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Supervised Learning: The Stepping Stone Of Artificial Intelligence

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Supervised Learning: The Stepping Stone Of Artificial Intelligence

Supervised learning is a form of machine learning that uses labeled examples to find patterns in unlabeled data. This is essential in developing artificial intelligence because it allows computer systems to learn from experience and also make predictions about unseen situations. 

What Is Supervised Learning?

It is a machine learning technique that uses labeled data to train an algorithm. Supervised learning predicts a target variable’s value using a set of predictor variables. 

For example, if we have a set of data that contains observations on people’s height and weight, we can use this information as input for our model so it can predict how tall or heavy each person will be at some point in time (e.g., twenty years from now). 

The main idea behind supervised learning is that we have some training examples (data points) along with labels indicating whether those observations belong together (e.g., Is this person tall?). These labels can come from another source besides humans; they could be numbers representing probabilities instead!

The Algorithms That Make Supervised Learning Possible

Supervised learning is a type of machine learning that uses labeled training data to learn.

In supervised learning, we have two algorithms: regression and classification. Regression models continuous variables, while classification models categorical variables. The algorithm will be able to make predictions about new unseen data based on the known training set and its knowledge about how specific patterns are related to each other. 

The Applications of Supervised Learning

Supervised learning uses labeled examples to train an algorithm. Supervised learning aims to create a predictive model where the labels are known and can use as inputs for the model.

Supervised learning is a potent tool. It makes predictions about future events, classifies data, and finds patterns in data. In particular, you can use supervised learning to detect correlations between variables and make predictions based on these correlations. 

For instance, if you want to predict whether a company will bankrupt based on its financial status, supervised learning could help you analyze historical financial records of previous companies that went bankrupt and make projections about what might happen next for your business model.

Learn Machine Learning and Artificial Intelligence Course with Imarticus Learning.

best data analytics certification course

Learn Supervised Learning by enrolling in the E & ICT Academy’s deep learning Artificial Intelligence certificate program. Learners will benefit from this IIT AI ML Course as they prepare for careers as data analysts, data scientists, machine learning engineers, and AI engineers.

 Course Benefits For Learners:

  • To gain real-world business experience and prepare for a fulfilling career in data science, students work on 25 real-world projects. 
  • Students can impress employers and showcase their skills with a certificate approved by the E & ICT Academy, IIT Guwahati, and an Imarticus Learning-endorsed credential. 
  • After completing this machine learning certification, students can find lucrative employment in the artificial intelligence and machine learning industries.

Contact us through the chat support system, or visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

5 things to know before opting for an AI certification course

Posted on by Imarticus Learning

Technological advancements are taking the world by storm. Scientific innovations are changing lives drastically. This is the age of Artificial Intelligence (AI) and Machine Learning (ML). Days are not far when we will have a replacement of human force with robots that have features just like humans.

In fact, many industries are already implementing such changes in their work. Due to the increased demands for such technology, artificial intelligence and machine learning courses are being offered by many institutes all over the world. 

However, anyone and everyone cannot take up an AI certification course. There are certain subjects and concepts which you have to know to be able to take up an AI course. In this article, we will discuss that in detail.

5 things about which you must know before choosing an AI certification course

Before choosing to enroll in an artificial intelligence course, it is important that you have clear concepts about certain subjects. They are as follows:

Thorough knowledge of mathematics – While studying artificial intelligence, you will need to have a thorough knowledge and a deep understanding of mathematics. This conceptual understanding helps in writing algorithms and programs for AI. It is important to have a basic understanding of mathematical concepts like calculus, linear algebra and probability. 

In machine learning, linear algebra is a compulsory subject to know. The dynamics between ML and linear algebra are explicable via certain abstract concepts like matrix operations and vector spaces. 

For building a machine learning model, calculus is inevitable. Along with having a basic knowledge of differentiation and integration, you should also know gradient or slope, partial derivatives and chain rules. 

