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Tag: Artificial Intelligence courses

Does Machine Learning Excite You? Check Out Our Data Analytics Course!

Posted on by Imarticus Learning
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Machine learning (ML) is truly a blessing to modern computing and technology, possessing the ability to endow systems and machines, the ability to think for themselves and tackle tasks on their own without any supervision of humans. Machine learning is able to do this by creating artificial neural networks which simulate how human brains work. Machine learning is assisted by data science and supports its applications in various fields.

Even though machine learning was initially invested upon with the primary focus on Artificial Intelligence, it was later recognized as a separate field and started being heavily invested upon from the 1990s and is one of the most valuable fields of computing that has one of the highest industry requirements of skilled professionals and freshers holding expertise in various skills and tools which assist in machine learning.

In this article, we will learn more about machine learning and how a well-planned data analytics course can help you progress in your career if you are already in this field or how it can help freshers get exposed to ML. 

What is machine learning?

Machine learning first came into existence due to the interest of having systems and computers learn from data on their own. “Machine learning” was first termed by Arthur Samuel in 1959, who was working in IBM at that time. During his tenure there, he was responsible for various important projects related to computer gaming and AI. It all started when Mr. Samuel took the initiative to teach computers how to play games through the game of Checkers on IBM’s first commercially available computer, the IBM 701.

Eventually, machine learning started being used for various purposes and borrowed many models and approaches from statistics and probability theory. AI uses predictive analytics along with machine learning to execute the various responses or trigger actions. All of this is acquired from the training data set which helps the machine in learning and equips it with the information.

Machine learning is an important branch of computing and data science that creates autonomous systems which learn from data on their own. A machine trained with clean processed data eventually identifies trends and patterns to respond to situations without human supervision.

Machine learning also promotes the automatic improvement and development of algorithms or data models which improve on their own. Machine learning is an important part of Artificial Intelligence which uses data mining, predictive analytics, and various tools to assist machines in learning more extensively with methods like deep learning to allow them to execute functions that emulate the responses of a human, just much more accurate and fast.

Machine learning is also not biased unless specifically asked to do so, hence promoting unbiased AI-supported systems that make fewer errors. Data mining is also a very relevant field and quite valuable to machine learning as it helps systems come to conclusions without having some bits of data or having unknown bits of information. Machine learning is a type of predictive analytics which is backed by data and is exploratory in nature.

Perks of a Data Science Prodegree from Imarticus

The Data Science Prodegree is a great data science course that students and working professionals can choose to gain more exposure and skills in the fields of machine learning, business analytics, and AI.

 

  • Acquire skills and learn how to use required tools and algorithms
  • Gain valuable industry and course certifications
  • Get placement support and opportunities from the best companies
  • Advanced live classroom learning supported by technology and real-life projects

 

Imarticus’s Data Science course with Placement is a great choice if you wish to advance in your career and learn about machine learning, AI, business analytics, or data analysis which will help you become more effective as a data scientist and pursue your dream career in this respectable field.

Regression and classification metrics with python in AI/ML

Posted on by Imarticus Learning
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Python is one of the most popular languages used in data science. It has a massive library that makes it easy for anyone to conduct machine learning and deep learning experiments. In this blog, we will be discussing regression and classification metrics with python Programming in AI/ML.  

We will show how to use some of these metrics to measure the performance of your models, which can help you make decisions about what algorithm or architecture might work best for your application or dataset!

What is a regression metric?

A regression metric measures how accurately a machine learning model predicts future values. To calculate a regression metric, you first need to collect predicted and actual values data. Then, you can use various measures to evaluate how well the model performs. 

How to use classification metrics with python Programming in AI/ML?

A classification metric or accuracy score measures how accurately a machine learning model predicts the correct class label for each data point in your training dataset. Once you have a classification metric, you can evaluate your machine learning model’s performance. 

You can use many different classification metrics to measure performance for a classifier machine learning model. Common ones include accuracy score, precision, recall, actual positive rate, and recall at different false-positive rates. You can also calculate the Matthews correlation coefficient (MCC) to measure how well your model performs.

