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Here’s Why Upskilling With A Machine Learning Course Is Imperative In 2022

Posted on by Imarticus Learning
Reading Time: 3 minutes

Machine learning has incorporated itself into your everyday lives to a great extent. This futuristic technology is empowering the world a little more with each passing day. Be it product recommendations at window shopping, fraud detection in the financial institutions, or content used by various social media platforms like Instagram, Facebook, and LinkedIn, everything uses machine learning algorithms. Simply put, machine learning is the future and it plays a very important role in our lives. And this is what makes machine learning so important. 

It doesn’t matter in which field you’re in, you can take your career to the next level by taking a machine learning course. In this blog, we will discuss why you need to upskill with a machine learning course in 2022!

Machine Learning

Why Machine Learning Course in 2022? 

Machine learning has emerged as the most sought-after skill to have because of the increasing demand and the numerous benefits that it offers. Below are some reasons why a machine learning course is imperative in 2022:

1. Better Growth and Career Opportunities 

A TMR report suggests that Machine Learning as a Service (MLaaS) is expected to rise from just $1.07 billion (in 2016) to a whopping $19.9 million by 2025. As you can see, this is not normal growth, the demand for ML is increasing exponentially. 

If you’re planning to give a boost to your career then ML is the best tool to do that. Learning this course can help you become a part of both the global and contemporary world. Machine learning is not limited to just the IT industry, it has a strong foothold in areas like cyber security, medicine, image recognition, facial recognition, and many more. As more and more businesses are realizing that this technology is impacting their business, they are investing more and more in it. 

For example, Netflix has put a reward of $1 million to anyone who can sharpen their machine learning algorithm by increasing its efficiency to 10%. This clearly indicates that even the slightest enhancement of ML algorithms can offer immense profit to the company, and thus more and more businesses are behind people who know ML. 

2. Attractive Salaries 

If you’re looking for a hike in your salary, then there is no better way than upskilling with a machine learning certification. Believe it or not, the best machine learning professionals earn as much as the popular sports personalities. According to Glassdoor.co.in, the average salary of a machine learning engineer is INR 10 lakhs per year ﹘ and it is their starting salary which eventually goes as high as INR 15 to 25 lakh per annum. 

3. Lack of Machine Learning Can Be Harmful to Companies 

Technological advancements are happening at the speed of lightning. And due to this, many corporations are left behind. Digital transformation is a vast field, and the fact is, there are not enough ML professionals to cater to increasing demands. 

If we look at the stats, then a New York Times study that took place in 2017 stated that the total number of professionals in the AI and ML field was less than 10,000 people all across the globe. 

This number is most likely to both increase and decrease. It is likely to increase because of the increased number of job opportunities that are being created, and it’s likely to decrease because more and more people are upskilling with ML every day. 

The best part about upskilling with ML is that you don’t need to have an advanced set of skills and qualifications to take a machine learning course, anyone from any background can learn it. 

Machine Learning is the Heart and Soul of Data Science 

There is no doubt that data science rules the market because of its innovative viability and all-explaining nature. And machine learning is the heart and soul of this pioneering technology. By becoming proficient in ML, you can build your career in the field of data science as well. Note that many organizations have data scientists and ML engineers working hand in hand to complete highly demanding tasks. You can get exposure to the world of data science while having a chance to learn and work with industry-leading experts. 

How to Get Started with Machine Learning in 2022?

Once you’ve made up your mind to become a machine learning expert, you’re just a step away from upskilling your career. All you need to do is find and enroll in the right machine learning course or certification program. With a combination of the right ML course, deduction, practice, and experience, you can soon become a machine learning professional. 

The Bottom Line

That’s all about why machine learning is the best way to upskill in 2022. We have discussed everything from the importance of machine learning, its potential benefits, and why you should learn machine learning. It’s up to you to use this data to make the right decision. 