Good knowledge of programming language – If you are aiming to become proficient in artificial intelligence, then it is important that you have fair ideas and knowledge of various programming languages like Java, Python and C++. This is because algorithms of machine learning are put into effect with the help of codes only. 

As per tech experts, the best language to learn for artificial intelligence is Python. With the help of this language, you will be able to create extremely complex algorithms quite easily. The programming language has a concise and easily readable syntax. It is almost the same as writing commands in the English language. There are different libraries in Python and they are particularly useful for machine learning and artificial intelligence.

Knowledge of algorithms and data structures – When it comes to ML and AI, having a basic understanding of data structure and algorithms is mandatory. 

Learning data structures is essential for learning data collection and for performing various kinds of operations on collected data. 

On the other hand, algorithms are a set of step-by-step instructions, which are written in that order for accomplishing and completing a specific and predefined work. Algorithms are expressed in the form of flow charts or pseudocode. 

Slight concept of machine learning – Machine learning is actually a subset of artificial intelligence. In machine learning certification, you mainly study and learn about computer algorithms. In the ML algorithm, you create a mathematical model, which is based on some kind of sample data to make decisions without being programmed explicitly. 

Statistics – Statistics mainly deals with data – right from collection to analysis, from sorting to interpretation, and finally presentation. Therefore, its importance in machine learning is quite obvious and evident. A candidate studying AI and ML should be familiar with outliers, mean, standard deviation, median, and histogram. 

Conclusion

Lucrative career opportunities are waiting in the artificial intelligence and machine learning industry. Enroll in the best course on the subjects from a reputed institute and obtain a globally-accredited certification to fly high in your career. Check out the official website of Imarticus Learning for more details. 

A Complete Guide On How To Approach A Machine Learning Problem For Beginners!

Posted on by Imarticus Learning

As beginners in machine learning, you will want to have questions answered to common problems. Questions like how to approach, how to start, which algorithm fits best, and so on.

Common problems in machine learning for beginners

Here, we will help you resolve those problems by answering common questions:

Where can you use machine learning?

You can use machine learning for problems when:

  • Automation is involved
  • Learning from data is needed
  • An estimated outcome is required
  • Need to understand pattern like user sentiments and developing recommendation systems
  • Object required to identify or detect an entity

How to solve machine learning problems?

Here are steps to solve problems in machine learning:

  • Read data from JSON and CSV
  • Identify dependent and independent variables
  • Find out if there are missing values in the data or if it is categorical
  • Apply pre-processing data methods if there are missing data to bring it in a go to go format
  • Split data in groups for testing and training for concerned purposes
  • Spilt data and fit into a suitable model and move on validating the model
  • Change parameters in the model if needed and keep up the testing
  • An optional step is to switch algorithms to get different answers to the same problem and weigh the accuracies for a better understanding – this explains the accuracy paradox
  • Visualize the results to understand where the data is headed and to explain better while representing it

What algorithm should you use?

You need to understand what labelling is to answer this. Labels are the values we need to make an estimate. This represents the Y variable, also known as the dependent variable.

Here is a small example to help you understand this:

if

dependent_variable_exists==True:

supervised learning()

else:

unsupervised learning()

Machine Learning CourseWhile you’re learning from a machine learning course, you will understand that your supervision and training refers to supervised learning. This means that the results need to be compared by a frame. The frame here is the dependent variable. However, there is no reference for frame under unsupervised learning, which is why the name.

It is time to figure out how algorithms are served. However, it is essential to note that this is a generalized approach. The situations can differ, and so will be the usage of algorithms:

  • Numeric data for linear regression
  • Logistic regression when the variable is binary
  • Multiple category classification through a linear discriminant approach
  • Decision Tree, Naive Bayes, KNN, and Ensembles for regression and classification

Machine Learning Course

As you grow in your machine learning career, you will learn how to take random XG boost, forest, Adaboost, among other algorithms for ensembles. You can try these for both regression and classification.

