The Digital Transformation Conference returns to North America on May 19 at etc.venues 360 Madison welcoming enterprise digital transformation, innovation & strategy leaders sharing their insights on their transformation journey.

The conference welcomes digital business executives from Gap, Prudential, New York Times, BNY Mellon, News Corp, London Stock Exchange Group, Arcadis and more to the stage sharing best practice, lessons learned and business case studies.

The conference for 2022 will have three core focus areas of which will be people, technology & process within enterprise, and in particular, digital transformation looking at best practices, common challenges faced, success stories, the technology, tools & thinking needed to adapt, survive and thrive

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If you are reading this article, you may just be new to Microsoft Azure certifications. Azure certifications are designed for system administrators, developers, and data and AI professionals to validate the skills necessary to cover all aspects of digital transformation. These skills go from managing on-premises, hybrid, or cloud infrastructure, to innovating with high quality applications, and maintaining those apps and the data infrastructure of the organizations. As a candidate you are free to pass the certifications organizing your own training, but it is advisable to follow a Microsoft Azure course to prepare for your certification. These are 4 days long courses that will cover main topics for each certification and train you to answer questions effectively according to the exam format.

How are Microsoft Azure certifications organized?

There are 12 Microsoft certifications available that include 14 exams. Exams are held online and gather between 30 and 60 questions that the candidate will need to answer in 150 to 180 minutes depending on the exam. To pass the certification exam you will need a score of 700/1000 or 70%. Microsoft Azure certifications are organized according to four levels of expertise and relate to professional data roles in a company.

Fundamentals Certifications are aimed at beginners. None of these are required for any higher Azure certifications, but they offer ideal starting points for anyone new to cloud or Azure.

Associate Certifications, if you already know your way around Azure, you may feel comfortable starting here. Then seven Azure Associate exams are open to candidates who have subject matter expertise implementing, managing, and monitoring an organization's Microsoft Azure environment.

Expert Certifications: start here, if you have serious tech skills! You will need an Associate certification (either an Azure Developer Associate or an Azure Administrator Associate) for the Azure DevOps Engineer Expert and the Azure Solution Architect Expert certification.

Specialty Certifications: These are certifications applicable to a particular service that is not from Microsoft. There are several of them including the Azure for SAP Workloads Specialty and Azure IoT Developer Specialty certifications. Speciality Certifications are accessible without having passed other Azure certifications.

Following a Microsoft Azure training, the road to success and certification

Image: Freepik

As we have seen, Microsoft Azure certifications have a particular format that you will need to get used to before sitting an exam. If you want to get prepared and study beforehand, the best option is to discover a Microsoft Azure course that is adapted to you.

Depending on which certification you are planning to pass, this training will help you discover the content of the exam and get used to the exam format to be more efficient when you sit the exam. A training in DP-203 will, for instance, last around 4 days with 8 hours of teaching per day. It starts with an introduction to the Azure cloud computing environment and follows with a module on how Spark works with Azure and how to use Azure Databricks. The course then covers the use of Azure for database management including use of SQL pools and you will discover how to make data stores and pipelines more reliable and secure. Finally you will learn how to monitor and optimize all Azure services to manage data effectively.

Follow a Microsoft Azure course, or self-train with Microsoft Learn

So, in a nutshell, following a Microsoft Azure course will help you increase your knowledge on topics covered in the exam so that you are prepared for all related questions. It will make you more confident in passing through the various levels of validation and certifications that Azure requires from candidates. Microsoft Azure certifications are the most demanded in the IT industry and will allow successful candidates to secure better job positions and higher remuneration in top companies around the world. A good place to start is getting in touch with a Microsoft Learning Partner. They provide you with a selection of training solutions which you can sometimes customize to your career plans and goals. These resources include face to face or visiocall training led by Microsoft Certified Trainers (MCTs). At times, you will also have the chance to apply this new knowledge during autonomous exercises sessions, so you can put yourself in the conditions of the exam.

