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Tag: Learn Machine Learning

How to learn machine learning – A complete guide for 2023

Posted on by Imarticus Learning

Machine learning is a rapidly growing field that has become increasingly important in many industries in recent years. It involves the use of algorithms and statistical models to enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Therefore, jobs in the field of machine learning and data analytics are continuously rising. If you want to enter this growing field and learn machine learning, then machine learning certification courses are one of the best ways to do so.

best big data analytics course

If you’re interested in building a career in data analytics in 2023, this guide will provide you with all the information you need to get started.

 

Here is how you can learn machine learning.

Understand the basics

Before diving into the world of machine learning, it’s important to have a good understanding of the basics. This includes understanding the different types of machine learning, such as supervised learning, unsupervised learning and reinforcement learning. This also includes algorithms and models used in each type.

Learn the programming languages

To learn machine learning, you’ll need to have a solid foundation in programming. Some of the most popular programming languages used in machine learning include Python, R and Java. It’s recommended to start with Python as it’s widely used and has a large community that offers plenty of resources and support.

Choose your tools

In addition to programming languages, you’ll also need to choose the right tools for your machine-learning projects. This includes frameworks like TensorFlow and PyTorch. This provides pre-built models and algorithms that you can use in your projects. Other tools like Jupyter Notebook, Pandas and NumPy are also essential for data manipulation and analysis. If you want to build a career in data analytics, then these tools can be really helpful.

Build your knowledge

To become proficient in machine learning, it’s important to build your knowledge through a combination of theory and practice. There are plenty of resources available online, including courses, books and tutorials. One of the best machine learning certification courses to get started is the Postgraduate Program in Data Analytics and Machine Learning.

Get hands-on experience

While learning the theory is important, getting hands-on experience is essential to becoming a skilled machine learning practitioner. This can be achieved through personal projects or by working on real-world problems. The Postgraduate Program in Data Analytics and Machine Learning by Imarticus focuses on practical knowledge and a job-oriented curriculum.

Join the community

Joining the machine learning community can help you learn faster and stay up-to-date with the latest developments in the field. There are plenty of online communities, including forums, slack groups and social media groups, that provide opportunities to connect with other machine learning practitioners and ask questions.

Keep learning

Machine learning as a concept is rapidly evolving. Hence, it’s critical to keep up with discoveries and updates in this field. This can be achieved through reading research papers, attending conferences and workshops and taking advanced courses.

Postgraduate program in data analytics and machine learning

Imarticus is a leading course platform that offers job-oriented certifications to help young professionals learn new skills like data analysis, machine learning and more. The Post Graduate Program in Data Science and Analytics is a 6-month job-oriented certification course offered to young professionals to help them learn machine learning and build a career in data analytics.

The course helps students build analytical models to solve real-world problems and improve business decisions. The machine learning certification course also offers a job interview guarantee that allows students to work with companies like Cognizant, IBM, Goldman Sachs, Standard Chartered and more. Following are the various benefits of completing the Post Graduate Program in Data Science and Analytics by Imarticus:

Young professionals are provided with a completion certificate by KPMG to boost their CVs.

The course also offers career services like interview workshops, mentorship, skill development workshops, career counselling, profile development and more.

The Post Graduate Program in Data Analytics and Machine Learning also provides students with a chance to participate in national-level competitions like KPMG India-organised hackathons.

Final thoughts

Learning machine learning requires a combination of theory, practice and community involvement. By following the steps outlined in this guide, you’ll be well on your way to becoming a skilled data analyst and machine learning practitioner in 2023. Enrol in the machine learning certification course by Imarticus to learn essential skills and take advantage of dedicated career services.

Remember to stay curious and keep learning as the field continues to evolve.

You want to learn artificial intelligence: What next?

Posted on by Imarticus Learning

You want to learn artificial intelligence: What next?