Accuracy Score:

Accuracy score measures how often the predicted value equals the actual value. It’s also known as error rate, accuracy, or simply classification accuracy. You can calculate the accuracy score by dividing the total number of correct predictions from all predictions made.

Precision:

Precision is the number of correct predictions divided by the number of predictions made. 

Recall:

Recall, or valid positive rate is the number of correct predictions divided by the number of positives. You can calculate how well your model performs for different classes by plotting a ROC curve and calculating the AUC.

False Positive:

False-positive is also known as Type I Error or alpha error in statistical hypothesis testing. It’s when your model predicts that an instance belongs to one class, but it belongs to another.

False Negative:

False-negative is also known as Type II Error or beta error in statistical hypothesis testing. It’s when your model predicts that an instance belongs to one class but belongs to another, and the actual value isn’t present in training data. 

Matthews Correlation Coefficient (MCC):

The Matthews correlation coefficient measures how well your model predicts the labels of unseen instances from training data. 

Area Under Curve (AUC):

The AUC score measures how well your model predicts future values by plotting a ROC curve and calculating the area under it.

Discover AIML course with Imarticus Learning

This artificial intelligence course is by industry specialists to help students understand real-world applications from the ground up and construct strong models to deliver relevant business insights and forecasts. 

Course Benefit For Learner: 

  • Students get a solid understanding of the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies.
  • Learn data science skills by participating in 25 in-class real-world projects and case studies from business partners.
  • Impress employers & showcase skills with artificial intelligence courses recognized by India’s prestigious academic collaborations.

Contact us via the chat support system, or drive to one of our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon. 

Steps to create a dashboard in Tableau

Posted on by Imarticus Learning
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If you are having trouble with the excel sheets and finding it hard to create complex formulas to deal with the data, the Data Visualisation tools such as Tableau are here for the rescue! It can help use unmanageable data into beautifully crafted interactive dashboards. Those who have Tableau certification are hot properties in the Data Analysis and Data Science fields. 

Tableau is easy to use, works faster, and is also easy to set up. The tool is available in both paid and free versions. Knowing how to operate this will be an added advantage while doing any Artificial Intelligence and Machine Learning course. 

Getting this software and creating a dashboard is required to get a simplified version of the raw data that you are dealing with. SO, here are the steps to create a dashboard in tableau. 

Step #1 Creating a dashboard

Download and install the Tableau software to start the process. Once it is set up, open it and click on the ‘New Dashboard’ button to create a fresh one. Give an appropriate name to this dashboard so that it is easy to identify.

Step #2 Adding sheets to the board

The next step is to bring in the excel sheets with the data that you need to work on. Drag the required sheet onto the dashboard space. Alternatively, you can also select the most relevant data from a sheet, instead of the whole sheet. 

Step #3 Add additional sheet(s)

Tableau dashboard allows adding as many sheets as required for the data analysis. The additional sheets may be added in the relevant space and can process the data on all or selected sheets, as required.  

Step #4 Customization

The next step is where you customize or filter the data as per the requirement to create an interactive dashboard. Choosing layouts, adding images or texts, navigation to move from one data to another set, etc can be done at this stage. 

Step #5 View and share the data

Once all the customization is complete, view the data in full-screen mode and see if any changes are needed. Once satisfied, you can share the dashboard with others for review and discussion. 

Why is Tableau important?

Tableau is a data simplifying tool that helps manage a vast data resource. It can easily blend with AI to make faster and smarter decisions regarding the data. It also assists in integrating the data to work directly with various models of Machine Learning. Having a Tableau certification will be an asset, especially one pursuing an IIT Artificial Intelligence course or an Artificial Intelligence and Machine Learning course. Integrating the results of data interpretation decisions made using AI and ML into visualized data using Tableau helps people understand it better. 

best artificial intelligence courses by E&ICT Academy, IIT GuwahatiTableau opens a way for qualifying and quantifying the data while also identifying any particular pattern with the missing data. It is one of the robust tools that is required while implementing the machine learning models or solutions so that the business side can also understand and visualize the analysis or predictions. 