Ensembles, as the name goes, refer to a group of at least two classifiers or regressors. Moreover, it doesn’t matter if it is the same or if working towards the same goals.

Building visualizations

Here are some of the things to remember when visualizing reports:

  • You can show class clustering with a scatter plot
  • Avoid scatter plot if there are several data points
  • Class comparisons can be explained through histogram
  • Creating pie charts help comparative breakdown
  • Line charts can help analyze reports with frequent deviations like stocks

If a scatter plot has too many data points, it will look clumsy. It will not be a presentable representation to show stakeholders. In such cases, you should use scatter charts.

Final thoughts

These points will help a beginner in machine learning career to become more aware of how to solve problems. You now know the essential things to do and things to avoid to get accurate results.

How long term modeling of our future energy system can be mapped with artificial intelligence and machine learning

Posted on by Imarticus Learning

Today, technology and sustainability are the main axes of development. To secure the planet and continue the growth of industry, we are engaged in a global energy transition. Most countries have become aware that measures must be taken to address a problem that, if not curbed, will have catastrophic consequences for the environment and, of course, for human beings themselves.

However, such a transformation requires the support of technology and, because of the enormous amount of data, artificial intelligence and machine learning courses are the basis to ensure the advancement of the energy sector. At Imarticus you can join the postgraduate program in data analytics & machine learning (AIML).

Technology as a tool

Changing the energy paradigm of the last century will be an arduous and complicated task. That is why new technologies have a lot to say as tools to facilitate evolution. The Internet of Things, machine learning, artificial intelligence, and Big Data will be key to making the processes of change as effective as possible. Massive data analysis must become a fundamental pillar for transforming how energy is generated, transmitted, and distributed.

Artificial Intelligence allows us to handle enormous quantities and analyze them logically and reasonably. About energy, in particular, we have data on meteorology, health, or the behavior of the people involved in the system: who generates electricity, who transports and distributes it, and who consumes it.

Data that, when properly analyzed, can provide a tailor-made understanding of the sector. The development and implementation of intelligent systems must not only facilitate the massive introduction of alternative energy sources but will also have the task of achieving rationalized storage of this energy, as well as providing greater flexibility for the demand, i.e. the people who use it.

Three levels of analytics can be applied: descriptive, to know what information is available and where to apply intelligence, predictive analytics, to anticipate production or demand, and prescriptive analytics. With the data, we work on predicting production, including renewable energies and demand, with the implementation of smart meters. In addition, technical and non-technical incidents, such as energy fraud, are detected. All of this is aimed at optimising the energy model, with the resulting economic and environmental benefits. We will see a huge take-off in the number of professionals who will choose to pursue a machine learning career.

Tools for the consumer

In this scenario, smart meters and internet-enabled sensors will be commonplace, which will improve our energy use while at the same time making it possible to bring costs in line with what each individual actually consumes.

Thus, machine learning will automate processes, while artificial intelligence will make it possible for devices to work automatically and learn from consumers’ habits. This will also be possible on a large scale, so that the operation of future solar or wind power plants, to give just two examples, will be more effective in a shorter space of time.

In this respect, we should note that although everyone is involved in the energy transition and awareness must start in every household, the technology will be geared towards people having little to do in terms of reducing consumption and costs.

Artificial intelligence-based models and predictions facilitate and will continue to be a major advantage in mapping energy systems. What is most surprising is that this is just one of the many applications of these technologies. If you want to contribute to the change, you can sign up for AI and ML courses by E&ICT Academy, IIT Guwahati.

How digitization through artificial intelligence and machine learning technologies has gained momentum post COVID-19?

Posted on by Imarticus Learning

In just a few months, the COVID-19 pandemic has managed to do what normal times would have taken years to achieve – a paradigm shift in the way companies in every industry and sector do business. Artificial intelligence and machine learning have been at the forefront during these challenging times. 

As the world gradually finds its way back to usual ways of life, it is interesting to see how the global crisis has paved the way for behavioral shifts, learning, and innovation. 