Artificial intelligence is an evolving technology that has been providing us with a plethora of job opportunities. With the advancement of data science and technology, artificial intelligence has made a mark on the lives of people. Not only locally, AI and machine learning also have a huge impact on the market. If you are looking for a course to learn python online, or learn artificial intelligence online, keep reading to know about this certificate course provided by IIT Guwahati and excel in your skills. 

AI or Artificial Intelligence is a technology that helps the machine to simulate human behaviour accurately. Artificial intelligence is a very trending technology in the course of data science which is the future for creating an efficient intelligent system. Sometimes, amateurs confuse AI and machine learning to be of the same category, however, it is not the same as one another. Machine learning, on the other hand, is a subset of AI which helps a machine learn past data that has been processed into it without being explicitly programmed to it.

AI and machine learning however do have the common goal to simplify complex problems to be solved much more efficiently. If you have pursued a career in data science and want to excel in your further knowledge in AI, this Machine learning certificate course by IIT Guwahati will be beneficial for you. You can learn about the principles of AI and machine learning with industry approved curriculum. 

Certification In Artificial Intelligence And Machine Learning At E&ICT Academy, IIT Guwahati 

best data analytics certification course

IIT Guwahati has been offering this lucrative course to learn artificial intelligence online which will help to excel in the career of the learner in the field. IIT Guwahati has been offering a plethora of course materials based on this subject which will help you excel in your knowledge of AI and machine learning.

The faculty who will be taking the classes for this course are highly trained and will provide the learners with their keen insights. Learn about the prerequisites, job opportunities and other important notions that will benefit you in taking up this career. 

Prerequisites 

If you are interested in pursuing this certificate course on artificial intelligence and machine learning, then you should have a bachelor’s or a master’s degree in either of the following disciplines. They are: 

  • Computer Science 
  • Engineering 
  • Maths 
  • Statistics 
  • Economics 
  • Any other science stream subject. 

The learner should have a minimum of 50% in aggregate in graduation. 

Job Opportunities 

With the advancement of the disciplines in data science, AI and machine learning is increasing by leaps and bounds. This method has been adopted by several multinational corporations and there are swathes of inspiration for learners who are thinking of taking up this course. The base salary of a Data scientist in India is an average of Rs.881,097 in a year. The job opportunities after completion of this course are feasible and will perk up career opportunities. Besides, the course has given job opportunities to many learners in Deloitte, Infosys, KPMG, Accenture, Kotak Mahindra Bank etc. 

Assignments And Mode Of Training

Learners have to do online training classes for getting this certificate course. They have to dedicate 8hours a week to attending live sessions by academicians and industry experts. There will be more than 100+ assignments alongside 25 real industry projects which will help in providing practical knowledge to know about JupyterHub, NumPy, OpenCV etc. 

Conclusion 

AI and Machine learning has been providing a plethora of learning opportunities to individuals which helps in advancing careers for individuals in data science. You should check out Imarticus’s website about pursuing the Certification Of Artificial Intelligence And Machine Learning which will give you a keen knowledge of data science and its different technological subsets. 

The domains of AI and Machine Learning (ML) are one of the fastest-growing sectors in the world. We are increasingly getting more dependent on services and products that are driven by AI and ML. AI/ML makes our systems faster, making our jobs easier. In this domain, there are always innovations being introduced and researched. Thus, the AI/ML sector is an extremely lucrative sector to join. 

What’s The Quickest Way To Learn Math For Machine Learning And Deep Learning?

Posted on by Imarticus Learning

In modern times we have everything from developments like smartphones, robots, driver-less cars, medical instruments like CAT scans and MRI machines, smart traffic lights, and a host of animated games. Even payments have gone digital and cashless! And all this has emerged over the last decade due to AI, ML, and data analytics.

The future holds great promise for development in these fields and to make a high-paid scope-filled career in any of these fields, mathematics is the key ingredient that you must learn if you want to learn machine learning. ML runs on algorithms and the algorithm is dependent on knowledge of mathematics and coding.

