Keras is a high-level deep learning framework that provides an easy-to-use interface for building, training, and deploying deep learning models. It is written in Python and can run on top of popular deep learning backends like TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). Keras is designed with user-friendliness, modularity, and extensibility in mind, making it a go-to tool for researchers, engineers, and developers exploring artificial intelligence and machine learning.

What is Keras?

Keras simplifies building deep learning models by abstracting the complexities of backend computations. It allows users to focus more on designing and iterating models rather than dealing with the low-level details of tensor operations. Some of its key characteristics include:

• User-Friendly: Offers a clean and intuitive API for fast prototyping.
• Extensibility: Supports custom layers, metrics, and loss functions.
• Cross-Platform Compatibility: Runs seamlessly on CPUs, GPUs, and TPUs.
• Wide Adoption: Used in academic research, industrial applications, and startups worldwide.

Top 10 Use Cases of Keras

1. Image Classification: Keras is widely used for training models that classify images into categories, such as identifying objects in pictures or detecting facial expressions.
2. Natural Language Processing (NLP): Used for tasks like text classification, sentiment analysis, and language translation.
3. Speech Recognition: Enables the development of speech-to-text systems and voice assistants.
4. Recommendation Systems: Powers personalized recommendations, such as those used by e-commerce and streaming platforms.
5. Healthcare Applications: Assists in medical imaging diagnostics, such as detecting anomalies in X-rays or MRIs.
6. Anomaly Detection: Detects fraudulent activities, such as credit card fraud, and irregular patterns in financial transactions.
7. Time Series Analysis: Used for forecasting trends, stock prices, and weather patterns.
8. Autonomous Driving: Facilitates perception and decision-making systems for self-driving cars.
9. Generative Models: Enables the creation of realistic images, videos, and music using GANs (Generative Adversarial Networks).
10. Robotics: Helps robots learn motor skills, object recognition, and navigation.

What are the Features of Keras?

1. Modularity: Components like layers, loss functions, and optimizers are fully modular and easily configurable.
2. Pretrained Models: Provides access to a library of pretrained models such as VGG, ResNet, and Inception for transfer learning.
3. Multiple Backend Support: Works with TensorFlow, Theano, or CNTK for flexibility in deployment.
4. Extensive Documentation: Offers detailed guides and examples for beginners and advanced users.
5. Customizability: Supports building custom neural network architectures and layers.
6. Built-in Support for GPUs: Accelerates model training by utilizing GPU hardware.
7. Integration with Other Libraries: Compatible with NumPy, Pandas, and Matplotlib for preprocessing and visualization.
8. Easy Debugging: Errors are reported with clear and helpful messages, simplifying the debugging process.

How Keras Works and Its Architecture

Keras provides a high-level abstraction for building neural networks. Its architecture consists of the following key components:

1. Models:
   • Sequential API: Allows building models layer-by-layer.
   • Functional API: Facilitates creating complex models with multiple inputs and outputs.
2. Layers: Layers are the building blocks of Keras models and can be stacked to create a neural network.
3. Backend Engine: Keras uses a backend engine (e.g., TensorFlow) to perform numerical computations.
4. Loss Functions: Specifies the objective that the model should optimize.
5. Optimizers: Algorithms like SGD, Adam, and RMSProp adjust model weights to minimize the loss function.
6. Metrics: Evaluate the model’s performance during training and testing.

How to Install Keras

To install Keras, follow these steps:

1.Install a Backend Framework: Keras requires a backend engine. You can install TensorFlow, JAX, or PyTorch. For example, to install TensorFlow, use:

pip install tensorflow

2.Install Keras: Once the backend is installed, install Keras using pip:

pip install keras

3. Verify Installation: To confirm successful installation, run the following in Python:

import keras
print(keras.__version__)

This should display the installed Keras version without errors.