How Machine Learning is Reshaping Location-Based Services?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Today life is a lot different from what it used to be a decade ago. The use of smartphones and location-empowered services is commonplace today. Think about the driving maps, forecasts of local weather and how the products that flash on your screen are perhaps just what you were looking for.

Location-enabled GPS services, devices that use them and each time we interact and use them generates data that allows data analysts to learn about our user-preferences, opportunities for expansion of their products, competitor services and much more. And all this was made possible by intelligent use of AI and ML concepts.

Here are some scenarios where AI and ML are set to make our lives better through location-based services.

Smart real-time gaming options without geographical boundaries.
Automatic driver-less transport.
Use of futuristic smartphone-like cyborgs.
Executing perilous tasks like bomb-disposals, precision cutting, and welding, etc.
Thermostats and smart grids for energy distribution to mitigate damage to our environment.
Robots and elderly care improvements.
Healthcare and diagnosis of diseases like cancer, diabetes, and more.
Monitoring banking, credit card and financial frauds.
Personalized tools for the digital media experience.
Customized investment reports and advice.
Improved logistics and systems for distribution.
Smart homes.
Integration of face and voice integration, biometrics and security into smart apps.
So how can machine learning actually impact the geo-location empowered services?

Navigational ease:

Firstly, through navigation that is empowering, democratic, accurate and proactive. This does mean that those days of paper maps, searching for the nearest petrol station or location, being late at the office since the traffic pileups were huge and so many more small inconveniences will be a thing of the past. We will gracefully move to enhanced machine learning smartphones that use the past data and recognize patterns to inform us if the route we use to commute to office has traffic snarls and provide us with alternative routes, suggest the nearest restaurant at lunchtime, find our misplaced keys, help us locate old friends in the area etc all by using a voice command to the digital assistant like Alexa, Siri or Google.

Machine Learning can make planning your day, how and when to get to where you need to be, providing you driving and navigational routes and information, and pinging you on when to leave your location a breeze. No wonder then that most companies like Uber, Nokia, Tesla, Lyft and even smarter startups that are yet to shine are investing heavily on ML and its development for real-time, locational navigational aids, smart cars, driverless electric vehicles and more.

Better applications:

Secondly, our apps are set to get smarter by the moment. At the moment most smartphones including Google, Apple, Nokia among many others are functioning as assistants and have replaced those to-do lists and calendar keeping for chores that include shopping, grocery pickups, and such.

Greater use of smart recommendatory technology:

And thirdly, mobile apps set smartphones apart and the more intelligent apps the better the phone experience gets. The time is not far off when ML will be able to use your data to actually know your preferences and needs. Imagine your phone keeping very accurate track of your grocery lists, where you buy them, planning and scheduling your shopping trips, reminding you when your gas is low, providing you with the easiest time-saving route to commute to wherever you need to go and yes, keep dreaming and letting the manufacturer’s know your needs for the future apps. The smart apps of the future would use your voice commands to suggest hotels, holiday destinations, diners, and even help you in budgeting. That’s where the applications of the future are headed to.

In summation, ML has the potential to pair with location-using technologies to improve and get smarter by the day. The future appears to be one where this pairing will be gainfully used and pay huge dividends in making life more easily livable.

To do the best machine learning courses try Imarticus Learning. They have an excellent track record of being industrially relevant, have an assured placement program and use futuristic and modern practical learning enabled ways of teaching even complex subjects like AI, ML and many more. Go ahead and empower yourself with such a course if you believe in a bright locational enabled ML smart future.

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

Posted on by Imarticus Learning
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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.

What Are the Algorithms in Machine Learning? How Does It Work?

Posted on by Imarticus Learning
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Machine learning is a vast field comprising of various data related operations such as analysis, prediction, decision making and much more. These applications require a set of well-defined steps to proceed with the idea designed for model construction. A set of well-defined instructions that produces some output or accomplishes a particular task is called an algorithm. The machine learning algorithms are broadly classified into 3 categories – Supervised, Unsupervised and Reinforcement Learning.