Why mathematics is so important in ML:

Some of the many reasons are :

  1. Selecting the apt algorithm with a mix of parameters including accuracy, model complexity, training time, number of features, number of parameters, and such.
  2. Selecting the validation of strategies and parameter-settings.
  3. Using the tradeoff of Bias-Variance in identifying under or overfitting.
  4. Estimating uncertainty and confidence intervals.

The math components required for ML:

ML algorithms require proficiency in the three topics of Linear Algebra, Probability Theory, and Multivariate Calculus.

Let us discuss the topics you need to learn machine learning under each of these heads.

A. Linear Algebra:

The use of Linear algebra notation in ML helps describe the structure of the ML algorithm and the parameters it depends on. Thus linear algebra is important in the interconnection of neural networks and their operations.

The topics that are important are : 

  • Vectors, Tensors, Scalars, Matrices,
  • Special Vectors and Matrices
  • Norms of Matrices
  • Eigenvalues and vectors

B. Multivariate Calculus:

ML learns from its experience with the data set and to supplement this we need calculus to power learning from examples, improving performance, and updating parameters of the different models.

The important topics here are : 

  • Integrals
  • Derivatives
  • Differential Operators
  • Gradients
  • Convex-Optimization

Probability Theory:

The assumptions about data use this theory to design the AI and its deep learning capabilities. The key probability distributions are crucial to algorithms.

Study these topics well.

  • Random Variables
  • Elements of Probability
  • Distributions
  • Special Random Variables
  • Variance and Expectation

Can you learn Math for ML quickly?

To learn machine learning it is not required to be an expert. Rather understand the concepts and applications of the math to ML. Doing things like math is time-consuming and laborious.

While there may be any number of resources online, Mathematics is best learned by solving problems and doing! You must undertake homework, assignments, and regular tests of your knowledge. One way of getting there quickly and easily is to do a learn machine learning course with a bootcamp for mathematics at Imarticus Learning

This will ensure the smooth transition of math and ML applications in a reputed institute for ML where they do conduct bootcamps. At the end of this course, you can build your algorithms and experiment with them in your projects. But, the main question that remains is why do a learn Machine Learning Course at Imarticus in the first place?

The Imarticus Learning course scores because: 

  • They have sufficient assignments, tests, hands-on practice, and bootcamps to help you revise and learn machine learning.
  • They use certified instructors and mentors drawn from the industry.
  • They integrate resume writing, personality development, mock interviews, and soft-skill development modules in the course.
  • They have convenient modes and timings to learn at your own pace for professionals and classroom mode for freshers and career aspirants.

Conclusion:

Mathematics is all about practice and more practice. However, it is crucial in today’s modern world where AI, ML, VR, AR, and CS rule. These sectors are where most career aspirants are seeking to make their careers, because of the ever-increasing demand for professionals and the fact that with an increase in data and the development of these core sectors, there are plentiful opportunities to land the well-paid jobs.

At the Imarticus, you can consider the Machine Learning course, you will find a variety of courses on offer for both the newbie and tech-geek wanting to go ahead in his/her career. Start today if you want to do a course in AI, ML, or Data Analytics. For more details in brief and further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Hyderabad, Delhi, and Gurgaon.

Explainable AI: Escaping The Black Box of AI and Machine Learning

Posted on by Imarticus Learning

With the introduction of machine learning, the vertices of Artificial Intelligence (AI) developed manifold and established their presence across multiple industries. Machine learning helps understand an entity and its behaviors through interpretations and detections of patterns. It has endless potential. But its difficulty is in forming a decision in the first place through a machine learning algorithm.

artificial intelligence and machine learning coursesThere are often concerns about the reliability of machine learning models because of the questions about processes adopted to arrive at an anonymous decision. AI and Machine learning courses help in comprehending extensive data through intelligent insights.

It is useful in applications like weather forecasting, fraud detection, etc. But there is a crucial requirement to understand the processes of ML because it can form decisions using insufficient, wrong, or biased information.

This is where Explainable AI comes into the picture. It is the bridge between the ML Black Box and AI. Experienced AI is a model that explains the logic, goals, and responsible decisive process behind a result to make it understandable to humans.