Basic Tutorials of Keras: Getting Started

To get started with Keras, consider the following steps:

1.Import Necessary Libraries:

import keras
from keras.models import Sequential
from keras.layers import Dense

2.Load and Preprocess Data: Keras provides utilities to load datasets like MNIST. Preprocess the data by normalizing and reshaping as required.

3. Build the Model:

model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(input_dim,)))
model.add(Dense(10, activation='softmax'))

4. Compile the Model: Specify the optimizer, loss function, and metrics:

model.compile(optimizer='adam', loss='sparse

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In today’s competitive job market, having the right certifications can give you a significant edge over other candidates. The top 10 certifications in the world are those that are in high demand and offer a good return on investment. They can help you advance your career, earn a higher salary, and gain access to more job opportunities.

Here are the top 10 certifications in the world:

1. DevOps Certified Professional (DCP): A certification that validates an individual’s knowledge and skills in implementing DevOps practices and principles, including collaboration, automation, and continuous integration/continuous delivery (CI/CD).

2. DevSecOps Certified Professional (DSOCP): This certification focuses on integrating security practices and principles into the DevOps workflow. It covers areas such as vulnerability management, threat modeling, and secure coding.

3. Site Reliability Engineering Certified Professional (SRECP): SRECP certification validates an individual’s expertise in implementing the principles and practices of Site Reliability Engineering (SRE). SRE focuses on achieving reliability and operational stability of systems through a combination of software engineering and operations.

4. AIOps Certified Professional: This certification pertains to the implementation of Artificial Intelligence for IT Operations (AIOps). It encompasses using AI and machine learning techniques to automate and enhance various IT operations processes, such as monitoring, event correlation, and incident management.

5. DataOps Certified Professional: DataOps certification focuses on the principles and practices of DataOps, which involves streamlining and optimizing the entire data lifecycle, from data ingestion to analytics and insights. It emphasizes collaboration, automation, and data quality.

6. MLOps Certified Professional: MLOps certification covers the implementation of Machine Learning Operations (MLOps) practices, which aim to streamline and optimize the deployment and management of machine learning models within the software development lifecycle.

7. BizDevOps Certified Professional: BizDevOps certification combines business (biz), development (dev), and operations (ops) practices, emphasizing cross-functional collaboration and alignment. It validates an individual’s ability to integrate business requirements into the DevOps workflow.

8. CloudOps Certified Professional: This certification focuses on the implementation of cloud operations practices and principles. It covers topics such as cloud architecture, deployment, scalability, and cost optimization.

9. ITOps Certified Professional: ITOps certification validates expertise in traditional IT operations practices and principles. It encompasses areas such as IT infrastructure management, system administration, and incident response.

10. SysOps Certified Professional: SysOps certification focuses on system administration and operations within the context of cloud computing environments. It covers topics such as resource provisioning, monitoring, and troubleshooting in cloud-based systems.

In conclusion, certifications have become an essential aspect of advancing in one’s career. The article highlights the top 10 certifications that span various domains, greatly augmenting your professional opportunities. Whether your interests lie in DevOps, security, AI, data, or project management, there’s a certification that can distinguish you from others. Seize the opportunity and invest in your future by embarking on the pursuit of one of these certifications today!

To gain further insights into these certifications and initiate your path towards professional excellence, visit the DevOps School Certification Page. Discover the array of courses, resources, and prospects that these certifications provide.

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IT certifications have always been playing a vital role in getting a job or required knowledge.

In an interview, if you have a certification, you have more advantages to get the job and I have experienced it personally.

There are lots of other channels as well to learn or to enhance the knowledge and skills these days but the thing which matters a lot is the certification, and no one can give a certified degree instead of an institute, and like the way things are evolving the demand of certification is getting increased as they need an expert for their work.

So having knowledge before going to ask for the job is much beneficial to you.

So today I am going to share the top 10 high-paying IT certifications in the world in 2022. So let’s begin.

Master in DevOps engineering (MDE) certification – This certification gives you entire information about DevOps and their related toolsets.