To choose an appropriate algorithm in machine learning, identifying the kind of problem is very necessary as each of these algorithms obeys a different plan of attack to deal with the proposed problem. Supervised learning uses an approach where the output is already known to the user or the individual while unsupervised learning concentrates on the concept of similarity in properties of the objects. Reinforcement learning differs from both of them and uses the art of learning from experiences.

Supervised learning

Supervised learning is used in machine learning tasks such as classification, regression, and analysis. It is considered as a concept that deals with labeled values. This means that the objects are categorized or assigned to different classes based on their properties. The algorithm implementation in supervised learning is done by a two-step procedure namely model construction and model utilization.

Firstly, the given data is cleaned and divided into training and testing sets. The model gains the ability to produce output by learning from the instances contained in the training set. The test set gives a measure of the model performance by producing accuracy. The accuracy indicates the amount or rather the percentage of unseen data that was computed correctly by the applied algorithm.

There are several metrics to determine the performance of the model and improve it if the performance is not up to the mark. This includes performing tasks like cross-validation, parameter tuning, etc. Hence, we can conclude that supervised learning uses labeled classes and target values to classify an unseen data point.

Unsupervised learning

In contrast to the supervised approach that already knows the predicted outcome, unsupervised learning uses the basis of similarity in properties to classify the unseen data points in the given n-dimensional space.

The main idea is to take a data point that is new to the given space, extract the behaviors of the data point, compare it with the already existing properties of the other objects and accordingly classify or categorize them into the appropriate group. The common examples of unsupervised learning are clustering, Apriori and K-means algorithm.

Reinforcement learning

Reinforcement learning is very similar to the animal kingdom where the animals do not train their offspring to perform a particular task but they leave them out in the ecosystem to learn from the experiences that it gains while struggling to accomplish a particular task.

The basic idea of performing reinforcement learning is to let the model learn on its own. It uses a trial and error strategy to gain knowledge from the available environment. According to the experiences gained from the conditions, it is exposed to, appropriate predictions and decisions are made. Markov Decision Process is an example of reinforcement learning.

Conclusion

Because of the wide variety of applications offered by machine learning, there are several Machine learning courses dedicated to offering the training in machine learning algorithms so that an individual can recognize the problem efficiently and work towards building an appropriate solution. Learning and understanding of machine learning algorithms are very easy. It just needs a proper classification of the interest in performing the desired operation.

What Is Distributed Computing Training in Machine 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.

How Machine Learning Systems Can Streamline Healthcare Disbursement Setups?

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Reading Time: 3 minutes

The ripple effects of the COVID19 pandemic have been felt across industries at several levels. The healthcare industry wasn’t spared either, with essential healthcare workers moving to the frontlines to deal with the emergency. As a result, many organizations saw their back-end operations, such as appointment bookings and disbursement trackers, floundering.

However, there is a silver lining in this situation– it’s that technology has speedily been integrated into systems. Telehealth software has seen a surge in demand so as to prevent risks of exposure; healthcare disbursements are next on the list to be made easier.

Healthcare disbursements are traditionally tricky and convoluted processes; the pandemic has put further amounts of strain on the system and caused frustration, delays, and errors. However, machine learning in healthcare is a step forward in fixing disbursement delays.

Here’s how:

  • Moving from Checks to Digital Disbursements

A majority of disbursement systems around the world rely heavily on cheques and other outdated methods. However, this has become a point of friction at this time considering courier services have shut down and deliveries are very delayed. In such a scenario, the use of digital reimbursement options, bolstered by machine learning, is tempting.

Providers can facilitate faster payouts through DTC (direct-to-consumer) payments. By shifting the process online, providers will also be able to keep track of all patient and consumer data on one server. Machine learning can be used to pull up the relevant information, create automated disbursement setups, and ensure the consumer receives their disbursement digitally. The reduced reliance on paper payment processes will lessen the load on healthcare finance systems as well as get disbursements out to the right people in a flash.

