Contact Us
×

Tag: best ai and ml courses

Top 5 machine learning skills desired by employers

Posted on by Imarticus Learning

AIML or Artificial Intelligence and Machine Learning are some of the leading subject matters in the tech industry. ML is a branch of AI and has a wide range of applications in our daily lives, ranging from traffic predictions, face and voice recognition, product recommendations, virtual personal assistant, fraud detection, automatic language translation, and many more.

Therefore, big ventures like Google and Facebook are exclusively employing AIML in their products and services. Machine learning is the process by which computer scientists and engineers attempt to impart intelligent behavior into machines, to make them think and respond like human-mind in real-time situations.

For example, Google Assistant, Cortana, and Siri are entirely powered by machine learning algorithms that recognize speech.

AIML works in a complex way to make predictions and decisions based on past data, eventually refining its accuracy. A machine learning course can definitely help someone study and get training in machine learning data and algorithms.

What are the top Machine Learning Skills?

To get a desirable job related to machine learning – data engineer, machine learning engineer or machine learning scientist – you need to have knowledge and training in both software engineering and data science.

Following are the top 5 machine learning skills desired by employers:

  1. Computer Science Fundamentals and Programming

If you are getting into a technical world, then you need to have knowledge of CS fundamentals like data structures (graphs, stacks, queues, etc), algorithms (optimizing, dynamic programming, etc), computability, and complexity (NP problems, P vs NP, etc).

Having experience in different programming languages, like Python and Java, will make it easy for you to implement these fundamentals for better results.

  1. Applied Mathematics

Within applied mathematics, probability and statistics go hand in hand. Many machine learning algorithms employ probability and its techniques, like Markov Decision Process and Bayes Net, to approach uncertainties and deal with them.

You should also be well-versed in statistics to be able to build algorithms from observed data through the application of various measures, analysis methods, and distributions.

  1. Data Modelling and Evaluation

Data modeling is the process of understanding the underlying structure of a dataset, in order to find complex patterns. Furthermore, you will have to evaluate the data to be able to choose an effective accuracy/error measure like regression, clustering, and classification.

The kind of evaluation strategy that you will apply, whether it’s training-testing split or sequential vs randomized cross-validation, depends on your knowledge of data modeling and its different measures.

  1. Machine Learning Algorithms

ML algorithms are broadly characterized into three categories – supervised, unsupervised and reinforcement machine learning algorithms. You can effectively choose a machine learning algorithm if you are aware of the learning procedures and hyperparameters that affect the learning.

Some of the common algorithms are K Means Clustering, Naïve Bayes Classifier, Support Vector Machine and Linear Regression. Having appropriate knowledge of the advantages and disadvantages of these algorithms is essential to machine learning.

  1. Natural Language Processing (NLP)

NLP is the bedrock of machine learning. It is a learning model through which a computer is made to understand and interpret the human language. Many libraries across the world provide the foundation of NLP and help computers understand human language by decoding the text or speech according to its syntax.

Natural Language Toolkit is one of the most popular libraries to build NLP applications. Without the basic skill of using NLP, it can become fairly difficult to get into machine learning.

 Conclusion

All these skills come under one roof with the Artificial Intelligence and Machine Learning course offered by Imarticus. A PG in Data Analytics and Machine Learning will definitely polish these top skills and help you understand related concepts such as Deep Learning and Artificial Neural networks.

AI courses: Human geography inspired machine learning algorithms

Posted on by Imarticus Learning

Numerous industries like finance, healthcare, insurance, and computer system businesses employ software developers. If you wish to become a software developer, you can choose Imarticus Learning’s SCBI course. It is one of the best certifications for software engineers. 

What are 6 Main Practices for a Successful Career in Software Development? 

To become a software developer, you need to be aware of the business conditions. This will help you understand what skills you need. A software engineering certification is also essential. Similar to these, there are a few more practices that will ensure a long and rewarding career. Take a look at the following things you can do to improve your prospects in the field. 

 

  • Learn Programming Languages

 

If you wish to become a successful software developer, you need to learn relevant programming languages. You can focus on Java as it is a programming language that works for different platforms and purposes. You can also learn other programming languages like C++, Python, and Scala. 

 

  • Choose a Niche and Practice

 

Software development includes different aspects like app design, DevOps, and the development of computer infrastructure. So you need to choose a niche. As a software developer, you will need to focus on one particular aspect and hone your skills. You can always focus on different aspects but it is best to become an expert in one area. This will help companies identify you as a developer within that niche and thus, improve your chances of employment. 

 

  • Create a Portfolio

 

As you develop your skills and try your hand at design or development projects, you need to start creating a portfolio. A portfolio will help companies understand the extent of their knowledge.

