skills and knowledge in the field of artificial intelligence.
Imarticus Learning is one of the leading institutes that provide numerous courses in data science, machine learning, blockchain, etc. The institute takes pride in helping students make a career in artificial intelligence. AI has improved its application in the past few years and is expected to revolutionize the world in many ways in the coming years. Thus, having good artificial intelligence training will prove to be useful in all fields. You can have such good knowledge with the help of experts and qualified staff at the institute which can help you to shape your career in a better way.

AI and Food: Safer and More Tasty Food?

Posted on by Imarticus Learning
Reading Time: 4 minutes

 

In February 2019, Tristan Greene wrote an article in The Next Web and quoted an IBM research study that suggested that artificial intelligence could improve the taste of food by creating new hybrid flavors. It took a part of the Internet by storm, less for its clickbait headline and more for its actuality. Greene was writing facts when he began his article with this: “AI will soon decide what we eat”.

Let’s explore the what, the why, and the how. We are sure you already know the why so we’ll mostly skip it.

Artificial Intelligence + Food. Really?

That seems to be a sensible question but not a surprising one. AI and machine learning have already taken over the world with them influencing everything from blockchain to computer vision to chemistry. So why not food production?

Now IBM, other tech giants, and new startups are changing that by feeding AI systems millions of different types of data in the areas of sensory science, consumer preference and flavor palettes to help generate new or advanced flavours that can literally put your mouth on fire. Or make it drool all day. Or make even the most tasteless food taste like heaven. Kale and quinoa, anyone?

The food industry has already scrambled to use artificial intelligence and machine learning for its sake. Take, for example, the world’s first automatic flatbread-making robot called Rotimatic which limits user control to just putting the ingredients into the appliance. It does all the dirty work by itself and claims to bake hot flatbread in under a minute.

Not just kitchen appliances, the food that we eat and its ingredients are also being influenced by AI and other techniques even as we debate whether genetically modified food products are safe for human consumption. Researches involving changes in the cooking style, omission or replacement of certain ingredients, and others have all been suggested by AI-driven tools. While none of them have hit the shelves yet, this new tool by IBM looks like it’s just around the corner.

According to the study, IBM and a company pioneering in flavors and food innovation named McCormick & Company created a novel AI system whose aim is to create new flavours. Published in February 2019, the blog post promised that some of its findings will be available on the shelf by the end of the year. While it is September and we still wait, let’s have a look at the scope of AI in the food industry.

How Does AI Help Food Become Better?

To answer this question, Greene uses the analogy of Google Analytics tools. Publicly available data like recipes, menus, and social media content about these recipes along with trends in the food industry are fed to AI systems. These then generate fresh, actionable insights.

An example is a tool that can show restaurants what the most popular food will be every month for the next 12 months. If this is a possible scenario, the restaurant can prepare itself and maybe even surprise its customers into submission, eventually becoming popular and running a successful service.

The same goes for farming models where new techniques are needed to plant and grow more produce as the population gets out of the window due to lack of space. Everyone involved in researches dealing with AI and the food industry is positive about what can be done.

Existing data is of prime importance if such tools are to bear any results. In the above example involving IBM, the tool is able to create new flavors because of the existence of data on different flavours that we currently have. In a way, AI is only helping us discover flavors sooner.

AI Everywhere in the Food Industry

Till now, we spoke about the use of AI in farming, food recipes, and restaurants. But what about food processing? Media suggests that AI is everywhere – from its help in sorting foods to making supermarkets more super.

According to a Food Industry Executive, there are a lot of examples that highlight the significance of AI in the food industry. Some of them are listed below, thanks to Krista Garver:

  • Food sorting – AI helps understand which potatoes (by their size and quality and age) should be made into French fries and which ones are suitable for hash browns or potato chips or some other food. This involves the usage of cameras and near-infrared sensors to study the geometry and quality of fruits and vegetables
  • Supply chain management – This is obvious: food monitoring, pricing and inventory management, and product tracking (from farms to supermarkets)
  • Hygiene – AI can detect if workers are wearing all the necessary equipment. Since AI tools are fed data about what constitutes 100% hygiene, they can constantly check the attire of workers and rate them on the basis of their current clothing. Is a worker not wearing a plastic hat? An alert goes to his manager
  • New products – This is similar to the IBM example seen above. Predictive algorithms can be used to understand what flavors are most popular in people of certain age groups. Why do kids love Kinder Joy? What is or are the ingredients that make them go bonkers?
  • Cleaning – This is the most promising one where ultrasonic sensing and optical fluorescence imaging can be used to detect bacteria in a utensil; this information can then be used to create a customized cleaning process for a batch of similar utensils.