DevOps is just a process to be followed to achieve a high quality of software by continuous integration and continuous delivery, and their open-source tools help it to achieve the goal efficiently and effectively.

Basically, DevOps only direct the way but the major works are done by these toolsets and you can have the proper knowledge and skills by only getting trained in any institute and have the completion certification.

The demand for certification is getting higher to get a good job role in the IT sector.

DevOps is liable to do the planning, designing, coding, testing, deploying, and monitoring.

As DevOps has shown its capability the salary of candidates will be more in 2022 and will be continued.

Site reliability engineering (SRE) certification – SRE is also one of the important certifications. SRE is mainly focused on operations where the goal of SRE is to improve the reliability of software systems, through automation and continuous integration and delivery.

SRE has also open-source toolsets that cover during the certification. SRE has shown tremendous growth till now and getting used all over the world.

It’s expecting the demand of SRE would be consistent and will be on a high-paying salary list.

SRE is for those software engineers who want to work as an operation team.

DevSecOps certified professional certification – It had been forecasted to be achieved a growth of 33.7% during the period of 2017-2023. And even it has been seen the growth in the market.

So as per the result, it will dominate the market in 2022 as well.

The national average salary for a Devsecops Engineer is Rs 10,00,000 in India.

DevSecOps course is for security professionals who are willing to work in the security field like cyber security.

DevSecOp’s assumption is security is everyone’s priority and everyone should work by keeping security concerns in mind. DevSecOps also works in the collaboration with DevOps.

Docker Certified Associate (DCA) certification – Docker is a containerization tool that creates containers and allows to build, test and deploy applications.

This certification helps to learn how Docker is used to package and ship the app as well as how to create containers and so many things.

Docker has become the number 1 choice of all companies and its demand is high.

The average salary of Docker candidates in India is Rs 4,79, 074 to Rs 8,14,070, and in the USA $1,45000.

Being a Docker certified candidate is much important to get a job.

Certified Kubernetes Administrator (CKA) Certification – It has been seen CKA course is at the top to get the certification into. Kubernetes are much important to organize the containers. So Kubernetes certification is important as here you will learn so many things and most significantly how to integrate with Docker to work with.

Kubernetes has already shown its growth as it is in demand at all companies.

Kubernetes candidates can earn salaries up to 6 to 8 lakh in India and in USA between $92,500 and $147,500 per year as per a new report.

AIOps Certified Professional (AIOCP) certification – AIOps stands for artificial intelligence for operations drive automation to solve the issues by speed analyzing the root cause of the issue and taking care of the events with any human interruption.

AIOps is now trending to market and is achieving heights of success. So the growth of AIOps is getting really good and opening so many job roles in AI.

AIOps certification is very important to get into this job domain as certification can grow your chances more to pass the interview and to get the full knowledge.

Based on research the average salary of AIOps is 21 lakh per annum in India.

Master in artificial intelligence – The AI is future and there is no doubt the candidates who are trying to achieve mastery in AI studies have a great future.

Certification is playing a key role here to get your foot into the AI world.

Having good knowledge and the advantage to get the priority in an interview is not so bad. This is the advantage of certifications.

The average salary of AI in the USA is $164, 769 and in India Rs 9,01,800 per annum.

AI is the main driver of emerging technologies like big data, robotics, and IoT.

GitOps certification – GitOps is a set of practices to manage infrastructure and application configurations using Git.

Gitops uses Git as the main repository for managing all the information, documentation. It maintains infrastructure as code and keeps them too in Git.

Some developers believe Gitops is the future of DevOps that replace the Developer part with a single repository that grasps all the information needed by a developer. 

That’s why GitOps certification is important.

GitOps employee’s salary is also high according to experience. One candidate has 45 lakh per annum.

The job openings are also in good numbers to apply.

MlOps certification – Mlops is communication between data scientists and the operation or production team, it is deeply collaborative in nature, designed to eliminate waste automate as much as possible, and produce richer and consistent insights through machine learning.