Conclusion

While the top-rated Artificial Intelligence course by E&ICT Academy, IIT Guwahati can develop a qualified and faster data analyst, the Tableau tool helps them be smarter. The drag and drop spaces on the Tableau dashboard allow you to investigate and relate the data with the intended outcome. It can work with multiple external data resources, not just the excel sheets. In other words, a single dashboard can combine data from various sources. Here, the visualization is possible with the help of charts, graphs, maps, tables, and some advanced methods.   

best Artificial Intelligence courses by E&ICT Academy, IIT Guwahati

What Is Distributed Computing Training in Machine Learning?

Posted on by Imarticus Learning
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The traditional machine learning approaches rely on using open-source tools for data analysis and prediction making. This approach does not work out well when the data is large. The RAM on the system gets damaged when large files like these are involved. We need to use an approach that not only helps us build the machine learning models successful but also ensures that the system is not burdened or damaged while an operation is being performed. Hence, we need to learn Distributed Computing in Machine Learning.

What is distributed computing?

An approach to improve the system performance, resolve scalability issues and increase the system efficiency by dividing the task being performed on a single machine to different systems is called distributed computing.

Distributed computing has many applications such as the world wide web, global financial systems, machine learning and much more. Here we concentrate basically on the concepts of Machine Learning Training with distributed computing.

Distributed computing training 

The main purpose of this training in machine learning is to help an individual master the skills in machine learning and resource allocation and management. Distributed computing came up as a technique to resolve the scalability associated with machine learning algorithms. It developed on a massive scale in recent years to provide large-scale operations such as big data analysis efficiently.

When we talk about distributed computing, there are two main approaches:

  1. Horizontal fragmentation- It uses an approach to store the selected portions of the available instance at different sites.
  2. Vertical fragmentation- Storing of the selected attributes of the subsets of the instances comprises of vertical fragmentation.

The data involved in machine learning is very massive if a real-time problem is involved. A situation might be encountered where the machine learning model needs to be trained again and again without disrupting the ongoing parallel task. In this situation, distributed computing serves as a boon by resolving the issues.

The training in distributed computing also highlights the importance of applying these techniques in fields such as medical computing where huge amounts of data are uploaded at every instance of the given time and need to be analyzed for relevant purposes.

Distributed machine learning platforms

Training in distributed computing for machine learning also provides information about the platforms that been developed to do so. Some of these platforms are listed below:

  • H2O- Developed by H2O.ai, H2O is an open-source platform for distributed computing in machine learning with in-memory support. It also provides support for traditional machine learning algorithms and includes AutoML functionalities.
  • TensorFlow- Distributed TensorFlow provides different servers each of which is considered to be a cluster and each process is made to run on an executive search engine.
  • DMTK- It stands for distributed ML toolkit and is developed by Microsoft to provide highly efficient techniques for performing a machine learning task.

Apart from the frameworks mentioned above, there are other frameworks such as Apache Spark Mlib and Apache Mount that assists in the machine learning applications as well.

Conclusion

Most of the problems that we encounter today are voluminous and very hard to process for machine learning tasks. Distributed computing left its footprints in the field of machine learning by solving one of the major issues that are big data handling. It has gained a lot of popularity in recent years because of its high degree of scalability, efficiency, and performance. It has not only helped in performing large-scale computations but has also helped in the optimization of the operating systems. To be accurate, it has revolutionized the world of machine learning training and computations.

What are the Top AI and Machine Learning Courses in India?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Artificial intelligence is one of the most talked-about technologies in recent years. It’s also an area expected to increase over the next decade, with AI and machine learning predicted to be worth $153 billion by 2025. This blog post covers what AIML is, the career scope, and the best courses to opt for a lined career!

Artificial Intelligence: Defined

Artificial intelligence is the general field for all types of AI and machine learning algorithms. In contrast, AI and machine learning courses refer to specific types of software or algorithm that can do certain tasks better than humans.

Artificial intelligence has many applications across different industries- from customer service chatbots on your favorite websites to autonomous vehicles driving you around town. It has been used in various industries such as healthcare, finance, engineering, IT services, and more – it’s even being implemented in education! There are also many career paths you can take within artificial intelligence.

Reasons why organizations are relying on Artificial Intelligence?