AI and ML in the Post-Covid-19 World

With the acceleration of digitization through Artificial Intelligence (AI) and Machine Learning (ML), digital sales have seen a boost, and businesses have focused their tech investments on cloud-based products and services. From online grocery stores and EdTech sites to online pharmacies and OTT players, the post-COVID-19 world looks very different through the AI and ML lens.

So, here are some examples to show how AI and ML technologies have gained momentum post-COVID-19:

  • AI and ML have been impacting the healthcare industry since long before the pandemic hit. AI algorithms have and continue to help in quickly sifting through large datasets to help identify similar diseases and their possible cures to accelerate the COVID-19 research work. 
  • AI and automation technology have also eased the healthcare sector’s administrative load by automating various processes. For example, data processing algorithms to extract data from internal systems and automatically generate medical reports and necessary audit trails have gained momentum post-pandemic. 
  • Also, advancements in ML will continue to help create new revenue streams. For example, scientists, drug researchers, and pharma companies are increasingly turning to AI and ML data processing algorithms to facilitate vaccine and drug discovery and their possible impacts on people. 
  • Lockdowns and social distancing norms have boosted online markets and the digital economy. However, even when the pandemic is gradually ebbing, customers are expected to continue using doorstep services as they did during the peak crisis. Hence, technologies like Augmented Reality (AR) and Virtual Reality (VR) have increased among eCommerce platforms to deliver a better customer experience. 
  • Talking about customer experience, the online retail industry has ramped up its use of AI chatbots and smart assistants to attend to the ever-increasing numbers of digital customers. Hence, the use of AI has helped streamline digital services, online ordering, and delivery systems. 
  • The pandemic has given rise to a digital workforce. To this end, the use of AI to quickly process applications, scan for eligibility and qualifications and perform other mandatory hiring checks has become the norm and is only expected to increase in the near future. 
  • The financial sector has also seen a dramatic rise in the use of AI and automation to serve its customers better and quicker during challenging times. For instance, banks leverage AI to help customers safely upload documents, categorize them and expedite processes without any delay. 
  • Lastly, greater digitization has also increased the risks of cybersecurity threats during the pandemic. While conventional cybersecurity risk management systems have failed to keep up with evolving cyber threats, AI offers innovative defenses. The pandemic has only nudged organizations to adopt holistic approaches to cybersecurity through AI and ML and create an integrated security system. 

How to Find the Best Artificial Intelligence Course?

If you want to learn AI and get a certification in AI and ML, opting for an online course can be the best call. But before you sign up for the course, ensure that it offers hands-on experience with real-world projects and has a curriculum with extensive coverage of concepts related to machine learning, NLP, deep learning, data science, and computer vision. 

The Popular Use Cases of Artificial Intelligence in BFSI

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AI has revolutionized every industry and has changed the way businesses function. Everyone is finding ways to adopt AI into their work. Similarly, the banking, financial services, and insurance sector is also looking to integrate AI into their business and one major reason for doing that is the security of customers.

Customers expect banks to deliver flawless experiences and improve methods of interaction. In the last few years, banks have faced a rise in security threats which is why the banks had to come up with new ways of tightening the security because they were starting to lose clients.

A global study indicates that 85% of respondents have already implemented AI within their organizations and expect to use AI in the new use cases that come and 77% of respondents are anticipating the use of AI processes in their business in the next 2 years.

How is AI strengthening the competitiveness of banks?

