Are you considering taking a machine learning course to level up your skillset? It is essential to make sure that the course you choose is comprehensive and provides tools that are easy to understand. 

Whether you want an introductory crash course or an in-depth, hands-on course with practical takeaways, there are certain features every good machine learning class should have. 

In this blog post, we’ll examine key elements of a quality machine learning course so you can make sure it fulfills all your educational needs.

The fundamentals of machine learning

First, a great machine learning course should give students an understanding of ML algorithms and techniques fundamentals. This includes supervised and unsupervised learning, hyperparameter tuning, feature engineering, and more. It should also provide an overview of popular ML libraries such as TensorFlow, Scikit-Learn, Keras, PyTorch, and others. A solid foundation in ML basics will ensure students have the knowledge they need to become well-rounded practitioners. 

Projects & Applications 

A good ML course should also include hands-on projects that help students apply their knowledge of ML techniques in real-world scenarios. Projects can consist of image recognition applications using convolutional neural networks or natural language processing tasks using recurrent neural networks. By working through these projects, students can gain valuable experience that they cannot teach in the classroom alone. 

Tools & Resources 

A quality machine learning course should provide access to helpful tools and resources to aid their learning process. For example, many courses offer access to cloud computing platforms such as Google Cloud Platform or Amazon Web Services, which allow students to run experiments on powerful machines with minimal effort. Additionally, some courses even offer access to GPU instances so that students can significantly speed up their training time. All of these tools are invaluable when it comes to mastering ML concepts quickly and efficiently.    

Real-World Examples

An excellent machine learning course should have plenty of real-world examples so students can better understand how their learning concepts apply to everyday life. After all, what good is knowing the theory if you can’t use it in practice? Real-world examples also help students think more creatively about solving problems and give them the confidence to tackle new challenges. 

Interactive Exercises

An excellent machine-learning course should offer interactive exercises that allow students to test their knowledge and understand how different concepts interact. This helps build critical thinking skills and will enable students to learn from mistakes. Plus, these exercises are usually fun, making them more engaging for students than just reading textbooks or watching lectures. 

Course Support

 Students need to know they have support available if they get stuck on a problem or need help understanding something. An excellent machine learning course should provide online and offline access to resources such as tutorials, forums, mentors, and more so that students can get help when they need it most. 

With so many options for machine learning courses, it can take time to decide which is best for you. To make sure you get the most out of your educational experience, take the time to research each course carefully before making your decision. Make sure it covers all the basics and provides hands-on projects and access to helpful tools and resources—then you will know you’ve got yourself a winner!

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Ready to take your career in a new technology-focused direction? Enroll at the E & ICT Academy for their comprehensive IIT AI ML course! Equip yourself with all you need for revolutionary leadership – don’t hesitate, start your journey now!

 Course Benefits For Learners:

• Students work on 25 real-world projects to develop real-world business experience and prepare for a rewarding data science career. 
• With a certificate recognized by the E & ICT Academy, IIT Guwahati, and a credential supported by Imarticus Learning, students, can attract employers’ attention and demonstrate their skills. 
• Students who complete these AI and machine learning courses land lucrative jobs in the machine learning and artificial intelligence sectors.

 Visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad, or get in touch via the chat support system.

Machine Learning is an inseparable part of Data science these days. With the advancement in software development in AI and ML, several advanced and cutting-edge Machine Learning tools have been introduced in the market. 

Due to popular demand, these tools have become easier to access, and every data scientist should avail of this wonderful opportunity. If you are a data scientist pursuing a PG in data analytics, it is even more important that you learn how to use these tools. Learning these tools will also be advantageous for your curricular studies, and you can upgrade your skills. 

Top 8 Machine Learning Tools Every Data Scientist Must Learn

And now, let me share about each of them in greater detail!

1. Python

Python is a widely used programming language and a useful tool in data science to analyse data. It can also be repurposed for machine learning and deep learning. The syntax is also quite easy, and the programming language has rich libraries. 

The community is also very active and responds quickly in case you have an issue. There are also regular boot camps for Python in India. There are many resources where you can learn Python. You can take an online course or read a book to learn the tricks. 

2. NumPy

It is an acronym for Numerical Python. It gives support for multi-dimensional arrays and matrices. This Python library uses C/C++ for its development. The biggest advantage of using NumPy is that it provides all the necessary mathematical support for ML. 