If you feel more comfortable, learning and practicing on your own, you can also join Microsoft Learn. It is a free online platform provided by Microsoft, that gives you access to a set of training for the acquisition and improvement of digital skills. It offers an interactive and self-paced interface that allows you to code directly into it for practice. Also, you don't need an Azure Subscription to do most of the exercises, since Microsoft Learn has a free practice environment.

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1. Replacing Standardized Testing

Standardized testing requires candidates to answer the same questions. This method of assessment is based on the principles of consistency. The answers are graded in a predetermined way. However, standardized testing has become obsolete over the last few years. This type of testing system does little to benefit students.

Cons of standardized testing include inflexibility, lack of progress, and increased stress levels in students. Top students may not perform well in standardized tests due to several reasons. Another problem associated with standardized testing is that it allows powerful individuals to get away with exams.

Artificial intelligence can help educators get rid of standardized testing. The pandemic has already forced schools to postpone tests. Universities in the U.S. are coming up with ways to replace standardized testing. And AI is one of the alternatives under consideration. AI tools can predict a student's score with near-perfect accuracy.

2. Personalizing Education

AI has several advantages over the traditional learning mechanism. It allows individual students to understand concepts. That means it can personalize learning on an individual level. Think of it as attending to one student at a time as opposed to a teacher taking a class of dozens of students at the same time. Moreover, AI tools can provide lesson plans for individual students.

AI allows educators to create individual test sessions for students. It enables them to micro-assess students throughout the year. This is different from the traditional grading system practiced in schools and universities. The pandemic has already brought up an increased acceptance of technology in education. More and more people are willing to adopt the AI model of learning.

3. Focusing on Weak Areas

AI can adapt to the behavior of users. Suppose you are interacting with an AI on a daily or weekly basis. After some time, the gadget will be able to predict your scores with near-perfect accuracy. Moreover, it will recommend a learning plan based on your strengths and weaknesses. This will allow you to focus on your weak areas.
The methodology used by AI can help teachers assess teachers on a micro-level. They can tell students where they are doing great and where they should put in more effort. This type of encouraging feedback can completely change a school's approach to education. The purpose of AI is to make learning valuable and interactive.

4. Allowing Remote Learning

The pandemic has accelerated unprecedented growth in industries around the world. From schools to restaurants, almost every sector is leveraging tech tools to achieve growth. With social distancing still very much in place, colleges and universities have shifted to remote learning. Students and teachers no longer have to be physically present in classrooms for lectures.

AI allows educators to provide learning via the Internet of Things (IoT) devices such as smartphones, laptops, and tablets. Applications like Google Classroom, Zoom, and Skype provide virtual learning opportunities. Moreover, these platforms enable students to submit assignments and take exams from the comfort of their homes.

5. Giving Helpful Feedback

Not only does AI allow teachers to create customized courses, but it also enables them to provide helpful feedback to students. Many schools have adopted AI tools to monitor student progress. These gadgets are also assisting teachers with identifying weak areas in student learning. Such a learning method comes with multiple advantages.

Students can get valuable feedback from teachers while teachers can focus on improving instructions for struggling students. And it is not just the individual courses that AI is helping teachers with. Some schools are working with industry experts to develop AI that can help students choose their majors. For instance, a student excelling in Math can use AI to receive the best possible choice for majors.

6. Providing Access to Data Analytics

The importance of data cannot be overstated in this digital age. Today, super-powerful computers can analyze and help researchers interpret a huge amount of data. Such systems are also assisting colleges with improving student performance. They are enabling schools to create customized courses for students.

Some colleges have already introduced AI-guide training courses. These tech-powered courses aim to ease the transition between different levels of learning. Who knows one day you might be able to select the best school or college for your kid from a list of recommendations just like a Netflix app.

7. Promoting Self-Learning

Perhaps the biggest advantage of AI is that it promotes self-learning. Students no longer have to visit book stores to purchase course material or relevant books. AI tools can provide them with a list of recommendations. Moreover, technology can also provide the best course to take while preparing for exams.