Artificial Intelligence and machine learning is the future of banking as it has the power to combat fraudulent activities and improve customer service. Here are some examples of what changes AI has brought about in the banking industry –

  1. Mobile banking – Mobile apps are becoming more advanced and personalized. Banks can generate more revenue with mobile banking services than they generate from customers visiting the branches. This has also saved a lot of time for consumers and improved the quality of services provided.
  2. AI chatbots – In banking, chatbots are used to create an interactive experience for the customers. Bots can communicate with the customers and solve their queries while staying within reason and following the rules. Chatbots can also work 24 hours a day without breaks, which increases the productivity of the banks.
  3. Enhanced security – With the unlimited amount of personal data that is digitized and the number of digital frauds that are happening, it is customary for the banks to keep the client’s money and confidential information safe, and AI helps with that. They have come up with cybersecurity measures like fingerprints, iris, and voice recognition. These measures are almost impossible to forge, therefore everything is safe.
  4. Conformity – It is important to regulate data constantly. There are different processes like “Know Your Customer (KYC)” or “Anti Money Laundering (AML)” that can gather data and transactions digitally, which makes the work faster and easier. If done manually, this process can take up a lot of time. AI algorithms can integrate data very accurately and efficiently.
  5. Financial evaluation – Banks usually earn their profits through the interest they receive on loans. To keep a check that the loans are given back on time with proper interest, they have machine learning algorithms that analyze millions of data and assess whether the client is a risk or not, and then come up with a decision.

Conclusion

People are looking for exceptional services at the click of a button, they don’t want to wait for days for a transaction or money transfer. AI is not only the future of banking, it is also the present. It is advised that all financial institutions start investing in AI right now so that they can optimize their services. With the help of AI certification that is offered by AI and machine learning courses, people can make this revolutionary change in their businesses.

Autonomous Cyber AI A New Defense System in Cybersecurity!

Posted on by Imarticus Learning

Artificial Intelligence (AI) is being used nowadays to enhance cybersecurity. Security tools embedded with AI analyze data from various cyber incidents/threats and use them to identify potential threats. Anomaly detection can also be automated with the use of AI in cybersecurity. Threat actors are conducting data breaches in firms with new tools and ways. There are numerous types of attacks evolving every day.

To tackle the evolving data breaches & to enhance cybersecurity, firms require a fully automated security system embedded with AI. Autonomous cyber AI is predicted to be a revolutionary asset with a lot of firms adopting it quickly. Let us see more details about autonomous cyber AI.

 

Autonomous Cyber AI

 

Autonomous cyber AI is a defense system that can handle the complexity & variety of cyber-attacks. It has automated security protocols and is activated at the time of any threat. It is believed that threat actors are using AI-driven attacks where the AI algorithm can manipulate any machine’s decision. To counter possible AI attacks & various other types of complex attacks, firms require secure algorithms and automated defense systems.

 

The data generated by firms is also huge and to manage this big data, we need AI to reduce human labor and increase accuracy. Cybersecurity experts also use other technologies with AI like machine learning, deep learning, etc. to create an autonomous cyber AI. Autonomous cyber AI is capable of identifying data outliers or anomalies which are hazardous for business data. Autonomous cyber AI immediately identifies any foreign element in the business data and takes measures to protect the system/data.

 

Humans cannot identify new attacks in time, which leads to data theft/breaches. It is expected that we can see machines fighting each other in the future because of the rise of AI-driven attacks. More than 3000 organizations/firms around the globe have already adopted autonomous cyber AI to tackle cyber-attacks. One can know more about AI by opting for an Artificial Intelligence Course from a trusted source like Imarticus Learning.

 

Benefits of AI Cybersecurity

 

The benefits of AI cybersecurity to firms/businesses are:

 

• Management of big data can be easily done with less human labor. Large volumes of data can be processed in less time.

 

• New security attacks can be identified by AI.

 

• Unknown/possible security threats can be known and fixed in time.

 

• 24*7 autonomous protection without any human intervention.

 

• It will help in cost optimization as it is a long-lasting solution for cybersecurity.

• Authentication system can be strengthened via AI where only a limited number of people are given access to security details.

 

• The response time after an attack is decreased as autonomous cyber AI acts quickly.

 

Conclusion

 

Cybersecurity is very necessary for firms to protect their data and digital ecosystem. AI is being used to develop smart algorithms that can control the movement of data. One should learn about autonomous cyber AI if he/she is looking to build a successful Artificial Intelligence Career as many companies are adopting it in recent times. Start your AI course now!