It also uses less memory and has a faster performance when compared to other libraries. It is truly an asset for Data Scientists who use machine learning for projects like random password generators, statistical analysis, calculators or video games.

3. Pandas

It is a data analysis and manipulation library built over the NumPy package’s architecture. It helps to handle tabular data. Pandas are quite flexible, as they can be used with other tools as well. You can effectively use it to build a recommendation system like Netflix or Python. You can also make prediction systems for stocks and neuroscience from this package.

4. Scikit 

It is an open-source ML library for Python. It is built over the architecture of NumPy, SciPy and Matplotlib. Since it is accessible and reusable, it has great flexibility. It can be used for training and testing models using classification, clustering and regression algorithms. 

Apart from making prediction analysis, recommendations systems and automation, it can also be used to develop evaluation and matchmaking systems.

5. Matplotlib

It helps in data visualisation and graphical plotting and is a wonderful OOPs tool that helps make embeddable plotting APIs. Since it can be used cross-platform, it can be integrated with third-party apps. It includes LaTex to develop neuroscience apps, stock price evaluation systems and also for game development.

6. R Programming

R is another famous tool for Data Scientists who are also into machine learning. It is also a good choice of popular programming language highly regarded by statisticians or data scientists. It is also useful for a visual representation of data (with the help of ggplot2). 

R has many packages, making it a perfect scientific research tool. It finds many applications in the healthcare data sector and other fields that heavily use statistics. It is also best suited for data mining.

7. TensorFlow

TensorFlow is quite a robust machine-learning tool developed by Google. As a data scientist, you can use it to build and train machine learning models in a short time. It provides a data automation platform. You can also train and monitor your models using TensorFlow. This tool is extremely useful but only when the hardware is decent enough.

8. Hadoop 

Apache Hadoop is a collection of open-source software platforms that allows data scientists to use a whole computer network to solve Big Data and Computation problems. Hadoop provides a software framework that helps distribute the storage and improves big data processing using the MapReduce programming model. 

Now Hadoop is used to power many cloud storage apps and also powers search engines and social networks. Its community is so widespread that you have multiple resources to learn Hadoop online.

As an aspiring data scientist, it might be challenging to learn these separately. However, there is an option to learn all these tools at once. Imarticus Learning has a good data science course with placement, which covers all these important tools as a part of its curriculum. You will be taught by the best faculty and even be given a job placement after ending this course. What else could you ask for? 

The popularity of artificial intelligence and machine learning has skyrocketed, and finance is one of the main sectors where it is flourishing. The emergence of fintech is a shining example of that. It’s amazing to see that this field, which was not popular at all, has now entered the mainstream and is making a significant difference in almost every domain.

Therefore, machine learning in fintech is also a reality where the potential is immense. So, read on to know more about it.

Machine learning has a lot of potential in fintech

AI and ML have been incorporated into the fintech industry, and it has been revolutionising it with the help of automation. According to the statistics, the investment in AI all across the globe in the fintech sector will be approximately $26.67 billion by the year 2026 with a 23.17% of CAGR.

Here are some instances where ML is making a difference in the financial sector –

• Sales forecasting
• Asset management
• Fraud analysis
• Risk management
• Predicting stock prices and their movements
• Product recommendations
• Personalised services
• Customer support

The intersection of finance and technology helps financial companies match the fast, evolving tech world. Machine learning is what is responsible for the creation of new ways to innovate financial services which are built on a tech platform. Automation has helped fintech to gear up significantly.

Here are some advantages that machine learning is bringing to the fintech scene of India –

• Cost efficiency: Since automation reduces the number of staff required for operation, it lowers the overhead cost of the company.
• Better fraud prevention: An ML system in place will work more efficiently to find the anomalies and perform better to prevent any fraud and other troubles.
• Less bias: Since the system will evolve with the data it will receive, it will generate better results backed by information. Hence, it will be more stable and without any bias.
• Better time management: Completing tasks like analysis, market study, etc., are time-consuming, but with a system like this in place, it will lower the time required for decision-making considerably.
• Better customer engagement: Since the implementation of AI and ML ensures every individual gets a customised solution, it enables fintech companies to offer more tailored products and services. Hence, they can improve customer engagement and serve their clients better.

But how should you go about your career? Follow this fintech roadmap

If you decide to take up fintech as a career, you need a roadmap for it. Otherwise, you may deviate from the career path that you have made for yourself. In the following decades, fintech will dominate the job sector with lakhs of new jobs.

