Contact Us
×

Tag: Neural Network

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

Posted on by Imarticus Learning

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.

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.

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 Makes an Imarticus – UCLA Certified Analytics & AI Professionals To Be Forever in Demand

Posted on by Imarticus Learning

Modern-day technologies like AI (Artificial Intelligence), ML (Machine Learning), data analytics, etc. are revolutionizing the working culture of businesses & firms. Analytics professionals use these technologies to ease & pace the analytics process.

Analytics professionals are required to extract meaningful insights from huge chunks of unorganized data to make better business decisions. Professionals with a certification from a reliable source surely get an edge over others when it comes to upward mobility.

Let us see how can you get the Imarticus – UCLA certification in Analytics & AI along with its benefits.    

Course Overview

Imarticus provides a post-graduate program in Analytics & Artificial Intelligence in collaboration with UCLA Extension which is a major certificate issuing institution in the United States.

Analytics and Artificial Intelligence CourseYou will get dual certification from two reputed sources if you opt for this course. Many individuals have built their Analytics and Artificial Intelligence careers successfully with the help of this course.

You will be receiving live training from world-class celebrities from UCLA & Imarticus. This course has around 400+ hiring partners with a 3-30 lakh salary range. You should have completed your graduation with at least 60% marks to enroll in this course.

Let us see how Imarticus – Analytics & AI professionals are forever in demand.

Broad Course Contents

This 28 weeks long online course covers all the aspects of analytics & AI along with data science fundamentals which helps in building a successful career. You will get to know about various tools/languages via this course like Python, Scikit-learn, Keras, TensorFlow, NLTK, OpenCV, etc. The major topics which will be covered in this course are as follows:

  • Data Science Fundamentals – You will be introduced to statistics in the first four weeks. You will also get to learn Python basics during this tenure. After that, you will perform data analysis with Python. Various concepts of data processing & statistics like central tendency, standard deviation, Z-score, etc. will be taught to you.
  • Machine Learning – ML will be taught to you for 6 weeks. Various topics of machine learning like multiple regression, correlation analysis, dummy variables, etc. will be taught to you. You will also be able to run various ML models in Excel in the practical classes. You will also learn about data science business models developed with the help of machine learning.
  • Deep Learning – Deep learning is a cutting-edge technology used for forecasting & enhancing decision-making ability. You will be taught about deep learning with an industry-first approach for 6 weeks.
  • NLP – Natural Language Processing (NLP) has helped in enhancing the ways humans react with computers. It has also automated the analytics process in firms/organizations. You will be studying this topic for about 4 weeks.
  • Computer Vision – You will be taught about computer vision, an AI-based technology that helps computers in extracting information for digital images and videos. You will focus on this topic for around two weeks including practical classes.

Pros of the Course

Besides the gravity of certifying institutions, the teaching methodology of this course ensures that the students will have a long run in the industry.

 

Analytics and Artificial Intelligence CourseThe pros of opting for this Imarticu’s Analytics and Artificial Intelligence course are as follows:

  • This instructor-led training is self-paced so that you can take your time to understand concepts.
  • You will work on various in-class industry-oriented projects to know about the practices in the industry.
  • You will also come across practice projects, boot camps, capstone projects, workshops, etc. if you opt for this course.
  • You will also get to test yourself in a hackathon at the end of the course.
  • The last two weeks of the course will be dedicated to placement preparation where you will get to learn from industry experts. Imarticus also provide excellent placement support to its students.
  • You will be provided a dedicated program mentor to guide you through the course and also for career advice. You can also monitor your test results & course progress.Analytics and Artificial Intelligence Course

Conclusion

This course follows an industry-first approach to make you ready for the industry. You can build a successful career with the broad topic coverage & placement support of this course.

Analytics and Artificial Intelligence Curse

Enroll in the Imarticus – UCLA Analytics & AI course now!

What is Differential Deep Learning?

Posted on by Imarticus Learning

Introduction

The world today has changed drastically. Therefore, it is important to change according to the environment in which we function. One such major change has been brought about by the advent caused by Artificial Intelligence.

Deep learning is a concept which has come around from artificial intelligence. It has been adopted by a lot of companies who want to automate their processes and make things easy. Also, people have started transitioning to deep learning to cut their losses and maximise profits. Therefore, deep learning is creating a lot of jobs in the market.

So, you can take up a Deep Learning course to increase your employability.

Understanding Differential Deep Learning

Deep learning is a way of implementing mathematical methods in the day to day working of a company. Numbers change every day. In most cases, we have to do these changes manually. With deep learning, you can keep a tap on the numbers without any additional effort.

With Deep Learning, you can formulate different algorithms to make your job easy. Deep Learning provides you with the ease of making predictions about future rates and prices based on the past and current trends and also on the market situations at different points of time. It provides ease of calculating and predicting values even if the market is volatile. Deep learning increases the analytic speed and efficiency of any company.