Mlops is the major function of machine learning engineering.

Mlops Goals –

• faster experimentation and model development
• faster deployment of the updated model into production
• Quality assurance.

The salary for an MLOps Engineer in India is approx Rs 11,40,000 per annum.

It has been predicted to be more job openings in 2022 and the certification is a must to get the job as certification can give you an advantage during an interview.

Its shows at least you have such knowledge pertaining to this and you are trained.

DataOps certification – As per Andy Palmer “DataOps is a data management method that emphasizes communication, collaboration, integration, automation, and measurement of cooperation between data engineers, data scientists, and other data professionals”.

The aim of DataOps is to quickly deliver business value from data.

The DatOps engineer’s salary in India is Rs 7,78,290 and  $92,468 in the United States per annum.

It is predicted, to improve data quality and reduce time to insight, enterprises will increasingly embrace DataOps practices across the data life cycle in 2022.

The certification will play a vital role here to get the job as Dataops is new and also it has so many scenarios to cover so certification is a must.

And it has always been seen certifications always give an advantage during an interview. That means certification increase the status of your knowledge as well as your resume.

                      Training Place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, SRE, AIOps, MLOps, GitOps, AI, and Machine learning courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

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Recently, Google AI has announced that the use of a machine learning system for rapid training to game-playing agents. This framework can be used by game developers to release human game-playing agents, who can efficiently focus on other priority duties to boost productivity. Google AI will provide an open-source library to show the techniques used in this practice. Google AI wants to harness machine learning algorithms for designers to balance their games, artists to generate assets of top-notch quality within a short period of time. The models can also be used to train challenging opponents to compete at the highest level of any game efficiently.

Traditionally, game developers leverage machine learning algorithms for direct access to the source, the uniquely interactive nature of video games, and many more functionalities. But, in the current scenario, Google AI has launched a machine learning system that can be used for rapid training to game-playing agents and seeking serious bugs instantly. The modern solution can work on some popular game genres and generate game actions from a game state within one hour on a single game. Google AI is determined to produce a machine learning system that can only play the game for the game developers while detecting and fixing bugs automatically.

This updated machine learning system can allow game developers to train multiple game-playing agents instead of one super-effective agent with a single end-to-end machine learning model. Game developers experienced a most fundamental barrier in implementing machine learning to computer games- bridging the gap between the simulation-centric world of games and the data-centric world of machine learning. The current machine learning system provides efficient as well as game-developer-friendly APIs with a Dagger-inspired interactive training flow to develop user-friendly video games by describing what a player perceives and the semantic actions related to it like joysticks, 3D objects, 3D locations, buttons, and many more.

Google AI is ready to provide an open-source library for game developers with no prior knowledge of machine learning while the training of game-playing agents can be finished within an hour on a single developer machine. The reason is the effective performance of imitation learning that teaches machine learning models by observing the behaviors of professional players in the games. The imitation model is inspired by the DAgger algorithm that allows taking an advantage of the interactivity of video games.

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Data annotation is the process of labeling data to make it easy for machines to access it.

Why did humans start making machines? The immediate answer would be to make a mechanical and computerised model that works like humans. Yes, humans wanted machines to imitate whatever they do. The purpose of artificial intelligence is no different. If we look at the things that artificial intelligence-powered machines are doing for us today, most of them try to minimize our work by taking over the routine, time-consuming jobs. In order to make machine learning models advanced, they should be trained with datasets. That is where data annotation makes its debut.

Artificial intelligence and machine learning have changed the way we live. Starting from product recommendations and search engine results to self-driving cars and autonomous drones, everything is powered by artificial intelligence. However, this would be impossible without data annotation. Today, we are building a future where automation and autonomous-powered working is everything. To create such automated applications and machines, the datasets need to be trained properly. However, since the datasets are very huge and the human mode of training won’t help, artificial intelligence companies use data annotation to label the content and use it for machine learning models’ training. By implying data annotation, machine learning models get to be fed with well trained and labelled datasets. In this article, we take you through the basics of data annotation, explain its types, and list the use cases.