Artificial intelligence is a growing field in the industry, and it has been around for more than half a century. Still, its capabilities have improved very recently with deep learning algorithms. A lot of companies are getting attracted to AI because of its ability to make sense of all kinds of structured or unstructured data by using machine learning algorithms

  • From identifying cyber threats early on to preventing fraudulent financial transactions, Artificial Intelligence is being used everywhere with a vast amount of data.
  • Machine Learning helps organizations process large amounts of complex information- whether that’s online search queries or medical records – by teaching computers how humans think/work by providing examples instead of coding rules into them. It automates tasks based on experience and data.
  • Artificial Intelligence plays a vital role in the financial sector as it speeds up trading, helps to improve customer service, and provides more accurate predictions on which stocks will increase/decrease value, etc.
  • From improving communication with customers by providing instant responses to solving problems with the help of predictive analysis Artificial Intelligence has found its place everywhere, from manufacturing units to hospitals.

What is the scope of making a career in Artificial Intelligence?

Employers are looking for people who have gained experience in Artificial Intelligence through internships or projects they’ve been working on during their time at college because there aren’t many courses available that provide hands-on knowledge about AI right now.

If you decide to pursue a career in Artificial Intelligence, you can get started by following courses in machine learning and data science.

Alternatively, you could build up your skillset by opting to gain certifications provided by educational institutions with high-quality material and expert guidance.

Learn and Grow with Imarticus Learning:

AI and machine learning courseThe Artificial Intelligence and Machine Learning program certification has been designed to provide the best learning outcome to aspiring AI and Machine Learning learners.

This 9-months extensive program helps students prepare for Data Analyst, Data Scientist, Machine Learning Engineer & AI Engineer roles.

This Machine learning certification program bolsters foundational skills in AIML to gain a deep understanding of the subject. This course goes a long way towards helping unlock lucrative career opportunities in the coveted fields of Artificial Intelligence and Machine Learning.

Course USPs:

  • Master skills of AIML through the most relevant curriculum designed by industry leaders.
  • Get an exciting opportunity to participate in a unique 3-day Campus Immersion module & interact with peers.
  • Learn what New Age AI/ML Engineers do by solving the problems they face on the job.
  • Get the opportunity to work on multiple AI & ML projects & create your own GitHub project portfolio to impress potential future employers.

10 Essential Leadership Qualities For The Age Of Artificial Intelligence

Posted on by Imarticus Learning
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Artificial intelligence (AI) is slowly being a revolution that can completely change the workforce. At the same time, it is still not able to replace human intelligence and reliability.

This is the main reason why leadership qualities are highly significant under the circumstances.
When AI is starting to show its power, it takes a highly capable leader to show the team that there is still a lot the humans can do.

In order to show them the same, a ladder needs to have certain attributes at this age of AI. These are qualities that are not taught during an Artificial intelligence course but are the ones that you need to develop yourself.
The essential leadership qualities

  1. Agility: In this fast world, a leader needs to have a quicker mind and make strategies on the go. This is one area where there are no compromises. If you have to survive in this era you have to be an agile leader.
  2. Adaptability: Sharpen adaptability skills because the requirements and circumstances could change anytime, A leader must be willing to make changes swiftly but effectively to adapt to the situations. Better the adaptability, finer would be the outcome.
  3. Accountability: Be accountable for all or any actions and decisions made as a team. Since leading from the front requires trust, this attribute helps develop confidence within the team. So be accountable and transparent.
  4. Commitment: Artificial intelligence may be able to show the way but the decision-making power is still with the humans- leaders. A leader must be committed to the decisions made and for any changes thereafter.
  5. Better communication: A leader needs better communications skills, period. Developing this attribute is more important than enrolling in any Artificial intelligence course. Look for courses that help develop this personal quality.
  6. High work ethics: Learn to value others in the team and give as much importance to every part of the work system. One who can inspire others and aspire to be a better person is better valued by the companies.
  7. Foresight: AI may be able to foresee future possible changes but it is the leader who needs to have the foresight to see and decide for the possible changes that could be down the lane. It also calls for some amount of creativity to use such changes for the betterment of the company.
  8. Flexibility with demands: When Artificial intelligence is predicting changes even a small change of course can have major impacts. A true leader must be flexible with such changes according to the demands. A leader must be able to alter his or her working style to suit the new scenario and should also be able to make it productive.
  9. Be able to influence: The flexibility in work and coming up as the winner at the end of such a trial should be enough to influence others to follow. This is one leadership quality that is highly dependent on the other attributes. One must be reliable, adaptable, and trustworthy enough to influence others. When you influence others to be positive, you are giving more value to yourself and to the company.
  10. Stay Humane: AI might be taking over too much of human efforts but the one thing that it cannot take away is the humane nature. A ladder who stays humane under all circumstances is sure to be born as a commander. This is another attribute that no Artificial intelligence course could teach you. You stay grounded even when you are flying high; it’ll make you the person that defines leadership qualities in this very age of robotics and manmade intelligence.