  • Addressing Glitches in Systems

Several reports talk of misplaced cheques, incorrect deposit information, and several such kinks in telehealth and digital healthcare solutions being used today. Machine learning can be leveraged to iron out these kinks because, especially during a healthcare crisis, such errors can have a snowball effect on consumers and providers alike.

Providers who use machine learning systems to manage delays will be able to maintain strict records of past and future payouts. The system can be trained to collect the right deposit information as well as cross-verify with other records if required. The reliance on an automated system, in this case, equals to a lesser reliance on outdated methods of payout tracking.

  • Simplify User Experience

Claiming payouts and processing them can become a nightmarish experience for both patients and healthcare providers alike. Machine learning systems effectively reduce quite a number of manual steps which, in turn, saves time, money, and efforts. Machine learning can be leveraged to extract critical information from healthcare contracts, estimate how much is owed, and prepare the right documentation in time for a payout.

For patients, too, the process of claiming payouts become simpler. They will no longer have to fill out a myriad of forms and move from office to counter over days. Instead, by automating certain processes from the providers’ ends, patients can be called in only to verify details if necessary and to provide any other physical documentation the healthcare provider may need.

Conclusion

The healthcare industry will most likely see a surge in the adoption of machine learning and artificial intelligence. This will be across the board– from handling disbursements to automating admissions and discharges. Therefore, students who are interested in pursuing an artificial intelligence career would do well to explore this niche and develop the right skillset for it.

You can do this by enrolling in a machine learning course that focuses on the healthcare system, or take on related projects that could leverage your portfolio when it comes to it. The current strains on healthcare providers worldwide have exposed significant cracks in the system that machine learning could most likely fix.

How To Build A Credit Scoring Model With Machine Learning?

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Credit bureaus and lending institutions have embraced big data and machine learning to develop credit score models on the basis of which the creditworthiness of a borrower is judged. This has many benefits for the business as they can better assess the risks of offering loans, gauge the repayments and plan accordingly. Businesses today take advantage of the huge volumes of data proliferating nearly every sector to create their own scoring models based on Big data and a long delicate and expert process of executing a machine learning course of algorithms to build their own models.

The trends:

The era of basing decisions solely on credit scores from bureaus are over. Today custom models work better and more accurately since they use data from a number of sources both internal and external to assess creditworthiness. Such data could include supplier information, account data, customer relationship or other market data. More the data the more accurate and efficient the scoring model becomes.

How to create the scoring model:

1. Goal setting:

Clear cut goal setting is important to achieve accurate results in scoring models. The goal needs to be in mine with the needs of business and its scoring model. For example, the goal could be the probability of late repayments of existing loans and dealing with the repercussions. Or, it could be using the data to decide on scoring the financial repayment plans of borrowers and their creditworthiness.

2. Data gathering:

This is a crucial requirement as all assessment is done on the basis of data. With enough data volumes and reliable data, a scoring model is made for the specific goals set. The test model so built can be used to supervise the model which will help in training the model under supervision from domain experts. Beyond this point, you will need to test the model with credible credit score website databases like the Boostcredit101.

3. Building the model:

With both internal data and comparative data in place, the experts can now build your scoring model. The Machine Learning Course procedure is complex and involves a large number of algorithms trained to interpret the data before the final test model is ready for deployment. It goes without saying that the goals of the business owners and the aim of the model builders need to be the same and both would need to contribute to the end goals and success of the scoring model being developed.

4. Validation:

The next phase is to validate the process and ensure the scoring model provides accurate results. Most applications lean on how to predict the late payments of the debtors. The scoring model will use the new data while scoring it against the test results to produce a score between 1 and 100. Higher scores mean fewer defaults and vice versa. These scores are also repeatedly done as changes in financial status, incomes and economic growth can all affect the score.