So, this is what we were waiting for. Here is the Fintech roadmap for beginners who wish to enter this field and give themselves the best leverage in their career in this field.

• Graduate with a degree

To begin your innings in the Fintech sector, it is advisable to begin your career with a graduate degree. You can opt for a non-medical or commerce field in your 11th class, and after clearing your 12th class, you could opt for an engineering or commerce degree. If you have opted for Engineering, make sure you supplement your course with a diploma in finance.

• Get the necessary post-graduation qualification

After you have acquired the necessary qualification during your graduation, now you need to go for the course that is necessary for your career. Obtain an MBA in fintech from a reputed university or college like KL university.

Here, you get a chance to learn the intricacies of fintech along with the knowledge of cryptocurrencies and artificial intelligence in fintech and Blockchain. KL university has tied up with Imarticus Learning. Together, they provide the students with the necessary qualifications for a fintech job. You can enrol for the MBA program they are running to secure placement once you finish the course.

• Work in a bank job

Banks are pouring billions into cloud development, digital innovation and fintech acquisitions in the current environment. Since they are shifting to a more digital method, starting your career with a traditional bank before shifting to fintech is not a bad idea.

• Work for a fintech startup

On the other hand, you can decide to go work for a fintech startup to begin your career. This can also be a good move to kick-start your journey in this domain.

To sum up, hopefully, this write-up has shed some light on how machine learning is evolving the fintech sector and what the future will be. And, if you are planning to enter this domain, then completing an MBA in fintech can be of great advantage, as it will shape your future in fintech.

Want to master ML (machine learning) and AI to supercharge your career? Read on

Artificial intelligence (AI) and machine learning (ML) are the two hottest topics in the tech world. Companies like Google, Facebook, and Amazon are all investing heavily in these cutting-edge technologies, and they’re always searching for talented engineers who can help them push the envelope even further. If you’re looking to supercharge your career, mastering ML and AI is a great way to do it. Here’s what you need to know.

What are AI and Machine Learning?

Before we dive into how to master ML and AI, it’s essential to understand what these terms mean. 

Artificial intelligence is a broad field that deals with creating intelligent machines that can react and work like humans. This can be anything from teaching a computer how to play chess to building a self-driving car. 

On the other hand, machine learning (ML) is a subset of AI that allows computers to improve and learn from experience without being explicitly programmed. So instead of programming a computer to do something specific, you feed it data (known as “training data”), and it figures out how to do it. 

For example, if you wanted to build a system that could automatically identify cats in images, you would give it a bunch of images containing cats and images that don’t include cats. The ML algorithm would then learn from this training data and be able to identify cats in new images automatically. 

How to Become an AI/ML Expert?

• Get a degree 

Although you don’t need a degree to get started in ML or AI, having one will give you a solid foundation of knowledge to build upon. Plus, degrees from top schools are highly respected by employers in the tech industry. If you’re unsure which route to take, consider getting a degree focusing on data science or artificial intelligence.

• Take online courses from leading providers. 

There are many online courses available that can teach student’s the basics of machine learning and artificial intelligence. Courses from leading providers are respected by employers and will give you the necessary skills to get started in the field.

• Participate in open-source projects. 

There are several ways to get involved in the machine learning and artificial intelligence community. You can join open-source projects, make contributions, or collaborate with others on their ideas – all of which will give you invaluable experience for your future career!

• Stay up-to-date on the latest developments. 

Machine learning and AI constantly evolve, so staying updated on the latest developments is essential. Attend conferences, read blogs and whitepapers, and follow leaders on social media to ensure you’re always learning new things.

Following these four steps, you can master machine learning and artificial intelligence and embark on a rewarding career. With the right skills, you’ll be in high demand by businesses across all industries that are looking to harness the power of these transformative technologies. So what are you waiting for? Start learning today!

Discover IIT artificial intelligence course with Imarticus Learning

Enroll in the AI and machine learning courses program at the E & ICT Academy to learn more about machine learning.

Course Benefits For Learners:

• Students work on 25 real-world projects as part of the IIT AI ML course to gain practical business experience and prepare for a rewarding data science career. 
• Students can grab employers’ attention and showcase their abilities with a certificate recognized by the E & ICT Academy, IIT Guwahati, and a credential supported by Imarticus Learning. 
• Students who pass this deep learning certification may find lucrative employment in the artificial intelligence and machine learning fields.

Visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad, or get in touch via a chat support system.

The popularity of AI and ML has skyrocketed in recent years, and the domain has seen significant investment to meet its rising demands and take the technology forward. As a result of this, employment opportunities have opened up, and candidates from various fields are now getting into this domain.

In this regard, having an AI certification from any leading institution can be of great help to you, and it will open up new opportunities for you. So, read on to learn more about it.

What are GitHub and Creative-AI?

Talking about the artificial intelligence and machine learning course, there are two tools that you should have a great grasp on, GitHub and Creative-AI. The reason is their popularity and wide range of usability.

GitHub

GitHub is an online repository tool software developers use to store project codes and databases, track the code, and collaborate with other developers on projects. This way, GitHub makes life easier for developers when sharing big files of code, and there is no requirement for zip-packing the files.

On the other hand, in the case of collaborating on open-source projects with fellow developers, it is quite simple and easy. GitHub also serves as a social network for developers to network, meet and collaborate with fellow developers, and pitch their online portfolio to prospective employers or business owners. 

An interesting part of GitHub is the portfolio display feature. GitHub allows developers to host their projects on the platform and see whether the code is in working order. For that, you don’t need to pay and upload your code on paid hosting services.

Creative-AI

It is the simulation of creativity in machines using the help of Generative Adversarial Networks (GANs). It uses the power of AI and ML to create two neural networks that, when pitted against each other, give a nearly-accurate result. In other words, from an original dataset, you can create a new dataset that is almost the same but with a few minor differences. That means you can create new music, images, audio and video output from the existing content input.  

What are the advantages of understanding GitHub?

Since GitHub is an internet hosting service –cum-repository, it has certain advantages in strengthening your resume as a software developer:  

• You can show your coding prowess to others

Since you have been storing your entire code on the platform, it is easier to show employers and developers your ability to read and write code. Machine Learning is all about being able to pitch your ideas in front of people, be it an employer or a stakeholder. Also, it shows that you have the true mindset of a seasoned developer who is willing to improve their code since you have put it on a public platform. If you are a bit open-minded and can accept feedback, then you are one of the best candidates to get hired. Who knows, you might not have to begin at entry level, and you might get a direct entry as a mid-level developer.

• It reflects that you are a team player

More than the programming skills, what is valued in an enterprise is the capability to collaborate. Several developers meet and sit down to ponder on a project and improve the functionality of a piece of software. Artificial Intelligence and Machine Learning are all about collaboration. Organisations value this aspect a lot; therefore, GitHub adds an edge to your resume.

• Most of the Machine Learning projects require open-source code

And that is where GitHub is one of the most helpful tools. Not only are there hordes of repositories which consist of machine-learning algorithms and code from all over the world. Many developers have contributed to it and worked on it to enhance the code. So, you are saved from a lot of work as you might not have to start a machine learning project from scratch. If you start from zero, it is very difficult and time-consuming to create and train an ML model. Also, you get to use the wisdom and skills of talented developers because of open-source projects on GitHub.  

How will Creative-AI benefit you in your career?

It is extremely beneficial for AI and ML developers to possess knowledge of Creative-AI, especially if they are inclined towards fine arts and artistic fields. Music, illustrative arts and video content creation or any other form of media creation and editing requires AI and ML professionals. These professionals need to have Creative AI on their resumes, and it is a must-have. Google, Facebook, and some top gaming companies have many openings for people with tech and art skills in this field.

So, you can see that understanding GitHub and Creative-AI can work wonders in your career as an AI and ML developer. However, the question is where to get the knowledge of these two tools from. In that case, you can consider an IIT artificial intelligence course.

And for that, you can look at the Certificate Program in Data Science and Machine Learning from IIT Roorkee in partnership with Imarticus Learning. It will help you get the required knowledge and ensure that you receive the placement as you move forward.

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Artificial Intelligence And Machine Learning: Top Courses, Skills, Salaries And Careers

Machine Learning Certification– What to Prepare and How to Ace the Exam

Data science is a significant trend in the tech industry. It’s not just about coding anymore. It’s also about learning how to use data and algorithms to create useful applications that solve real-world problems. 

Understanding machine learning, statistics, and programming languages like Python or Java are necessary to succeed as a data scientist. Continue reading if this sounds like something you’d be interested in.

AWS Certified Machine Learning: An overview

AWS Certified Machine Learning Associate helps you develop the skills required to build and deploy ML models. The exam evaluates your knowledge of machine learning concepts, including how they can apply in various cases.