• DATA ANNOTATION – OUTSOURCING V/S IN-HOUSE – ROI AND BENEFITS
• A GUIDE TO MACHINE LEARNING: EVERYTHING YOU NEED TO KNOW
• OPERATE MACHINE LEARNING IN MS EXCEL WITHOUT A SINGLE LINE OF CODE

What is data annotation?

In simple terms, data annotation is the process of labelling data to make it easy for machines to access it. Data annotation is specifically important for supervised machine learning as the models rely on labelled datasets to process, understand, and learn from input patterns to arrive at desired outputs.

Data comes in various forms like text, image, video, documents, etc. But such diverse types can’t be fed into a machine learning model without segregating and sorting it according to their varieties. Therefore, data annotation acts as an intermediary tool to mitigate training issues. By using data annotation, companies can train their machine learning models with the right tools and techniques. In a machine learning model, data annotation takes place before the information gets fed to a system. The process is similar to how we teach kids. For example, in order to teach them about a ball, we either show the picture or a real ball. Similarly, data annotation labels the object as ‘ball’ in the dataset and feeds it to the machine learning model. Some of the uses of data annotation are listed as follows,

• While using annotated data to train a machine learning model, the accuracy of its mechanism will be higher.
• Machine learning models trained with annotated data leverages a seamless experience for end-users.
• Even virtual assistants or chatbots use the trained dataset to answer users’ queries.
• In search engine recommendation, a machine learning model trained with annotated data provides comprehensive results.
• Besides helping on large scale, data annotation can help with localized labelling based on geolocations. It locally labels information, images, and other content.

What is human-annotated data?

Despite the sophistication technology is enjoying, they will be nothing without humans help. It is no different while training a machine learning model. Human help big time in making machines learn about the way the world functions. Therefore, data annotation loops humans in the training process to improve performance.

But why is human-annotated data important in machine learning? Humans have a special talent called judgement and hunch, which machines don’t possess. The recent developments in the technology industry are pointing to developing machines that can think like humans. That is where human-annotated data comes into the picture. Human-annotated data introduces subjectivity, intent, and clarification, making machines determine whether a search result is relevant.

Types of data annotation

Text annotation: Today, most companies are moving to automatic models, especially, text-based to power their working system. Owing to the increasing adoption, text annotation has become the centre of attention recently. Text annotation includes a wide variety of annotations like sentiment, intent, and query.

Video annotation: When it comes to video annotation, humans are seen as a good source to train the datasets. For example, companies use human assistance in search engine results. They collect the input from many people in terms of their preferences and promote similar content to others.

Image annotation: Image annotation is very important in training a dataset. Many technologies including computer vision, robotic vision, facial recognition, etc. rely on image annotation to label and interpret image forms. To train the models with image data, metadata must be assigned to the images in form of identifiers, captions, or keywords.

Audio annotation: Audio annotation is quite different from the other types of annotation. Unlike others, audio annotation takes an in-depth step to transcribe and time-stamp the speech data, including transcription of specific pronunciation and intonation.

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Training an Artificial Intelligence model is similar to teaching a child

Artificial Intelligence has changed our lives for better. Be it in the form of robots, automated cars, or voice based applications like Alexa and Siri, we have seen it all. Without a doubt, AI is that one technology that makes the best use of human intelligence to take up tasks that earlier could only be performed by humans. Machines now stand the potential to learn and put the knowledge gained in the best possible use. All the human-like tasks are now performed using AI.

There are several aspects to Artificial Intelligence and so are the fields within this splendid technology. Some of them that have successfully garnered attention and appreciation equally from every corner of the world are natural language processing (NLP), computer vision, and deep learning. Machine learning is that sub field of deep learning that mainly revolves around analysing data and making predictions out of the analysed data. Needless to say, all this relies heavily on human supervision.