Also Read: 10 Interesting Facts About Artificial Intelligence

How Edge IoT Solutions Can Improve Business Performance?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Artificial Intelligence (AI) and Internet of Things (IoT) technologies are transforming the ways of business entirely. The power of these technologies is revolutionizing the business sphere by evolving its chief resources such as software, products, applications, and overall systems.

AI and IoT are becoming widespread as businesses are looking for ways to hyper-optimize their performance by propelling the value-addition processes and generating better results from data through connected devices.

AI and IoT help companies to find patterns not easily identified by humans. It enhances operational capacity and boosts the production of products and services. By implementing AI and IoT businesses mitigate their risks and improve their efficiency.

artificial intelligence courses in IndiaThat is why companies today are investing heavily in the digitalization of their businesses.

They are employing new technologies and experts with an artificial intelligence and machine learning career to help them achieve optimal performance levels.

AI at the Edge

Artificial Intelligence at the edge is the intelligence that is influencing the retail and semi-autonomous car businesses at large. These technologies are diminishing the data communication costs and allowing instantaneous business operations which comprise decision-making, information creation, and action-taking. The real-time processing in AI at the Edge is vital for many operations in robots and semi-autonomous vehicles, etc.

A high amount of computational power is required by many Edge AI technologies. When the latency and instantaneous response process is low Edge AI becomes crucial for businesses. Through these Edge AI technologies, businesses get to mitigate their risks, detect problems, and find solutions to resolve issues.

Attaining Business Values

The AI at the Edge solutions helps businesses improve their functional capability and, hence, increase competence. These technologies break down important data streams for businesses and help them understand relevant parameters for achieving desired outcomes. The AI-based IoT solutions help businesses streamline their production-planning process by reducing the expected downtime and identifying and diminish the factors of poor performance.

IoT solutions also help in reducing maintenance visits by describing the accurate and real-time status of machine health. These technologies create large amounts of data. But storing and analyzing these data when needed is a whole different task. For this, companies need Edge AI. Edge AI helps businesses find their point of error and failure in the process.

The Edge in Edge AI technology is used relatively. It helps differentiate the local from the central. However, there is a need to differentiate various types of things that lie at the edge. Employing Edge AI technologies smartly by mapping out clear processes and action plans to achieve measurable results can help businesses deliver better outcomes.

For achieving their business values through AI at the Edge technologies, companies are looking for experts with comprehensive knowledge and skills in this domain to help them implement it in their business models. Considering the rising demands of expertise in this field, Artificial intelligence and machine learning courses are being developed for prospects looking to make a career in this field. Edge AI is entering into every industry slowly and with its enormous potential, it is likely to prove quite beneficial.

Artificial Intelligence is Modernizing Restaurant Industry

Posted on by Imarticus Learning
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John McCarthy coined the term ‘Artificial Intelligence’ more than 6 decades ago. We have been dreaming of a technology-enabled future for decades and it is turning into reality now. Artificial Intelligence is the programming of machines to imitate human intelligence and their actions. It helps in minimizing human efforts along with improving speed, accuracy, and effectiveness.

Today, there are huge numbers of data created in almost every sector. There is a need for Advanced Algorithms, huge data management, storage, analysis, and several other complex tasks. Artificial Intelligence and machine learning training is gaining popularity due to this increasing need for accuracy, precision, and effectiveness.