5. The implementation:

This final phase is where the permanency of the scoring model is tested by the actual implementation. A successful model will remain while the inefficient models get wiped out. Challenger models play the role of checking to see if the scoring model is functioning well or is the challenger is the better model.
The Big Data connection:
A shift from total reliance on credit-bureau data has seen lending banks, institutions, and companies that use credible data buy such data. Data is digital gold and large volumes of big data are needed to train AI on a machine learning course.The cleaning, parsing and making sense of such large volumes of multivariate data is a job for expert data scientists. This data is then used to create the scoring model be it a new or challenger model.
According to data scientists, it is these scoring models with the best ML algorithms that ML can accurately tap all unrelated factors and relationships in the data to provide a better scoring model. Though it is not without problems it is heartening that ML can help the machines self-learn with data and the more data one inputs the better are the results of the scoring model.

Conclusions:

The insights, big data, and ML have helped create scoring models for businesses, lenders, and organizations. While traditional credit bureau reports are also crucial, ML can go further with scoring models helping them add insights and provide newer business points of view. If you are interested in learning more about ML and credit risk scoring you could do a machine learning course at the Imarticus Learning Institute where futuristic technologies are taught and skilled on. Don’t wait too long. Start today!
For more details in brief and further career counseling, you can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

Hands-on Linear Regression Using Sklearn!

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Reading Time: 3 minutes

Linear regression in Machine Learning is a kind of analysis where there are both dependent and independent variables. Their relationship is depicted here by a linear line from the provided data. This technique is mainly used for predicting the results.

The data and the values from the linear regression are used to find the Mean Squared Error (MSE) to change the values of the variables so that they have the least value possible and get the most accurate results.

While there are several tools that are usually used for linear regression Sklearn is one of the popular tools.

What is Sklearn?

Sklearn is the short form for Scikit Learn which in Python Language, an open-source ML library to help unify the various tools of algorithms in machine learning.

Linear Regression trainingSklearn precisely works as a one-stop solution that helps with importing, preprocessing, plotting, and predicting data.

Its versatility, powerful nature, ample documentation, and active nature make it one of the popular toolkits in ML.

Linear Regression in machine learning

Linear regression comes at the last stage of the ML processing. Though there are several tools for the same, Sklearn is used more because of its versatility. When there is a log of consistent data it is easier to predict future patterns. The various steps towards the implementation of linear regression are,

  • Loading the data or object from its source
  • Training the model results in dividing the data into dependent and independent variables.
  • Later it is split the data into train and test sets.
  • It is the test set that will be used for the predictions.
  • Explore the results to find the suitability of the data for linear regression. A low accuracy data shows the non-compatibility of data to this.
  • Narrow down the data to a smaller portion and repeat the same steps above.
  • Now the data should be able to show the MSE which can be used for better predictions.

Importance of Linear regression 

Linear regression is important as it determines the accuracy of the prediction from the data given and also finds out the predictors that are significant for better results.

Linear Regression Training in Machine Learning

It is useful in determining the power between the dependent and independent variables such as sales and expenditure, or in the case of health care dose and its effects on the body, etc.

Analyzing the regression model explains how much the variables do change with respect to each other. Forecasting weather or its impact and changes is a fine example of this application. In the case of sales and marketing, it explains how much increase is possible in income with a specific amount of expenditure.

Finally, it is one of the main tools for predicting future trends and possibilities. It can be used for a future prediction of any time span. Linear regression using Sklearn provides a near-accurate prediction of the values.

In addition, there are different types of linear regression – simple or multiple linear regression. Each type is suitable for a different purpose or type of data available.

Conclusion

Its possibility is too vast that it requires proper Linear Regression Training to use the best data for getting the most accurate output. Once learned it is easier to predict data and values in the fields of sales, marketing, financial assessments, insights into consumer behavior, risks in finance or loans, etc.

As already mentioned, the accuracy of the prediction highly depends on how compatible the data is for the linear regression model. The Sklearn method is a far better option to determine the same and can provide the closest values to help with a better prediction model for the business.

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

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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?

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Reading Time: 2 minutes

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.