 

  • Explore Potential Industries to Figure Out a Target

 

You need to check what industries are currently requiring software development services. Once you figure out which industry you want to work in, you can start learning the necessary skills. It is important to research every industry and then work towards an end goal. 

 

  • Focus on Soft Skills

 

While industry knowledge and software development training are necessary, you also need to have proper soft skills. Spend time improving your techniques, interact with peers and experts and build a network. 

 

  • Receive Training and Certification for All Skills

 

The final practice which is crucial is a certification. While you can be a self-taught software developer, it is best to receive a certificate from a renowned institute like Imarticus Learning. This will help you stand out and also approach important companies in the industry. 

How to Start Your Career as a Software Developer? 

Before you start a career in software development, you need to enroll in a software developer course. At Imarticus Learning, we offer Certification in Software Engineering for Cloud, Blockchain, and IoT.

For this course, Imarticus Learning has collaborated with IIT Guwahati and E&ICT Academy. Experts from these institutions have created a curriculum that is futuristic and closely related to the current business needs and conditions.

Academicians hold interactive sessions and encourage students to learn through active participation. You can also interact with your peers and develop your soft skills. The duration of this program is 9 months and over this period, you will receive intensive, hands-on training. You will learn the fundamentals of new-age software development and engineering and software architecture. 

Once you complete the course from Imarticus Learning, you will be able to sit for interviews and land lucrative jobs at reputable companies. The industry experience that you receive through 6 Capstone projects will help you secure your future as a software developer.

How AI courses are empowering Influencer Marketing?

Posted on by Imarticus Learning

The AI certification is more relatable to the industries such as security, e-commerce, surveillance, etc. People don’t usually think of it in terms of marketing, especially influencer marketing. But it is one of the most effective strategies to promote a product. 

Influencers are the big thing these days in marketing. People tend to follow and believe them and try to implement the tips and tricks by these experts in their lives. Brands are looking for those successful influencers to market their products in their relevant industries and niches. 

There may not be a wider range of customers with influencer marketing but the small number of them could turn out to be more loyal and make a strong base. This is why it is essential to find successful influencers in the respective niches. Artificial Intelligence can help in this search for influencers and save them time and effort. 

How do AI courses help?

For the successful application of AI in influencer marketing, one must know how it works in this industry. Those who pursue any kind of Artificial intelligence and Machine Learning course must be doing a project at the end of their course. Courses such as the AIML from Imarticus offer this opportunity by options of projections from various industries, including marketing and social media marketing. 

To find the most suitable customers for any [rduct, one must find out who is interested, what their age groups and other minute factors. What helps with this finding is ML and the model projected by it. It helps narrow down the target and then goes and finds the best influencers in this field. 

An example of one such project with Imarticus certification course is Marketing Classification that finds out about the interests of teenagers to help with product marketing. Someone having experience in such a task can help find the right targets and market the products better. 

What is the result?

The result of using AI, Machine learning, NLP and other tools here will 

  • Help create a framework that helps reach the target audience
  • Identify those microblogging content creators for each marketing segment
  • Can identify the influencers who are willing to be on board for a long term
  • Helps create a constant digital presence with minimal expense 
  • Create a workflow plan to make the process easier.

Choosing the right course

Artificial Intelligence is applied in almost all industries. Each industry will be using it for the various departments and each department will be using it for different purposes. AI is not a single technology but one that uses various technologies to come up with a working model. The best course in AI will be covering multiple fields and having the expertise of the leaders of the industry.

This Imarticus course Certification In Artificial Intelligence & Machine Learning is conducted by IIT Guwahati. It not only covers a wider and relevant field in AI and ML but also conducts various discussions, exercises, projects, and assessments with the help of experts and mentors. This is one course that will help you be updated with the latest technologies. The Capstone project will help boost your knowledge and experience in the required field. 

Conclusion

Having someone with an Artificial Intelligence certification can help a company assess the various aspects of digital marketing and fine-tune the process by eliminating unnecessary segments and diverting attention to the most useful ideas and their implementation. Influencer marketing is one such advantageous segment of digital marketing that helps improve engagement rate and content quality. It has helped the leading brands in achieving their goals quickly and accurately. 

The fourth Industrial Revolution: a primer on computer vision tutorial

Posted on by Imarticus Learning

The fourth Industrial Revolution is upon us, and it’s bringing a new wave of technological innovations. This post will explore the basics of computer vision, one of the most exciting technologies to come out in recent years.

It’s a branch of artificial intelligence that understands scenes from images or videos. With computer vision, you can quickly identify objects in pictures and recognize what is happening at different locations by looking at them! You can use it for applications such as face recognition, navigation assistance, and many more!

What is computer vision, and what are its applications?

Computer Vision = Artificial Intelligence + Machine Learning

AI is particularly interested in solving problems by building machines capable of intelligent behavior, learning from data, and taking action based on what they’ve learned. And machine learning is a subset of artificial intelligence concerned with the design and development of algorithms that can access data.