SMU Assistant Professor of Information Systems, Sun Qianru, talks about how training an Artificial Intelligence model has so much in similarity to that of how parents teach their child to identify objects.

AI and its complexity

Considering the complexity that Artificial I is associated with, Professor Sun’s research mainly talks about –

• Meta learning

• Semi-supervised learning

• Deep convolutional neural networks

• Incremental learning

Well, not just that. The research also revolves around the application of all of these in recognizing images and videos.

The research, “Fast-Adapted Neural Networks (FANN) for Advanced AI Systems” is currently in its early stage. The research revolves around computer vision. This aspect of computer vision employs algorithms that rely on CNNs (Convolutional neural networks). The areas under scrutiny are image recognition, image processing, etc. All of this work is funded by the Agency for Science, Technology and Research (A*STAR).

Building the reasoning level of model adaptation based on statistical-level knowledge learning is the hypothesis of FANN. Here’s everything that the research talks about –

• Knowing the fact as to how complex AI is, Sun’s research talks about how critical it is to train AI model that is in line with the current trends in the field.

• When a model is trained to yield accurate recognition results, the amount of data that goes in is immense. Sun cites an example of face recognition to support this. She argues that if there’s just one face available for the system to recognize, then how will it be possible for it to differentiate that one face from the rest? Only when adequate amount of data comes into play, only when other faces too are employed for face recognition should the model be successful in distinguishing. To learn the differences, the model should have huge data that it can rely on.

• All said and noted, the fact that machine learning models stand the potential to identify the global features cannot be overlooked. These models encode the data available that help in producing desired identification results. The models are successful in recognizing from images, text or sound. All of these employ deep neural network architectures that contain many layers.

• Sun’s research takes into account two main aspects. One is where some machine learning models train themselves on a labelled data set. The other being how the best performing AI models are all based on deep learning. The research addresses the point how models are built to determine the data followed by classifying it.

• The professor talks about how some models get updated when the prediction made turns out to be wrong.

• There’s yet another project that Sun is working on. It is a food related application for the Health Promotion Board based out of Singapore. The main idea behind this app is to enable the users have a fair knowledge about the nutritional values of the food that they consume. The users can make use of this information to lead a healthy lifestyle. All that the users have to do is take pictures of the food they’re consuming and that’s it. All the relevant information is out there – on their smartphones.

• However, this is where the complexity began. While training a model, her team had introduced a limited set of categories into it. But, with different photos being clicked, the need of expanding the categories came into play. Not just this, the category list was required to be updated and modified in the Application Programming Interface (API) on a regular basis.

• The rich diversity that the place brings in posed a challenge for the team. With a different place, comes in a different culture. Hence, the team needs to pay extra attention to train their models by employing effective learning algorithms.

• All this calls for not only diverse data collection but also on developing different adaptation learning algorithms. The complexity is for sure in existence and the team plans to deal with this by making use of a small data set.

This research by Sun and her team aims to achieve high robustness and computational efficiency, especially in the image recognition aspect. The research team is confident the outcomes of the research will have tons of benefits to offer. The key ones being great improvement in the yield rate and reduction in the manufacturing costs. All this would play a pivotal role when the fast-adapted inspection devices undergoes the process of installation, fabrication, and testing.

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The world of data science and machine learning is a fascinating one, both for the incredible innovations and for how deeply they affect everyday happenings around us. Businesses employ and basically rely on these methodologies to help them analyze their data and make decisions that directly affect their bottom line. Data scientists can spot trends in all kinds of information, even pinpointing commonalities among successful writers. 

It’s an awesome job, no doubt, and one that has a bright future ahead. If you’re even remotely interested in the industry, check out the Machine Learning & Data Science Certification Training Bundle. It’s the perfect way to master important concepts and either start or strengthen your position in the field – and you can now get this course pack for 97% off. It’s safe to say $35 is a steal for eight courses covering Python, R, TensorFlow, Keras and more. 