Virtual assistants like Alexa and Google Assistants have already improved the living experience for millions around the world. Schools, colleges, and universities are starting to benefit from AI-powered tools. There might come a day when you'll ask your VR to recommend the best study material for exam preparation.

Conclusion

AI is often seen as a villain for replacing physical instruction mediums and putting the livelihood of teachers in danger. It is important to see the picture in a bigger context. AI is the future and the future is defined by digitalization. The advantages of AI far outweigh its disadvantages. It has proven to be extremely helpful during the pandemic. Here's hoping it continues to improve every sphere of life.

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PHP is mainly focused on server-side scripting, so it can not only do the tasks any other CGI program can do, such as collect form data, generate dynamic page content, or send and receive cookies, but also can be used to develop client-side Graphical User Interface ( GUI ) applications as well.

It is integrated with a number of popular databases, including MySQL, PostgreSQL, Oracle, Sybase, Informix, and Microsoft SQL Server. It was originally created by Danish-Canadian programmer Rasmus Lerdorf in 1994

Future of PHP

Web development is a fast-evolving domain with frequently emerging trends and new technologies. One of the most prominent programming languages that can effectively implement the latest solutions in web applications is the PHP programming language.

Let us look at the various uses of PHP language in enhancing Web Development:

Web Pages Applications

PHP has a 3-tiered architecture that works on browser, server, and database systems in a linear manner. So PHP helps in achieving a high degree of customization, provides a highly interactive User Interface, and is capable of performing online transactions and integrating with database systems. And because of this only, it is used by more than 75 percent of websites for server-side programming. Even Multitude of Facebook Apps is scripted in PHP.

Decrease the time of Web Development

PHP is partly object-oriented programming, so, just like C++ supports reusability of code, so does PHP, in which codes can be used again for other website development tasks and this helps to save a lot of time for Developers.

Also, Developers have a wide variety of frameworks to choose from for web development, such as Laravel, WordPress, Symphony, etc. which provides different types of functioning features that makes the website developing work secured and speedy.

Web-Based System

There are innumerable types of systems that can be constructed with PHP and these also don't need to be hosted on the internet. It is possible to run PHP on a local server, with access to only those connected to an internal network.
To name some of the examples of the web-based system developed using PHP :

• Moodle: Free and open-source distance education system.
• Help desk to serve customers.
• Intranet system with login and password for company employees.

GUI – Based Applications

PHP not only serves the purpose of being used as a scripting language for web-based applications, it is also employed for creating desktop Graphical User Interface ( GUI ) based applications. Tools that help to achieve this functionality are PHP-GTK2, DevelStudio, and ZZEE PHP GUI.

eCommerce Applications

Many of the highly used eCommerce platforms such as OpenCart, Magento, PrestaShop, Zen Cart, AgoraCart, and Ubercart, have all been created on PHP. Through the use of frameworks like CodeIgniter and CakePHP, PHP allows the creation of eCommerce applications in a swift and simple manner.

So, we can conclude that PHP is a time-tested programming language that manages to keep the leading positions in web development. And it seems to continue to maintain its popularity in 2021.

It stays at the forefront of web development as it's constantly developed and upgraded by its creators. It can comply with the requirements of businesses for cutting-edge web applications and offer a variety of tools and frameworks for building the latest technological trends such as chatbots, PWAs, SPAs, and many others.

As we have seen that PHP is much used and I too believe that it is nowhere to go as of now. It may eventually become very less popular maybe after 10 or 20 years and some other coding language may occupy the lead position. But one thing is clear that it does have a future.

The best thing most of them like about PHP is that it is extremely simple for a newcomer, but offers many advanced features for a professional programmer.

Additionally with features like

• Runs on various platforms (Windows, Linux, Unix, Mac OS X, etc.)
• Is compatible with almost all servers used today (Apache, IIS, etc.)
• Supports a wide range of databases
• Free to download. Get it from the official PHP resource: www.php.net
• PHP is easy to learn and runs efficiently on the server-side

And, characteristics like:

• Simplicity
• Flexibility
• Familiarity ( Syntax is C-like )
• Security
• Efficiency

Is the language Alive or Dead?