It’s interesting how Artificial Intelligence has grown over a small period in several sectors. Education, Transportation, Sports, Media, Marketing, Banking and Finance, and so on goes the list of industries that use Artificial Intelligence.

How Artificial Intelligence Helps in the Restaurant industry

Below are a few examples of the application of Artificial Intelligence in the Restaurant Industry.

Forecast Sales. People indeed make orders based on a specific climate. You would not go for a hot cup of cocoa on a hot humid day. You would go for something that is cooler. The climate makes an impact on the food and beverages sold by an eatery. AI with Machine Learning helps the restaurant to project their sales based on the orders made, current weather conditions, and past sales performance.

Scheduling Capabilities. Predictive Scheduling is fundamental these days. Staffs need to be informed in advance of their schedules, whom to be switched with, shifts, etc. Doing this manually can get quite tiresome for the manager. Artificial Intelligence provides help with this kind of function. It can perform a lot of scheduling tasks in an efficient time that would normally take up a lot of human effort and time.

Smart Virtual Assistant. Virtual Assistants and related technologies have been gradually growing over the past few years. It gathers various information related to the restaurant and presents it to the user when they search about it. Based on the information customers pick a suitable restaurant. Therefore they play a direct role in the growth and sales of the restaurant.

Robots. Is it possible? Yes, definitely. It won’t be a while before we may actually be delivered food from robots. A restaurant chain in Pasadena named Caliburger was one of the firsts to bring in a robot to their kitchen. Though it is yet to come into effect in India, it could take the restaurant sector to a whole new level of functioning.

AI-Driven Kiosks reduce waiting time and improve the eatery experience for customers. These systems act as POS systems and make the ordering process simpler for the customers.

Inventory and Purchasing. Pos systems integrated with Inventory and Purchasing can improve efficiency and helps in reducing losses. It helps to track and order based on sales, menus, and recipes. One can cross-verify between the actual food preparation and the portion of food left, thereby helping to find out sources of waste and theft in your kitchen. AI-powered restaurant management solutions ensure the right supply of materials and help to remove one more tedious work from your list.

Mapping And Management Tools. Delivery is gaining popularity these days. It is important to optimize the delivery routes for quick, efficient, and quality food delivery. Management Tools helps to keep a check on the drivers’ progress, guide on routes and on how to save miles and time, and keeping the customers in the journey throughout.

Self Serve Options powered by Artificial Intelligence could work wonders in the Restaurant Sector. Though it has not come into effect in India, self-serve kiosks could speed up the services without actually sacrificing the human touch. Customers can browse through menus, make orders, and make payments online without having to stand in long queues. This helps in improving customer satisfaction.

Artificial Intelligence is changing our lives in more ways than we know in this 21st century. So is Artificial Intelligence helping to reshape the Restaurant Industry. The AI-powered tech advancements these days are critical in boosting sales and increasing customer satisfaction to a much higher level. Since most individuals have already embraced digital technologies, it would only make it smarter and easier to inculcate Artificial Intelligence in the Restaurant Industry.

How Is Ethical AI Different From Fair AI?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Artificial Intelligence is turning out to be an integral part of every part of businesses and human lives at a surprisingly faster pace. As much as it is transforming lives and processing easier, it comes with its share of drawbacks.

The topic here is the ethical and fair side of AI that has already proven to be a problem even for the big giants like Amazon and Apple. So the question here is how to rectify the problem without compromising the usefulness of AI.

In order to solve the issue, it is important to understand the difference between ethical AI and fair AI. Although they are not much different from each other, knowing the thin thread that separates them is crucial.

The dilemma of ethical AI

While AI  algorithms help replace several tasks which usually the human mind accomplishes, there are several pressing issues regarding the ethical outcomes it causes. Both  AI and Machine learning requires data that is fed manually, there is still no definite criteria on what qualifies as non-biased that leads to an ethical result.

If the algorithm leads to an unwanted advantage for anyone while someone else ends up on the receiving end it is considered unethical. The problem is defining what are ethical terms and what constitutes unethical. To solve this issue, leading brands have slowed down their progress with regard to the AI development in their business end.