The course’s objective is to verify your expertise in a variety of crucial areas, as defined by AWS as being able to:

• Choose and defend the best ML strategy for a specific business issue.
• Choose the best AWS services to implement machine learning solutions.
• Create and put into practice scalable, economical, dependable, and secure ML solutions.

You will be required to demonstrate this knowledge across six different domains, each accounting for a specific percentage of your final score. These domains get divided into:

• Data Engineering: 20%
• Exploratory Data Analysis: 24%
• Modeling: 36%
• Implementing and using machine learning: 20%

How to prepare?

• Collecting the information

• • Understand the syllabus. Your first step is understanding what you expect to cover in your course and how those topics fit into a broader learning context. 

• Collect information from books, online courses, and forums. The best way to get started on this front is by reading books published specifically for machine learning certification purposes. 

• Learning through AWS Resources

If you are considering taking the AWS Machine Learning Certification exam, there is no better place to start than with the Amazon Web Services (AWS) resources. 

The first step toward passing an exam is preparation. Make sure your study schedule gets packed with practice questions and exercises so that when it comes time for your next test, you won’t be caught off guard by unexpected questions!

• Mock Test 

If you’re looking to prepare for the exam, there are a few valuable resources available. One of the best ways to improve your score is by using mock tests. These practice tests simulate real-world conditions and help you learn how to approach a question.

IIT Roorkee machine learning course is India’s most popular data science course. It is a comprehensive course that covers the entire spectrum of data science, from core concepts to advanced topics.

Explore a machine learning certification course with Imarticus Learning.

With the help of this certificate program in data science and machine learning, students can launch their data science careers. Through this curriculum, students will learn the fundamentals of data science and machine learning and the knowledge necessary to apply these ideas in the real world.

Course Benefits For Learners:

• Students will learn Python in this five-month course, which IIT faculty members created to help them understand data mining and machine learning methodologies.
• This data science certification course will be taught live online by the best instructors in India.
• Our online data science program will help students build a strong foundation in data science.

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

Supervised Learning: The Next Generation of Machine Learning

Machine learning is a powerful tool for solving complex problems. You can use it to predict future outcomes, recognize patterns in data, and extract knowledge from massive datasets. But it requires a lot of data training and time to build models that will work well in your specific context. This blog will explore supervised learning and how you can apply it more effectively in your projects!

A new wave of supervised learning techniques

Supervised learning is a type of machine learning that uses the data provided by an already trained model to make predictions and build new models. You can do this by training a model on the same dataset it will use to make predictions so that you can use your existing data set as input for this step in your workflow.

You can also use supervised learning to build models that perform specific tasks, such as identifying spam emails or detecting fraud before they happen. Supervised learning also makes it easier for developers to add new features to their applications without knowing how those features work internally.

A look at the mathematical foundations of supervised learning

An ML technique called supervised learning makes use of examples to forecast results. It is one of the most basic types of AI, and you can use it to create any computer program, from robot cooks who assist you in the kitchen to self-driving vehicles that can drive through cities at night.

Supervised training allows us to build our models by feeding them with labeled data annotated with specific information about what it means for an image or series of images. 

How to improve your supervised machine-learning models?

The first step is understanding the data you are working with. You should know what it looks like, how it’s structured, and what questions you want to answer.

The second step is understanding your problem: what problem statement do you have in mind? What do you want to accomplish? How will this help someone else solve their problem? 

The third step is understanding your model: what kind of model do we have here (supervised versus unsupervised)? What algorithms does this algorithm use (MLP vs. CNN)? What features does it use (such as images or text)? How many levels deep does it go before making predictions about future events or behavior changes based on past ones (bagging)?

Deep learning and Advanced neural networks 

Deep learning utilizes neural networks with multiple hidden layers—deep learning trains many models, which can take hours or even days to run on high-performance computers. Deep learning algorithms also require more data than traditional supervised models (such as logistic regression).

As a programmer, you need to be willing to learn new things. Being open-minded is essential too! 

Learn Machine Learning and Artificial Intelligence Course with Imarticus Learning

Learn how to become an AI engineer by enrolling in the E & ICT Academy’s deep learning Artificial Intelligence certificate program. 

 Course Benefits For Learners:

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

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

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https://imarticus.org/think-you-cant-master-ai-learn-artificial-intelligence-from-scratch/