As per reports by Science Direct, certain models of AI developed early in the process were easy to interpret since they had a certain amount of observability and clarity in their processes. However, with the advent of complicated decision systems like Deep Neutral Network (DNN), the process has become more difficult.

The success of DNN models is a result of productive ML models and their parametric space. It comprises muliple parameters that result in making DNN a black-box model too complicated for users. The search for an understanding of how this mechanism works is at the other end of the black-box model.

A machine learning course makes the process a lot easier. As the need for transparency is rising, the information utilized in ML is no longer justifiable, as it does not provide any detailed explanations for their behavior. Explainable AI along with ML helps in addressing the partial innate of AI. These biases are detrimental in industries like healthcare, law, and recruitment.

Explainable AI consists of three basic core concepts, which are:

  1. Inspection
  2. Accurate predictions
  3. Traceability

Accurate predictions refer to the process of explanation of models about the results and conclusions reached that enhance decision understanding, and trust from users. The traceability factors help humans to intervene in the decision-making of AI and control their functioning in case of need. Because of these features, explainable AI is becoming more and more important these days. A machine learning career is on the rise. In recent predictions from Forrester, it was reported that 45% of AI decision-makers find trusting an AI system is very demanding.

To assist developers to understand ML and explainable AI in detail, IBM researchers open-sourced AI Explainability 360. Google also announced an advanced explainable AI tool. The field of explainable AI is growing. And with it, it will bring enhanced explainability, mitigation of biases, and greater results for every industry.

What Are The Application of Machine Learning in Medicine?

Posted on by Imarticus Learning

Medicine and healthcare are leading industries with altruistic goals. Smart applications of the Machine Learning are today playing a role in a variety of areas like billing, insurance claims, record-keeping, patient-care, staffing solutions and many more. Just over the last decade technology has changed much. So have the volumes of data and its complexities, the various smart devices being used, and the algorithms specific to medical care.

ML is the system of algorithms developed for specific tasks that use deep-learning and artificial intelligence to simulate the way in which the human brain works to execute and aid us in complex tasks. Hence Machine Learning Course is an evolving and very important field. The ability of the ML algorithms to accurately predict, self-learn and assist us with forecasts is truly amazing and way beyond what the human brain is capable of. The field of ML is lucrative too!

Let us look at some of the best applications of ML in the medical and allied fields.
1. Diagnosis on a global scale: ML algorithms and applications score in disease diagnosis, providing simple diagnosis and online treatment even in rural areas, identifying patterns in the disease progression that is hard to recognize, and creating a global repository of research to help better the healthcare segment. Some of these are cancer detection, treating underlying genetic conditions hard-to-diagnose like Parkinson’s, diabetes, etc, providing therapeutic aid, and predicting conditions like stress, depression, etc.

2. CAT scans and MRI aided Diagnosis: Deep Learning and ML have been responsible for the development of advanced imaging devices like the Computer-Aided-Tomography scans, Magnetic-Resonance-Imaging, and the non-invasive Doppler or ultrasound scans. These developments embody machine learning training, handling of large data volumes and the ability of AI and can easily be used to forecast accurately the development of tumours and aneurysms.

3. Manufacturing and discovery of new drugs: Research, discovery, and use of the latest drugs are very important today as we discover more diseases and strains that are traditional-drug-resistant. ML allows the learning in an unsupervised fashion and helps develop new drugs with optimum dosages which can even be personalized for cancer patients.

4. Modification behavior-therapy: Prevention being better than cure several startup firms have come out with gesture-control based therapy apps, symptomatic analysis, early cancer warning, and detection apps, sleep trackers and such behavior-sensing apps that can help even with predicting the susceptibility to genetic diseases. For ex: Angelina Jolie underwent mastectomy based on such a forecast of cancer.