With proper Machine Learning training, you can design different algorithms for a different set of problems with which the company deals. Application of Deep Learning brings in automation which drastically improves the performance of any system is it calculating the sales value, keeping a tap on the generated leads, making a financial analysis, etc.

Differential Learning principals can be applied to different machine learning algorithms. Also, once you are well versed with the working of differential learning, you can try your hands on any machine learning language. It is going to be easy for you to understand.

You can apply Differential learning in models like regression, Principal component analysis, etc to get the best results. Also, differential deep learning is effectively used in the field of Finance. This can be used to develop a mechanism on pricing.

You can also use this to manage and assess various risks. Differential deep learning is effectively used to produce different models for closed-form solutions. Various Risk management metrics like hedge strategies, etc can be easily generated with the help of deep learning.

Uses of Differential Deep Learning

  1. It is used for developing different machine learning models where these algorithms are built to give results on different sets of inputs.
  2. Differential deep learning is being widely used in the field of risk management. With the use of different models, you can assess the situation at different values,
  3. It makes use of Automatic Adjoint Differentiation which can calculate changes and derivative sensitives quite easily.
  4. With Differential deep learning various fast pricing analytics can be formed which can be used to compute the metrics of risk management at different sets of information
  5. It is used to extract important estimations and values even when small data sets are available. Using differential deep learning can bring you to a conclusion very easily.
  6. It replaces the frequent interference of humans which was a prerequisite earlier. Differential Deep learning creates a system which is automated and reduces the cost of labour to a great extent. This system reduces the chances of errors drastically.

Conclusion

Differential Deep learning has changed the way we use our data. It has made the application of Machine learning possible. Also, with the help of this, companies are moving towards a fully automated world.

How the Indian Government’s AI Website Will Help the Country’s Artificial Intelligence Industry?

Posted on by Imarticus Learning

Collaboratively developed by the National Association of Software and Service Companies (Nasscom) and the National e-Governance Division of the Ministry of Electronics and Information Technology (MeitY), the portal is intended to be a one-stop-shop for all AI-related developments to happen in India.

The portal will also distribute documents, case studies, research reports and more relating to AI, and will also become a repository for AI-related learning job roles.

Noteworthy Resources On the Website

  • AI Courses

In order to help professionals and fresh graduates upskill, a variety of AI-related and machine learning courses are being offered through this government portal. The course providers range from IIT Bombay to companies such as IBM.

  • Editorial content

In order to collect all related information in one place, the website publishes editorial content and news as well as case studies and resources for budding AI entrepreneurs.

  • Highlighting successes

The portal also aims to highlight the country’s successes in AI in the recent past, spanning industries and use cases.

How the Website Will Help India’s AI Industry

The website is a distinctive step forward in recognizing the potential and scope of AI in India. Here are some ways in which the website can benefit the AI landscape at national and grassroots levels:

  • Democratising AI

Sanjay Dhotre, the MoS for E&IT, Communications, and HRD, said that digital innovation, including AI, has been a massive equaliser among the Indian population despite the diversity of the country. AI has already penetrated several homes; in the future, this website hopes to bring AI to virtually every part of the country and expound on its benefits in a growing economy.

  • Cementing India’s Place in the World

This public declaration of support towards Artificial Intelligence developments is a solid way of confirming to the world that India is investing in the technologies of the future. India has already supplied global leaders in technology to different parts of the world. By funding this AI website and encouraging its use, the country is making its way up the list of countries with one eye on the foreseeable future.

  • Encouraging Innovation

By making case studies, courses and resources accessible to the masses, the AI website is, in effect, encouraging each and every citizen to experiment in the field. There is no dearth of innovation and ideation in the country; this website becomes a place for resources to support the realization of AI-related dreams and the furthering of the country’s technological goals.

  • Building Networks

The website is touted as a convergence point for students, fresh graduates, AI experts, experimental businesses and new-age entrepreneurs to share and transact over revolutionary AI-driven ideas and proposals. The website also becomes a launching pad for innovation and a networking platform for those with ideas and those with the power to fund them. The departments are also working on creating investor pitch events to link ideators and funders alike.

  • Preparing the Future Generation

Alongside the website, the departments also announced the Responsible AI for Youth Program which is targeted at imparting AI education in government schools. The different phases of the programme aim to train teachers and gather groundbreaking ideas from the students themselves. Keeping in mind that India is one of the most populous countries in the world, instilling an understanding of AI and a passion for innovation at a young age is critical to the growth of the country internationally.

Conclusion

The AI website can be looked at as a show of support towards futuristic technologies that emerge from the grassroots level. By attempting to democratize AI, this government website is also creating a conducive atmosphere for technological growth on par with the rest of the world.