Since this topic arose, we will try to look at both sides of the coin:

Still a Popular Coding Language

As of early 2020, 26% of programmers are using PHP. Today, it's one of the most popular server-side programming tools. One of the main reasons that may explain its popularity is the simplicity of this tool. PHP is a relatively easy coding language. That makes it a number-one tool for people who are new at creating websites.

Also, WordPress uses PHP which makes this programming language more important and relevant as the average market share of WordPress is 34% of all websites. Overall, 77 million websites are using WordPress today. So we can see that a variety of shop management systems are still using PHP.

PH7 Version

This version is called the revival of this Programming Language because, in this, some crucially important updates and enhancements were added after which Programmers mostly stopped complaining about this tool.

The greatest benefits of the seventh version are enhanced site speed and much better memory usage. Besides, the new version also included the following improvements:

• New type hinting.
• The capability to use keys in lists.
• Type declarations.
• Faster error handling.
• Nullable types, and much more!

75% of the Web

PHP earned a lot of bad reputation in the 1990s and early 2000s for being insecure, but still, according to a report, PHP appears to be running more than 75% of the web, managing to cling to the title of most used server-side language.

Still Dominating the Web …

As we can see from the graph above, PHP is the most used server-side programming language by far. Approximately 75 percent of all web pages are powered by PHP and this is a far too high number for a language to be dead.

And this is the graph 0f 2019, and the story is still very much the same.

Employment Opportunities

Since most of the web content is powered by PHP, by most I mean > 75 %. So, there is the availability of a lot of Jobs that involve PHP coding. PHP developers are the ones who are needed to maintain the Websites, so plenty of Jobs will be available in the future involving PHP.

Suboptimal Codes

Anything which has been around since 1994 requires updates. But, PHP, updated its codebase but doesn't have a definite decision as to which codebases are no longer validated. This has made the language cluttered.

There are many ways now to build the same functionality, which makes it easier to write incorrect and bad code in PHP.

Although, it is true that it is easy to get started with PHP mainly due to the presence of old stuff. But if you stay in the comfort zone, you end up with the suboptimal code that is now regarded as a bad practice. And this is the reason some developers hate PHP as it is not clear with this language what solution is the best.

Declining Employment Scenario?

According to most developers, PHP is alive mainly due to its parasitic relationship with WordPress. So, whenever Job requirements are outside the WordPress-related activities, then PHP is not anymore the potential candidate as a language to be considered as a role requirement.

According to a UK-based recruitment agency that published their recruitment demands data, the experience with PHP recruitment shows a consistent downward trend.

Conclusion

We can definitely say that PHP is not dead! Some of the reasons reflecting Language's Aliveness is :

• Easy to update
• PHP libraries

PHP is quite a popular and widely used programming language, it won't fade easily. PHP usage will drop down in a slow and steady manner. PHP is its own victim in some cases. PHP death will leave a great impact on eCommerce as these use WordPress. Hosting companies also play a role as they continue to support WordPress as the main CMS of choice, making it much more accessible to general users than other server-side languages like Java and C++.

So, we can conclude that as long as WordPress is alive and kicking PHP is not dying as a lot of PHP legacy code is tied up with older versions of WordPress. And, to sum it up, languages never die, they proliferate.

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Behind this digital push exists new technologies- which have brought with them new challenges. One of the main challenges the world is faced with is how to allow for a seamless transition into the new technological world. A successful digital transition is almost entirely dependent on competent human capital.

It is reasonable to suggest that the best way to prepare for technological change is by using technological strategy. Artificial Intelligence solutions include comprehensive and intuitive machine learning tools that allow for faster, better and smarter decision making. But does this mean we should ever negate the role of human intelligence all together?

In Competition or In Combination?

The space between artificial intelligence and human intelligence is characterised by a functional and highly affiliated relationship. Many have questioned whether the current technological state situates us in a place of immortality or extinction. Perhaps the answer to this ponder is neither. The question isn't who will win?' but rather, how are they linked and can they co-exist?