Computer vision tutorial is a field in which computer scientists apply their knowledge of imaging, mathematics, physics, engineering, visual perception, and computing to develop methods and algorithms so computers can visually understand scenes. It involves extracting information from a single image or a sequence of images.

From an engineering perspective, you can apply computer vision tutorials to understand and analyze areas such as video surveillance, medical imaging, document management/image retrieval, automatic facial recognition systems for security, etc. You can use it in autonomous vehicles. A fundamental component enables a car to understand its surroundings and make intelligent navigation decisions.

Applications of computer vision:

Automatic Facial Recognition

One of the most common computer vision applications is automatic facial recognition. An image of a person’s face is captured and then used to identify that person from a database of images. You can use this application for security purposes.

Video Surveillance

You can use computer vision to monitor and capture events occurring in video surveillance automatically. You can find this application at airports, casinos/gambling venues, shopping centers, and other places of interest where security and safety are concerned (e.g., amusement parks).

Automotive

Computer vision is used in automotive applications to help the car avoid obstacles and driver assistance systems. Driving a vehicle without using computer vision would be virtually impossible, given that there are just too many visual variables for a person to take into account at any one time. 

Medical Imaging

Computer vision is used in medical imaging to help doctors diagnose and treat patients. You can use computer vision to automatically identify lesions on the skin or tumors inside the body.

Explore and Learn AI Deep Learning with Imarticus Learning

This intensive course will prepare students for a data scientist, Data Analyst, Machine Learning Engineer, and AI Engineer. Students can now utilize our real-world projects from a variety of sectors. This course will assist students in gaining access to attractive professional prospects in the disciplines of Artificial Intelligence and Machine Learning.

Course Benefit For Learner: 

  • Learn machine learning skills by participating in 25 in-class real-world projects and case studies from business partners. 
  • This AIML course will provide students with a strong understanding of data analytics and machine learning fundamentals and introduce some popular tools used by professionals today. 
  • Students can now take advantage of our Expert Mentorship program to learn about Artificial Intelligence and Machine Learning in a practical setting. 

What is Supervised learning?

Posted on by Imarticus Learning

Supervised Learning is a machine learning method that makes predictions based on input data. It’s one of the most popular methods for predictive analytics because you can use it to make accurate predictions and analyze trends in the data. This blog post will discuss supervised Learning and how it can help you improve your business!

What do you mean by supervised Learning?

In simple terms, it is a standard machine learning algorithm that uses labeled training data to predict the output. Supervised Learning applies predictive modeling techniques on large datasets/data streams to find patterns and relationships between features, which you can use for building accurate models.

Supervised learning algorithms are a common way to make predictions when there is data on both the input and output sides. The algorithm will learn to map the input variables to the desired output variable by using a training set of example data. You can use supervised learning algorithms in various industries and applications. 

How does it work?

Supervised Learning is an algorithm that can learn from data with answers labeled correctly. The algorithm consists of training data with several input values (x) and the corresponding desired output value (y). It then predicts the output for new inputs.

You can use supervised learning algorithms for a wide range of tasks, such as:

  • Classification: Determining the type of object an image contains, such as a cat or a dog.
  • Regression: Predicting a value, such as the price of a house or the number of calories in food.
  • Clustering: Grouping data into clusters based on similarities.

There are many different supervised learning algorithms, each with strengths and weaknesses. Popular ones include linear regression, logistic regression, support vector machines, and neural networks. Choosing the correct algorithm for your task is essential for achieving good results.

Why should you use supervised Learning to train your models?

Supervised Learning is a machine-learning method that enables us to obtain the parameters of an algorithm from labeled training data. We have a set of input and output pairs with known labels. The goal is to learn from these examples to correctly map new inputs onto their correct outputs when given previously unseen instances.

The most common example of a supervised learning problem is the classification task that labels our data with more classes. In this case, samples typically get drawn from labeled training sets, and each label corresponds to a class (or multiple disjoint classes). The critical point is that tags associated with different inputs must be read-only (immutable).

Discover AIML certification with Imarticus Learning

This Machine learning course will give students a solid grounding in the practical applications of data science by teaching them how to use these skills to solve real-world problems. This program is for graduates and early-career professionals who want to further their careers in Data Science and Analytics, the most in-demand job skill.

Course Benefit For Learner: 

  • Learn machine learning skills by participating in 25 in-class real-world projects and case studies from business partners. 
  • This machine learning course will provide students with a strong understanding of data analytics and machine learning fundamentals and introduce some popular tools used by professionals today. 
  • Impress employers & showcase skills with AIML course recognized by India’s prestigious academic collaborations.