Artificial Intelligence skilling has to start from a young age! How? Explore…

Posted on by Imarticus Learning

The chasm between machines and living things is shrinking. Artificial intelligence (AI) is deeply rooted in all aspects of technology, from robots to social networks. India has the potential to skyrocket in the domain of Artificial Intelligence and surpass USA and China, largely owing to:

  • It’s deep-rooted IT &ITeS infrastructure
  • Innovation ( India ranked among the top 50 countries in the Global Innovations Index 2020)
  • Accessibility to large datasets

These have pioneered more than a handful of start-ups and private investments in this sector. For AI to flourish further, there needs to be a nationwide upskilling of the younger generation in Artificial Intelligence Training. The GenZ needs to be acquainted with the theoretical and practical aspects of AI application to increase its scope of innovation and entrepreneurship.

Artificial Intelligence CareerIn the future, the interaction between humans and AI will define in a lot of ways the structure and functioning of a modern-tech society.

Thus it becomes imperative to lay down the basis of friendship for the years to come by exposing the young ones to AI.

While a lot of minds will wander to an Artificial Intelligence Career it is also important that others are no less familiar with the upsides and downsides of such a powerful technology.

Here is how we can ensure the frontiers of the same:

  • Introduce young people to the concepts of AI and machine learning through education curriculum. In India, the Central Board of Secondary Education (CBSE) announced the integration of AI in partnership with IBM for the academic year 2020-21
  • Encourage learning through hands-on projects so that student can make better, informed and critical use of these technologies
  • Enrolling young minds on various Edu-tech platforms specializing in the field of Machine Learning and AI which help them gauge interest and real-life applications of such technologies using intuitive software

Some of these websites include- Scratch, App Inventor, Cognimates etc

  • Experiments with Google is an easy-access, affordable, and user-friendly tool to explore artificial intelligence training at a young age with exciting experiments on AI, VR, AR, Chrome, Voice, Android etc to apply creativity and technological dexterity at the same place. One of these fun-filled learnings includes MixLab that uses voice commands to create music
  • Engage in the practice of cultural inquiry – like what is the goal of You tube’s recommendations or how do my Amazon purchases reflect on my Instagram feed
  • Lastly, before introducing your children to the world of AI and machine learnings, self-education of the same is very crucial

Apart from exploring the possibilities of AI, these junior minds also need to know the limitations of AI to have a balanced approached. That is to say, AI is not the ultimate machine as it is created by humans and will improve along the way by errors made and rectified by humans.

Artificial Intelligence CareerIn recent studies, a scientist is experimenting to teach AI to learn like a kid. They want to inoculate the eager learning attitude and swift skills of young people into the algorithms of machines.

And, AI does not create everything. It is the innovation and vision of responsible human beings that will introduce, implement, and maintain the technological structure in human society.

Case Studies: Training Neural Networks to Play the Legendary Snake Game!

Posted on by Imarticus Learning

Video games play a critical role in developing and evaluating futuristic AI and ML models. Thanks to their performance in a variety of tests, the gaming world has been used time and again as a playground for testing AI devices.

This isn’t a new phenomenon, but one that goes back at least 50 years. The Nimrod digital computer built by Ferranti in 1951 is widely touted as the first known example of the use of AI in gaming. Mega Man 2 was used by Japanese researchers to test AI agents and the AI system Libratus was used to beat pro players of Texas Hold ‘Em Poker to make technological and gaming history.

The Snake game is quite a familiar feature of many childhoods because of its simple objective and playing process. The player controls the snake by eating apples which are spawned at random locations to optimize the game. For every time the snake consumes an apple, the snake must begin to expand one grid. And the one rule? Don’t let the snake collide with anything.

To take things one step further, global researchers and have been applying neural networks and machine learning algorithms to this legendary game.