Conclusion

It is mind-numbing (mouth-watering, too?) to visualize these products actually coming into form in a few years. Which is why there is no doubt that AI will revolutionize the food market. The only question that then remains: has the revolution already begun now that you can’t say no to a bunch of addictive products?

How Artificial Intelligence Help To Transform Employee Productivity?

Posted on by Imarticus Learning
Reading Time: 3 minutes

How does Artificial Intelligence Help To Transform Employee Productivity?

Every company moves towards becoming a tech company, and AI-enabled computers and bots will enter the field of how recruitment, on-boarding, training, and working happens at our workplaces. Here are just some of the areas where successful artificial intelligence courses used by tech companies to train smart machines could be used in the foreseeable future.

Onboarding and recruitments:

Did you know that many companies use AI-enabled systems to scan and identify the right person for the job in most smart tech companies? They can effectively and accurately wade through millions of applications and gain foresight from their profile sampling techniques to invite the deserving candidate.

Pymetrics uses neuroscience-inspired “games” to assess Artificial Intelligence Courses with emotional and cognitive features of the profile avoiding any human bias on gender, status, race or socioeconomic factors. They compare the new profiles to their inhouse data of profiles of persons who were successful at the job being recruited for. It can also make lateral options a choice for candidates who are not just right for the particular job but fit other open vacancies.

Similarly, Montage, with the top 100 amid Fortune 500 companies as clients use an AI-driven interviewing tool which can undertake automated scheduling, on-demand text interviewing and such to reduce unconscious biasing in recruitments.

Chatbots are not just for customer service and help the new recruits settle in better. Unabot, used by Unilever is a good example of using NLP (natural language processing) to answer queries on payroll and HR with advice to employees in plain and simple human language.

On-the-job training:

The entire learning process and training is full of examples of AI-interventions. They help garner from older experiences and transfer to new recruits the wealth of information required for being successful on the job. Honeywell uses AR/VR to capture the work experience and learn “lessons” from it to be passed on to new hires. Such tools keep records, use image recognition technology, play these back, provide real-time feedback, issue reminders, and help in a VR experience of the role.

Augmented workforce

Fears that AI will replace workers and take over their jobs, is baseless. The very aim of AI is to aid the workers and one should exploit the help in increasing productivity, efficiency and augmentation of the workforce since AI brings many benefits to its applications. Humans can better use their faculties for creative and human-interaction based areas of work in artificial intelligence courses since machines do need human interaction and maintenance too.

Machines have proven skills in repetitive tasks, providing insights into large volumes of data and the potential for predictive trend analysis. PeopleDoc, Betterworks and such can go a long way in bettering the day-to-day workplace experience with monitored processes and workflows and processes and RPA-robotic process automation.

Surveillance in the workplace

Are you aware that according to a Gartner survey, half the companies with 750million USD make gainful use digital data-gathering tools to monitor employee performance and activities? This includes employee engagement and satisfaction levels. Some companies use tracking devices to monitor bathroom breaks and audio analytics to determine voice stress levels. Others use the carrot of fitness and exercise programs through traceable Fitbits. Workplace Analytics is used by Humanyze on staff email and IM data, and microphone-equipped name badges. Not all AI is bad as bullying, stalking and security are good goals. Right?

Workplace Robots

Physical autonomous movement robots are fast becoming the means of access for warehousing and manufacturing installations. Robots like Segway have a delivery robot while, security robots like Gamma 2 keep the trespassers away, and ParkPlus helps you find parking slots. Include the automatic shuttles and driverless cars at workplaces and wonder why we humans are still complaining.

Conclusion: 

Though the concepts have been around for ages the past two decades have seen a phenomenal and sustained increase in ML/AI applications. Artificial intelligence is the ability of machines to simulate neural networks and human intelligence without the use of any human intervention or explicit programming. Machine learning is a subset of AI technology that develops complex algorithms based on mathematical models and data training to make predictions whenever new data is supplied to it for comparison.

Do you want to succeed in artificial intelligence courses? Then learn with Imarticus Learning for becoming career-ready and skilled. Why wait?

For more details in brief and for further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi and Gurgaon.

What Are The Prerequisites For Artificial Intelligence?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Artificial intelligence keeps changing in its definition as does its scope and capabilities. A few decades ago, simple calculators were considered artificial intelligence since math problems were previously only solved by the human brain. Today, artificial intelligence powers home automation systems and gadgets like Google Home, Siri, and Alexa. We see new AI being released almost every week with juggernauts like Google and Facebook it improve the user experience. The auto-reply feature with suggested replies on Gmail is an example of artificial intelligence where the responses are ‘taught’ to the machine.
Having a good foundation is imperative if you want to foray into artificial intelligence. It isn’t as simple as attending a machine learning course to be a valuable employee in the field of AI. People who are interested in artificial intelligence can take several paths to learn the various AI skills necessary for the subject. Based on your previous knowledge and skill level, you should chart your own course.