Here’s why music created by AI is better than you think

Posted on by Imarticus Learning

Artificial Intelligence or AI is capable of carrying out tasks that are much more advanced than just arranging words to generate lyrics. AI already has the ability to offer an immersive listening experience by adapting to a user’s preference. As seen in Spotify and Apple Music for a long time, AI systems understand the user’s preference and recommend songs that the user will enjoy.

AI has gone a step further and now is also able to compose completely personalized music for users. AI can understand certain benchmarks such as harmony, structure, and balance, using which, AI models can generate songs or background music based on the input provided by the user.

Is AI Capable of Creating Better Music Than Humans?

If AI is able to compose music without human supervision, people who need background tracks or copyright-free songs might not need music producers or artists as much as they currently do. Purchasing AI-created music also is easier as there are no royalties while the music generation process would be faster and available on demand.

Yes, with vast amounts of data and training, AI can help in creating a very capable autonomous music generation system, however, it will still be relying on historic data and other pieces of music in order to generate future songs. But, due to the vast amount of data available, the probabilities are limitless and if taught to truly identify good music, AI can become capable of generating hit songs one after another using the very same data.

Even coming up with new songs are just mathematical likelihoods for AI and by analyzing enough combinations, AI is bound to come up with good music. Similarly, meaningful lyrics can also be generated with Natural Language Processing or NLP. However, it will take a while till AI systems become as sensitive to the context of lyrics and innovative in using musical notes.

How AI is Helping in Creating Music?

Even though completely AI-generated music has not reached the Billboards Top 10 yet, services such as AIVA uses AI and Deep Learning models for composing soundtracks and music for users. This helps both small content creators and mainstream celebrities generate music for YouTube, Tik Tok, Twitch or Instagram. This is a cheaper alternative as well. Amper is another great online tool for content creators and non-musicians to make royalty-free music based on their own preferences and parameters. Amper has been created by the music composers who are behind creating the soundtrack for movies such as ‘The Dark Knight’. 

Alex the Kid is a UK-based Grammy-nominated music producer who has used ‘heartbreak’ as a theme and with the help of Machine Learning (ML) and Analytics, has created the hit song ‘Not Easy’. The song even features celebrity music artists such as Wiz Khalifa, Sam Harris, and Elle King.

The hit song had reached the 4th rank in iTunes’ ‘Hot Tracks’ chart within 2 days of its release. Alex used IBM Watson for analyzing billboard songs of the last 5 years as well as cultural and socially relevant content, scripts, or artifacts in order for including references to these elements within the song. Then, the producer used Watson BEAT, the ML-driven music generation algorithm powering the cognitive system for coming up with various musical backgrounds till he found the most suitable combination. 

Conclusion

Artificial intelligence and Machine learning courses can definitely help one learn AI topics for getting involved in interesting projects such as those mentioned above. A Machine Learning and Artificial Intelligence course, such as one offered by Imarticus, are essential for building AI systems such as soundtrack generators or lyrics generators. 

How Long-Term Modelling of Our Future Energy System Can Be Mapped With Artificial Intelligence and Machine Learning?

Posted on by Imarticus Learning

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

ai and ml courses by E&ICT Academy, IIT GuwahatiHowever, such a transformation requires the support of technology and, because of the enormous amount of data, artificial intelligence and machine learning courses are the basis to ensure the advancement of the energy sector.

At Imarticus you can join the postgraduate program in data analytics & machine learning (AIML). 

Technology as a tool

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

Artificial Intelligence allows us to handle enormous quantities and analyze them logically and reasonably. About energy, in particular, we have data on meteorology, health, or the behavior of the people involved in the system: who generates electricity, who transports and distributes it, and who consumes it. Data that, when properly analyzed, can provide a tailor-made understanding of the sector.

The development and implementation of intelligent systems must not only facilitate the massive introduction of alternative energy sources but will also have the task of achieving rationalized storage of this energy, as well as providing greater flexibility for the demand, i.e. the people who use it.

Three levels of analytics can be applied: descriptive, to know what information is available and where to apply intelligence, predictive analytics, to anticipate production or demand, and prescriptive analytics. With the data, we work on predicting production, including renewable energies and demand, with the implementation of smart meters.

In addition, technical and non-technical incidents, such as energy fraud, are detected. All of this is aimed at optimizing the energy model, with the resulting economic and environmental benefits. We will see a huge take-off in the number of professionals who will choose to pursue a machine learning career.

Tools for the consumer

In this scenario, smart meters and internet-enabled sensors will be commonplace, which will improve our energy use while at the same time making it possible to bring costs in line with what each individual actually consumes. Thus, machine learning will automate processes, while artificial intelligence will make it possible for devices to work automatically and learn from consumers’ habits. This will also be possible on a large scale, so that the operation of future solar or wind power plants, to give just two examples, will be more effective in a shorter space of time.

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

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