An ethical AI will be the one that can accomplish ethical prediction and quality outcomes without impacting humans in one way or the other.

When is it a fair AI?

When it comes to fairness in AI predictions and outcomes, it is all about perceptions. A fair decision to one might appear unfair to another. So it is not easy to judge the AI program as unfair or fair.

While there are several issues with the unfair outcome of AI programs, what it really needs is a smarter and sensible approach.

  • It needs cooperation between humans and the AI system to evaluate and judge fairness. The employees need to be more intuitive and apply more common sense to do the job well.
  • As humane as AI seems to be it needs a bit more humanity than already has. Here, it is the company’s choices and values that can be used to determine the fairness in the AI system under development or use.
  • While creating the algorithms even the smallest factor and the most unimportant aspect should also be considered to create a more effective filter. This approach prevents a wider range of unethical and unfair decisions by the AI system.

The future of ethical AI

Owing to the past mistakes in the field of AI, many of the Artificial Intelligence training courses are trying to include a moral side in their curriculum. Since most of the unfair decisions by the machine is due to the social and economical perception by the humans, it needs correction right from the root level.

difference between ethical AI and fair AIIn order to pursue an Artificial Intelligence career, one must start thinking ethically and be able to decide between what is fair and unfair for the right matters. This is where critical thinking comes in handy.

Though it is difficult to create a Fair AI, it is easier with Ethical AI as long as it does not cause a clear bias with many factors in consideration.

Conclusion

What it shows is that machines can only impersonate humans and they are yet to reach that feat emotionally and for logical thinking capability. For them to reach that point, humans must change their views, perceptions, and work ethics.

Take That Next Step Towards a Rewarding Data Science & Analytics Career With These Analytics Courses

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Reading Time: 2 minutes

Take That Next Step Towards a Rewarding Data Science & Analytics Career With These Analytics Courses

Are you interested in completing a data science course in India but don’t know where to start? Then you’ve come to the right place as we’ll discuss the top data science courses in India and learn how they help you start a career in this fast-growing industry.

All of the courses below have reasonable data science course fees and you can choose according to your requirements and aspirations.

Data Science and Analytics CareerWe’ll discuss the data science course details of our programs in the following points:

Post Graduate Program in Analytics and Artificial Intelligence

Our post-graduate program in analytics and artificial intelligence is among the most popular data science courses in India. We offer this program with UCLA Extension. It is a data science course with placement assurance which means you will get access to our dedicated placement support to our private placement portal and additional services.

The program gives you a dual certification from UCLA Extension and Imarticus Learning. UCLA Extension is one of the oldest and largest higher education providers in the United States. Some of the key concepts you’ll study in this online data science course in India are Machine Learning Algorithms, Deep Learning, Computer Vision, and many more.

Machine Learning and Deep Learning Prodegree

Machine learning refers to the field of developing computer solutions that can perform tasks and learn from them without requiring human intervention. Our Machine Learning and Deep Learning Prodegree will help you learn the required skills to enter this field as a skilled professional.

We offer this program with IBM. The course teaches you machine learning, Python, IBM Watson, and deep learning through 16 in-class and industry projects with a Capstone project as well.

Post Graduate Program in Data Analytics

Our Post Graduate Program in Data Analytics teaches you data science from scratch. It is among the best data science courses for beginners as it covers all the required concepts.

You will learn the foundations of data science and its in-demand tools including Python, R, PowerBI, Tableau, Hadoop, SQL, and Spark. Like our other programs, it is a data science course with placement support to help you start your career right away.

Data Science Prodegree

We offer our data science prodegree with KPMG. The program is industry-aligned and teaches you the most in-demand skills in the industry. You will work on real business case studies and receive project mentorship directly from industry experts.

This online data science course in India teaches you SQL, programming, Tableau, statistics, R, Python, and many other important concepts. You will also work on a KPPG in India Capstone Project by the end of this data science course in India.

Conclusion

Starting a career in data science and analytics is quite simple. All you need is a little effort, commitment, and guidance and the rest is easy.

Now that you’re aware of our data science course details, you can start your learning journey right away. You can find out more information on our data science course fees and eligibility criteria on our website.