Machine Learning Course If you’re a student in a neural network course or a machine learning course, this is fertile ground for experiments of your own! Here are some case studies born of such experiments:

Creating the Snake Game Using Deep Reinforcement Learning

In this experiment, the researchers used a Convolutional Neural Network (CNN) that had been trained with a Q-learning variant. The aim of the experiment was to use a Deep Reinforcement Learning model in enabling a self-ruling agent to play the game with the constraints getting stricter as time passes.

A reward mechanism was also designed to train the network, make use of a training gap strategy to circumvent training during target changes and categories a variety of experiences for better training.

The final results of the experiment showed that the agent outshone the ground-level DQN model. It even surpassed human-level performances in terms of high scores and duration of survival.

Playing the Snake Game Using Genetic Algorithms and Neural Networks

Researchers at a Polish university used a framework of a neural network that essentially determined what action to take from any given data at the time. The researchers referred to the neural network as “DNA”– it functioned as the “brain” of the snake, so to speak, due to its role in influencing decisions.

The class has patterns with weights as well as other patterns with biases, reflecting each neural network layer. Next, a function is created that allows the calculation of performance. In this case, the performance included the number of moves that the snake executed without dying as well as the scores.

Neural Network TrainingThe neural networks training that were used had one inconspicuous layer with six neurons as well as a genetic algorithm to identify the best possible methods and parameters. The population of snakes was first generated and allowed to play so that researchers could identify the number of steps and the count of apples that were consumed.

Based on this, the researchers identified which snakes performed best and would be selected for breeding. The “parents” were chosen and the DNA– weights and biases– transferred to the new snake produced.

The selection stage was followed by a mutation, where every new snake ended up inheriting a neural network from its “parents”. This was repeated time and time again until the best results were achieved.

Conclusion
The video gaming world has played pivotal roles in enhancing the quality and complexity of AI and ML over the past few decades. It remains to be seen what future advances come of this surprising yet clever collaboration.

What is the best way to learn Artificial Intelligence for a beginner?

Posted on by Imarticus Learning

What is the best way to learn Artificial Intelligence for a beginner?

Over the past few years, the field of Artificial intelligence has displayed tremendous amounts of growth. AI is now driving businesses of billions of dollars across various industries and enabling enormous career opportunities.

If you have plans to learn artificial intelligence, it is the perfect time to start acting on it. This article discusses the best way to master AI for beginners.

1. Begin with the Basics

The first thing you have to do is unlearn everything about the AI. Clear all the preconceived notions and make your mind open and fresh for learning. Now you can actually start learning.

Start with the basics. Learn about the various technologies involved and their objectives. It will help you get oriented at the beginner level. You can refer books or blogs to get through this step.

2. The podcasts and Videos
The next step is listening to podcasts and videos. It will give you more comprehension about the industry, application of different technologies, the effect of them in our real life, various techniques in them and many more.

Often these videos and podcasts come with jargons and concepts involved. So, it is important to have a fair amount of familiarity with the basics.

3. Guided Courses
A dedicated artificial intelligence course is one of the most important practical ways of mastering AI. A guided course will take you fully into the world of Artificial Intelligence. You will get global exposure to the skills required. Usually, such a course will brush up on the basics you have already taken care of and then help you develop the right technical skills required to work with AI.

If you are planning to join the industry, such a course is inevitable. A guided course will also put you in touch with experts of this technology and excellent study materials. So, it is important to attend a guided course for a complete learning experience. Along with that, you will get a certification proving your excellence in AI at the end of these courses. It will help you during the search for a job.

4. Projects
The best way to learn anything is to practice it properly. So, it is essential to indulge in lots of projects and gain practical exposure. You will be doing capstone projects during your course. From those projects to the projects you are personally interested in, you have to constantly work and build your portfolio. By doing this you will be able to master this skill in a very short time.

For a beginner with very low prior experience with AI, these are the little steps that make sense. Also, through this, you will be able to find some time to process the transition between each step and prepare for the next one. Within just a year, this road map will equip you with AI capabilities that are good enough to be a part of the industry. So, start your process as soon as possible and take part in the AI revolution going all around the world.