5. App-based medical personalization: Currently medical predictions use biosensors, historical data, genetic information and symptoms to diagnose ailments which tend to have rather fixed treatment methods. The time is not far where ML can offer holistic personalized treatment options which can help faster recoveries by identifying the underlying cause, diets, optimum medicine dosages, and recovery paths in a moment. Even the very process of seeking doctor’s advice appears to be going online to reach out further into rural areas and the personalization of healthcare regimens.

6. Outbreak Prediction: ML, deep-learning and AI have succeeded in not only assisting in healthcare but can also accurately and through unsupervised learning predict and forecast epidemic outbreaks which can then be nipped in the bud.

7. Smart Record Keeping and insurance claims:
The better the maintenance of records the easier cashless hospitalization and treatment becomes. ML is taking very large strides with smart apps to enable smart health-records in real-time, better document classification and intelligent paperless claim settlements.

8. R and D and medical trials: ML has and bears high potential in these areas of medical care where innovation and data play a huge role.

9. Data Crowdsourcing: The smart apps powered through intelligent ML allows doctors globally to crowdsource their data resources making it easy to give a real-time diagnosis, treatment, and better healthcare facilities. Even operations and complex deliveries can now be conducted easily and safely.
10. Radiotherapy and medical imaging:
Radiology, laser treatments, pathology, gastroenterology, cosmetology and several disciplines of medicine are being innovatively modernized with smart algorithmic-based ML to make model-making of individual cases much easier to treat successfully.

In conclusion, ML is growing by the moment and now is the right time to get on board this transformation. If you want to learn Machine Learning and reap career benefits in the medical field then you need to have Machine Learning Training.

At Imarticus Learning, the mentored specialization in skill-oriented courses like ML is par excellence and comes with certification, skill-based training, personality development, and assured placements. Hurry!

What Are The Best Courses For Cyber Security Using Machine Learning?

Posted on by Imarticus Learning

What Are The Best Courses For Cyber Security Using Machine Learning?

Today everything is online and from such activity the sheer volumes of data generated, its management and security from misuse is a matter of concern that cybersecurity professionals are tackling on a war footing.

ML and AI have seen huge developments in the last decade in conjunction with the rapid growth of data and data analytics. Most organizations value their data and ML as organizational assets. So any threat to them or the devices connected to the algorithms is considered a serious cybersecurity threat. And cybersecurity depends more and more on ML since it holds potential for analyzing large volumes of data, structure and process data in real-time, and present instantly any threat to its intelligence as it occurs.

To learn machine learning is the mainstay of threat intelligence which alerts you so you can deal with mitigating the threats. Gone are the days of incoming alerts and handling attacks. Today we have advanced ML where threat intelligence is the buzz word.

Why? Because, ML has huge applications in helping organizations defend against malware, apply TI (threat intelligence), make unknown connections, identify key parameters, the transformation of unstructured text, threat actors, and such relevant risks.

Cybersecurity ML algorithms and software help: 

  • Understand why and how ML will affect the future of cybersecurity
  • AI techniques add value to ML to make the analysts more effective.
  • Obtain insights on ML processes for threat intelligence.
  • Help to detect future threats through predictive analytics.

How to become a cybersecurity professional:

Choosing a career in cybersecurity or opting to change careers to it is a great career move at the moment. You will have to learn machine learning with a reputed training institute like Imarticus Learning who are renowned for fast-tracking career options and enhancing technical skills required for careers in the latest emerging fields. Such learning courses are available online with a host of reading and comprehension on cybersecurity risks and its mitigation. However, classroom sessions and supervised learning will also be needed to gain practical and implementation skills.

You could start with an entry-level position gaining experience in security, risk management or IT and move your way up to a mid-level role as an analyst, security administrator, risk auditor or cybersecurity engineer. To sharpen and hone your cybersecurity skills advanced training and certifications will be required before you can actually practice as a security consultant.

Cybersecurity education:

A formal after-school college experience for an associate’s degree will take four full-time semesters or two years to start as an Intern. A bachelor’s degree could last 8-semesters or four years and the master’s degree will last two years or another 4-semester duration should help you learn all about the theory behind cybersecurity.