The prerequisites of artificial intelligence will give you a good foundation to stand upon when you are learning the key concepts. You will have to have a good foundation in calculus, linear algebra, and statistics in order to help you to develop algorithms. You will also need a good knowledge of Python and Python for data science track as it is the predominant language used in machine learning.
Whatever math skills you might have already, you might want to brush up on them before foraying into Artificial Intelligence. There are many courses available online that will go into depth about the various concepts used in AI. If you are getting into AI to solve a problem, then you can rely on existing libraries to help you with the math required. However, if you are looking to get into research or deep into machine learning, you will have to get an in-depth knowledge of math.
The next steps involve learning and soaking up as much machine learning concepts and theory as you can. It will help you on many fronts including planning and collecting data, interpretation of model results, and creating better models.
The next step should focus on data cleaning, exploration, and preparation. As someone who will be working with machine learning, you will have to have a good quality of feature engineering and data cleaning on the original data you have. This is a very important step and will regularly feature in your work in the future. You should spend as much time as you can here, doing practice tests and runs.
For practice, you should participate in as many Kaggle competitions as you can. These are generally easy and will help you work with multiple scenarios and typologies. With machine learning, the more practice you have, the better you are.
As a beginner, these are the steps you will have to take in order to understand the basics of artificial intelligence. If you are interested in a deeper understanding of the subject, then you can opt of Deep Learning and Machine Learning with Big Data.

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What is the practical advice for Machine Learning?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Read, and re-read resources on introductions to Calculus, Mathematical statistics, both differential and inference, algorithm analysis, optimization, differential equations, linear algebra, Python, R and more. Does that sound difficult?

You don’t need advanced learning in them. You will however essentially need to understand how you can apply this learning to handling data analysis of the present and future of nearly every field under the ML, AI, Deep Learning and VR fields.

Here are some advantages of machine learning training in such courses.

  • You get to learn ML fundamentals and basic algorithms, statistical pattern recognition, data mining, statistics including Bayesian probability, working with Python, Pandas, and R, the Sci-learn and Tensorflow APIs, and more in a well-paced, learn-at-your-convenience online and classroom training mode.
  • The integrated curriculum helps you through practical industry-needed and relevant practical applications like

1. Unsupervised learning (deep learning, clustering, recommender systems, dimensionality reduction)

2. Supervised learning (neural networks, support vector machines, parametric/non-parametric algorithms, kernels)

3. ML best techniques and practices (variance and bias theory, AI, and innovation in the ML process).

  • Most learning is through applications, case studies, live-industry-project and effective mentoring, virtual classes, workshops, hackathons, and such support.
  • Your certification carries weight as it declares you have applicative knowledge and our job and industry ready.

Data Science Course

That having been said, here are some practical tips for ML and discerning learners.

  • The first timers in ML rarely get things right. Don’t panic. ML skills are cultivated skills and are meant to be regularly practiced.
  • Implement your learning through a model. Compare your implementation skills with others while discovering the open-source libraries, mathematical or program techniques, and tricks, math-tools, etc. that can improve your efficiency.
  • Don’t get overwhelmed because leveraging your skills means research-work and doing small projects which help assimilate learning and applying the learning to practical situations whether they be smartphones, VR or chatbots. The tools in Python take care of the math while you get your hands deep into data analysis, data cleaning, and mining and data exploration and predictive analysis.
  • It isn’t just about math for beginners. Most often it is about data, data and more data! So get cracking in honing your data analysis skills.
  • Apply your learning to building algorithms like perception and control for robotics, building smart robots, anti-spam, and web-search text understanding, medical informatics, computer vision, database mining, and audio based applications.
  •  Attend hackathons (Kaggle, TechGig, Hackerearth, etc.) which give you support, exposure and mentorship in  ML practical ideas.
  •  Build your portfolio with projects

a. Where you collect the data yourself

b. Where you get exposure to data cleaning, dealing with missing data, etc.

  • Master areas that you like to work in like Neural Networks, AI, and ML as applied to image segmentation, speech recognition, object recognition and VR.

As in all fields, it does get easier as you progress and get adept. So why wait? Partner with Imarticus courses and get a head-start in ML. Go ahead and do a machine learning course with a reputed training institute like Imarticus.

The Ultimate Glossary of Terms About Machine Learning

Posted on by Imarticus Learning
Reading Time: 3 minutes

Machine learning is an artificial intelligence application which provides computer systems with the ability to learn on its own and improve with experience without any explicit requirement of additional programming. Machine learning has its focus on developing computer programs whole can access data and utilize the data to learn on its own.
Some of the commonly used terminology used in Machine Learning are as follows:

  • Adam Optimisation

It is an algorithm utilized to train models of deep learning and is an extension of the Stochastic Gradient Descent. In this algorithm, the average is run employing both gradients and using the gradient’s second moments. It is useful for computing the rate of adaptive learning for every parameter.