Consider this your guide to machine and deep learning, with an intro using Google’s TensorFlow and Keras frameworks in Python. Get all the intel you need to understand the basics of NumPy and Pandas (which will be something you’ll actually understand after taking these courses). As you work through the courses, you’ll learn how to create artificial neural networks and data visualizations – pretty impressive skills for someone who’s only taking 48 hours of coursework! 

This highly rated bundle can set you apart in the workforce. Case in point: the class pack includes lessons on mastering R for practical data science. Learn how to assess data from different sources in the R environment, plus, learn the basics of using R Studio. The skills you’ll gain, like learning which algorithms work best for different types of data, could help you boost your company’s performance. 

Whether you’re gunning for a promotion or looking for a new gig, this deal could be a game changer. Right now, you can get the Machine Learning & Data Science Certification Training Bundle for $35, down 97% from the original MSRP.

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Data is the basis of innovation. It’s the main reason why businesses collect pertinent information from consumers, as they could translate to powerful business predictions and decisions. But at the rate companies across the globe is accumulating mounds of raw data, there’s never enough qualified professionals to manage the torrential downpour of information. It’s no surprise data scientists are incredibly in demand, as they have the capacity to transform data into quantifiable pieces of information that can then turn into fact-based decisions.

If you want to capitalize on this demand, there’s no better time than now to harness and sharpen your data analytics and visualization skills. The Dynamic Data Scientist Bundle Ft. Power BI, Python & MATLAB was specifically put together to help you on that front, and for a limited time, you can get it on sale for only $29.99.

Regardless of your experience with organizing, processing, and analyzing data, this 7-part e-learning bundle will impart with you the skills to help make accurate and powerful data-driven predictions for any organization. With instructional content led by experts, you’ll learn straight from top data science specialists things like coding a multitude of data structures, mathematical concepts used in machine learning, creating visualizations and altering them to accommodate various situations, and effective data gathering and visualization techniques to make sense of raw information.

In addition to all these, the bundle also offers courses on using the different tools that data scientists have in their arsenal. You’ll be guided through what Microsoft Power Business Intelligence (BI) is and how it can be leveraged to view and share insights into businesses. You’ll discover MATLAB to figure out how machine learning works and get familiarized with Python to make line and scatter plots, as well as create various types of visualizations.

Future-proof your career and make yourself indispensable by learning data science. The bundle usually retails for $1,393, but for a limited time, you can get it on sale for only $29.99.

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Master the world’s most popular programming languages with this training bundle. You’ll also learn about Raspberry Pi and artificial intelligence.

It’s no secret that learning how to code is one of the most important things you can do if you want to land a high-paying tech job. Whether you’re primarily interested in creating best-selling apps or pro-level websites, you need to be well versed in the programming languages and platforms that are used by today’s most competitive developers.

But if you want to stand out from the crowd and get a great tech job, you need to know more than just one or two programming languages. The Premium Learn to Code 2021 Certification Bundle will get you up to speed with the world’s most popular and powerful programming languages and scripts so you can have a successful career as a developer. The entire training bundle is available for over 95% off at just $59.99 right now.

With 27 courses and 26 hours of instruction led by industry pros, this best-selling bundle is your one-stop resource for mastering Java, MySQL, Swift, and more. There’s also plenty of instruction that walks you through the basics and more advanced elements of Python. In addition, other courses include: How to create apps by utilizing JavaScript’s advanced elements and protocols, integrate comprehensive UIs into your websites using powerful scripting protocols, and work with Ruby to create a wide range of applications for desktop and mobile devices.

Get the skills and tools you need in order to become an in-demand developer this year with help from the Premium Learn to Code 2021 Certification Bundle while it’s available for just $59.99—over 95% off its usual price for a limited time.

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