The actual practice of writing algorithms can be honed by online challenges participation, certifications and hackathons on Kaggle. The necessary attributes for cybersecurity would be proficiency in English, mathematics, and statistics. Combined with a certification you are set to start your career according to the BLS.

The top-10 roles in Cyber Security:

The field of risk evaluation, mitigation and prediction are growing with data analytics and data taking center stage in modern times. Take your pick of career paths from a few of the roles enumerated here to always be in demand.

  • Ethical Hackers.
  • Security Systems Administrator.
  • Security Consultant.
  • Computer Forensics Analysts.
  • Information Security Analyst.
  • Chief Information Security Officer.
  • IT Security Consultant.
  • Penetration Tester.

The top cybersecurity certifications:

Certifications are essential to your resume and offer employers a real-time measurable scale of your skills in cybersecurity and validate that you can implement and use the learn machine learning applications for risks and security of cyber systems effectively and practically.

The top certifications are: 

  • The ISC (2) Certification
  • CISM- Information Systems Manager
  • CISA- Information Security Auditor
  • Information Systems and Risk Control Certified
  • CEH- Ethical Hacker
  • Tester GIAC/ GPEN Penetration
  • Cyber Security Courses     State Approved

Payouts:

The Cybersecurity professionals have a median salary of 116,000 USD. At an hourly rate of 55.77 USD/ hour, it is almost thrice the national average income offered to full-time workers. The BLS reports make the high salaries a very attractive feature to make cybersecurity your dream career.

Conclusions:

The cybersecurity professional is highly paid and has immense job scope in a variety of roles. Formal education, practical skills, certification and performance at the end-of-the-day will set you apart and help your career progression. There definitely is a huge demand for the cybersecurity professions which will continue into the next decade according to most reports on Glassdoor, Payscale, and BLS.

Resources are available aplenty to make cybersecurity your career no matter where you live. The Imarticus Learning courses, unlike many online programs, have limited class sizes meant to enhance learning, certification attainment and networking.

So hurry and learn machine learning! Also, for more details and further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

How Does a Beginner Start To Learn Machine Learning, Having Some Knowledge of Programming Languages?

Posted on by Imarticus Learning

So you have learnt to programme and were having a happy time at work, by putting into practice what you learnt. Suddenly, out of nowhere, this huge wave of Machine Learning comes up, and you are all at sea! You know programming, but now you need to learn machine learning to stay relevant. And you are staring at a Herculean task, sifting through all the available machine learning courses online – unable to decide, whether any of them can actually help you out!
The first question you should ask yourself is what the kind of role that you see yourself playing in the future is? Yes, before actually diving into the time and effort consuming task of attending machine learning courses, you should make an effort to read as much as possible. Read articles, participate in forums, talk to people and find out, what are the kind of roles on offer in this field. See which of these matches your aspirations and career goals. Before you start to learn machine learning, you should know the field, however abstract it may seem.
Once you know, what the hullabaloo is all about, it is extremely crucial to assess your current skills. Yes, you know programming, but which languages are you familiar with? Can they help you in getting deeper in the domain of machine learning and artificial intelligence? If your answer is no, the first set of machine learning courses you should look into are the ones that familiarize you with such languages like R or Python.
As you become familiar with the programming languages that will help you learn machine learning, do an honest assessment, whether you really like programming in them and have you decided to pursue the career as a programmer. In that scenario, you should look at more and more advanced machine learning courses that teach you the intricate details of programming in R or Python. Also, you should undertake some of the basic machine learning courses that help you to familiarize yourself with algorithms and statistics.
In case programming is not what you want to pursue, but analytics is where your heart and ambition lies; you should look at machine learning courses or even generic artificial intelligence courses dealing with statistics, algorithms, when and where to use them. This will give you a much better grip on which algorithm to apply in which situation and the logic behind it.
As you become more and more proficient in the logic, you should also be able to develop quick prototypes of your proposed solutions with your understanding of the programming languages. This is the benefit of your efforts to learn machine learning in a streamlined manner. You can easily relate to the problem, identify solutions, build prototypes, evaluate and improvise. No one can actually stop you.
And lo! You have crossed the hurdle with the help of machine learning courses online. All you needed was a firm decision to learn machine learning and determination to achieve your goals with dedicated efforts.