  • Bootstrapping

It is a form of the sequential process wherein each subsequent model tries to correct the errors in the earlier models. Each model is dependent on its previous model.

  • Clustering

It is a form of unsupervised learning utilized for discovering inherent groupings within a set of data. For instance, a grouping of consumers on the basis of their buying behavior which can be further used to segment the customers. It provides useful data which the companies can exploit to generate more revenues and profits.

  • Dashboard

It is an informative tool which aids in the visual tracking, analysis of data by displaying key indicators, metrics and data points on a single screen in an organized manner. Dashboards are often customizable and can be altered based upon the preference of the user or according to the requirement so of a project.

  • Deep Learning

It is a form of a Machine Learning algorithm which utilized the concepts of the human brain towards facilitation of the modeling of arbitrary functions. It requires a large volume of data, and the flexibility of this algorithm enables multiple outputs of different models at the same time.

  • Early Stopping

It is a technique of avoiding overfitting while training an ML model using iterative methods. Early stoppings are set in such a manner that it halts the performance of improvement on validation sets.

  • Goodness of Fit

It is a model which explains a proper fitment with a set of observations. Its measurements can be summarised into the discrepancies between its observed values with that of the expected values using a certain model.
This Machine Learning Course is a good fir when the errors on the models which are on training data along with the minimum test data. With time, this algorithm learns the errors in a model and corrects the same.

  • Iteration

It is the number of times the parameters of an algorithm is updated during training of a dataset on a model.

  • Market Basket Analysis

It is a popular technique utilized by marketers for identification of the best combination of services and products which are frequently purchased by consumers. It is also known as product association analysis.

  • MIS

Also known as Management Information System, it is a computerized system comprising of software and hardware which serve as the heart of a corporation’s operations. It compiles data from various online and integrated systems, conducts an analysis on the gathered data, and generates reports which enable the management to make informed and educated business decisions.

  • One Shot Learning

This form of machine learning trains the model which a single example. These are generally utilized for product classification.

  • Pattern Recognition

It is a form of machine learning which focuses on recognizing regularities and patterns in data. Some examples of pattern recognition used in many daily applications include face detection, optical character recognition, object detection, facial recognition, classification of objects etc.

  • Range

It is the difference between the lowest and the highest value in a data set.

What is the Best Programming Language For Artificial Intelligence Projects?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Artificial Intelligence is the hot topic of the last couple of years and is all set to be the science of the future. It has already opened up a realm of possibilities for humans, and by taking advantage of a machine and deep learning, it is no doubt going to play a huge role in the future of humanity. You can do almost anything with this technology – even build apps which can hear, see and react accordingly.

A lot of newcomers are beginning to get into programming for AI, considering how important it is turning out to be. However, with the plethora of options available, it can be difficult to choose a particular language for programming. Let us consider the many languages which are currently being used for AI development.

Python
Currently rising in popularity, it is one of the main languages which come up in how to learn machine learning. Being extremely simple to use and learn, it is preferred by many beginners. Compared to other languages like C and Java, it takes extremely less time for implementation.

Another advantage is that with Python, you can opt for procedural, objective oriented or functional style of programming. There are also a lot of libraries which exist for Python, which make programming considerably easier.

Java
A comparatively older option, it first emerged in 1995 – however, it’s importance has only grown at an unparalleled rate since then. Highly portable, transparent and maintainable, this language also has a large number of libraries to make it easier for the user.

Java is incredibly user-friendly and easy to troubleshoot and debug, and the user can also write code that runs on different platforms with ease. The Virtual Machine Technology implemented in Java is key to this feature, actually. Many Big Data platforms like Apache Spark and Hadoop can be accessed using Java, making it a great all-around option for you.
Julia
Developed by MIT, this language is meant for mathematical analysis and numerical computing to be done in a high-performance fashion. These features make it an amazing choice for AI projects since it was designed keeping the needs of Artificial Intelligence in mind. Separate compilation is done away with, too – however, it is only growing, so it does not have the same number of libraries as the others.

Haskell
Haskell, unlike Java, is a great choice for engaging and working with abstract mathematical concepts. You can create AI algorithms using the expressive and efficient libraries which come with the language, and the language is far more expressive compared to many others.

Probabilistic programming is also a cakewalk since developers are able to identify errors relatively quickly, even during the compile phase of iteration. However, you still cannot expect the same level of support that Java and Python offers.

You will need to learn some machine learning skills, if you are to have a long career in this field – in order to do that, you should check out the big data and machine learning courses on offer at Imarticus Learning.