Want To Learn Artificial Intelligence And Machine Learning? Where Can You Start?

Posted on by Imarticus Learning

These are exciting times to be a part of the technology industry, what with newer and fascinating fields being discovered every day. With the tech companies leading the way, the newer fields like Artificial Intelligence and Machine Learning are finding takers in multiple domains. Probably, this is the reason the demand for machine learning courses is at an all-time high.
Every second person, in the technology domain, you interact with would wax eloquent about how they are going to learn machine learning or how they have been taking the latest and most difficult artificial intelligence courses. Hearing this can be quite an unnerving experience, especially if you are a newbie in the field and looking to find out some machine learning courses that can help you unravel this mysterious world.
But, fear no more, as in this article, we try to help you find your initial foothold in this domain and slowly but surely come up to speed in your quest to learn machine learning. The first thing that confuses newcomers and throws them off-track when they begin to learn machine learning is the different terms and their interrelationships. What do the terms – Artificial Intelligence, Deep Learning, Neural Network Programming, Machine Learning – mean and how are they related.
If you somehow can navigate through the maze of big data and machine learning courses and get a hang of these terms, another big question comes up. Do I need to learn programming, statistics as well as calculus? Is this the right direction for my career, even though I do not understand either of this? Is there a way to learn machine learning without being proficient in all these?

There are no straight forward answers to these questions. But below are a few pointers that may help you to take your first step as well as identifying the correct artificial intelligence courses for yourself.
First and foremost, we need to understand the interrelationship between artificial intelligence and machine learning. Artificial intelligence is mostly trying to mimic human intelligence and behaviour by the machines including creativity, learning and reactions to any situation. As you start to learn machine learning with the help of machine learning courses, you realize that machine learning is nothing but a subset of artificial intelligence, dealing with pattern recognition and self-learning.
Now, the next fundamental question – do you need to learn programming language or statistics to complete artificial intelligence courses? You definitely need to have some basics in both as the statistics help you understand what you are doing, and the programming language shows how it is done. In case either of these is unknown to you, it is recommended you start your quest to learn machine learning with the courses for these.
You can find some excellent courses to learn programming languages like R or Python, models and algorithms basics through – imarticus.org Or also can contact on – info@imarticus.com or 1-800-267-7679 Or else you can visit our different training locations in India – Mumbai, Pune, Thane, New Delhi, Banglore, Chennai, Gurgaon, Hyderabad and Ahmedabad.
Keep Learning…!!

What are The Best Machine Learning Prediction Models for Stocks?

Posted on by Imarticus Learning

Predicting stock prices has been at the focus for a long time due to monetary benefits it can yield. Prediction of the future stock price is trying to determine the future value of a company stock which is traded on a stock exchange. Traditionally investors have relied upon fundamental research and technical analysis to predict the stock price movements.  Fundamental analysis is concerned with the performance of the company and its business environment. Investors mainly consider the current price and likely future performance of the company while picking the stocks.
Technical Analysis is concerned with past patterns of the stock price movements and predicting future trends. Lately,  machine learning models are also used in technical analysis to process the historical and current data of public companies to predict their stock prices. Mathematical models can be developed which process historical data about quarterly financials, trading data, latest announcements, and news flow etc and machine learning techniques can identify patterns and insights that can be used to make predictions for stocks. Trading signals can be generated and because correlation based on which the trading call is given is often weak, the time window in which profit can be made by the execution of the trade is usually very small.  Therefore, firms that specialize in ‘quant’ trading keep their machine learning algorithms simple and secretive so their trading strategies can be optimized for speed and reliability.
Now, we take a brief look at some of the machine learning models for prediction of stock prices.
Moving Average – Moving average is average of past ‘n’ values and is considered widely in technical analysis.  20 day, 50 days and 200-day moving averages of stock prices and indices are critical data points in predicting future trends.
Exponential Moving Average (EMA) differs from simple moving average in that it gives greater weightage to the most recent values compared to the older values.
Linear Regression is another commonly used statistical approach to model the relationship between a scalar response and one or more independent variables.
Support Vector Machines (SVM) is a machine learning technique based on binary classification, which is now greatly used in predicting whether the price of a stock will be higher or lower after a specific amount of time-based on certain parameters.
There are also a few non-statistical models that are being used to forecast stock price movements. A textual analysis of financial news articles is one such method. In this method, a crawler is trained to scan all the financial news articles and look for the patterns that are likely to have an impact on prices of specific stocks. Text mining of historical news articles with concurrent time series analysis can be done to figure out the impact of various types of news articles. Different weightage for articles based on the credibility of their sources can be given.
Thus, Machine learning can be applied to stock data and mathematical models can be developed to predict stock prices. Trading strategies can be optimized for speed relying on these models while simultaneously eliminating human sentiments from decision making.
There is a lot to explore with regards to stock predictions and machine learning models that need further explanation cannot be expatiated in a concise article like this.  The machine learning future in India is very bright.  If you need to pursue machine learning courses, learn from pioneers like Imarticus.

What Do You Need To Know For AI

Posted on by Imarticus Learning

In a world where technology is developing at a rapid rate, fields that focus on automation and artificial intelligence are becoming the most lucrative. With many artificial intelligence courses available, it is important to remember that a strong base is fundamental.

So, the question is – What do you need to know before you can venture into a field like artificial intelligence?

Before heading to the pre-requisites, it is important to understand that AI is a field that is multi-dimensional. It can be used for anything from medicine to education. This also means programming AI is diverse, akin to law, where you constantly need to educate yourself on the updates of the technologies available in your field of AI.

Finally, the different fields of AI can have specific requirements, but on a broader scale, most AI in any field requires strong foundations that are basically the same. Here are a few things you need to know before studying about artificial intelligence.

Numbers Are Key
A strong understanding of mathematics is a must when venturing into artificial intelligence. The key here isn’t just knowing basic math. If you hope to venture into artificial intelligence, a deep understanding of discrete mathematics is part of the core foundation of the field.

Most artificial intelligence is based on various algorithms, and an understanding of these algorithms, as well as the ability to mathematically analyse them for errors and solutions, are considered the most basic requirement for AI.

Programming
 Much like math, programming is an essential part of artificial intelligence, implementing the mathematical data into code in a manner where you can not only develop but maintain and enhance machine learning is also part of the core foundation of AI. This means you must be able to code at a high level and find a way to be creative with code to improve the functions of a developing AI system.

In-depth knowledge of Python is often considered a mandatory pre-requisite to learning artificial intelligence as this open sourced programming language is currently the most popular and widely used.

Analyzing Data
 While programming and math are the foundations, the ability to analyse and interpret data is considered a cornerstone for anyone involved with developing AI. This skill is important as this is where the error guidance and solution base of AI stems from. Imagine a world where you create an algorithm and program that algorithm into a robot to vacuum.

This works successfully as a single task. Imagine now that you integrate another code into the same robot to do the dishes. The robot accidentally breaks the dishes or uses bleach to wash the dishes. This error is because the codes can overlap and create a fault or a bug. Data interpretation is essential to identify faults and bugs in order to rectify them.

Conclusion
While the three pre-requisites mentioned above are core tools for those studying AI, they aren’t the only ones you need. The field of AI you venture into may require knowledge of the field itself. An example of this is medical AI, where you will need an in-depth knowledge of medicine and how medicine functions. AI is ever growing, and its complexities are deep.

No matter the type of AI you choose to learn, a strong understanding of math, programming and the ability to analyse data accurately are a must.