If AI is the term used to describe the simulation of human intelligence processed by machines, including learning, reasoning and problem-solving, can we truly separate AI from HI? Ultimately, AI is nothing but a simulation of HI and would cease to exist without human input.

One catastrophic demonstration of the risks we face when leaving artificial intelligence to its own devices took place in 2018. A self-driving Uber test car struck and killed a person on the road. Based on this case alone, we can say that AI still lacks human input- and probably always will.

Niche human qualities such as communication, creativity, empathy, questioning and strategic thinking, make the world go round. However, so too do efficiencies. For example, one doctor can make a diagnosis in ten minutes, and an AI system can make millions in ten minutes. Further, there is no risk of intelligent machines producing decisions and carrying our tasks based on biased opinions. AI can work for days on end and effectively generates the capacity for a productive, methodical and efficient global digital economy.

Most jobs that can be performed by humans have three essential elements: emotional intelligence, creativity and STEM education. The latter has joined forces with artificial intelligence to produce the perfect balance. Science, technology, engineering and mathematics each hold their own in academia and the workforce. But what happens when artificial intelligence technology steps in to intervene in the STEM education learning process?

Combined AI Learning Solutions

In an effort to provide human opportunities for professional development in an increasingly digitalised world, AI has become the basis that underpins the structure of a new learning model. University 20.35 has created an entirely new university model, whereby each participant at any given moment makes a decision based on the recommendations that take into account their digital footprint, that of others and their use of the educational content available to them.

The collection of Big Data on participants professional and educational backgrounds, in combination with the analysis of student activity, enables the university model to create and recommend the best development path to be followed.

Each participant's digital footprint is collected during STEM educational processes to confirm the students' skills. Using the digital footprint, the university model can detect gaps in knowledge to successfully confirm that a trajectory module tutor is efficiently able to transfer knowledge to the students. Through this transfer of precise and required knowledge, we realise truly impeccable efficiency gains. Less time and energy is allocated to transferring skills and knowledge that is already established.

Another way artificial intelligence and human intelligence can work together and learn from each other is via the development of a digital twin. University 20.35's model of learning has the capacity to generate one for each student. An educational digital twin is essentially the replica of the students own physical profile. This near to real-time digitalisation of the student's knowledge and skills can be modified to take into account lost information (forgetfulness) and enhanced skills (practising and established skills). By digitalising the student's profile, a proactive educational program can be modified for best practice.

In other words, artificial intelligence allows us to aggregate those fragments of ourselves into a comprehensive model that better represents us. When we as humans work with AI, we can improve the mutually beneficial relationship that already exists. In most cases, AI solutions will continue to rely on humans for their initial datasets. The further development of artificial intelligence, solutions will most likely see its ability to gather and grow its own data. Whilst this may mean AI will become less dependent on humans for knowledge transfers, the need for human intelligence will never go amiss. Rather, we can anticipate the emergence of a more symbiotic relationship where both modes of intelligence are dependent on each other to produce the best possible results.

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While many might have their vision set on attending a traditional four-year institution, this isn't often the most cost-effective solution. Current college students know that the direction of education is online. This type of learning allows for massive flexibility so that you can structure your degree around your chaotic life schedule. In fact, online education is now more popular, effective, and affordable than ever thanks to advances in artificial intelligence (AI) that have entirely rescoped receiving an education from your computer.

Smarter Alternative

Pioneers in AI have constructed a smart alternative to online education with the help of AI. The advanced algorithms of AI adaptive programming can provide students with personally structured one-on-one educations that avoid education bias.

This adaptive learning system allows for different learning models to be constructed based on individual student needs. While students can drown in the crowd of a packed lecture hall, online learners will be treated to an advanced personal assessment and a customized learning journey. This allows students to learn material at a personal pace rather than by a traditional syllabus. The result is students receiving a more well-rounded and thorough education.

E-learning allows for a deviation from a set syllabus to provide students with material that is always relevant. The ability to adapt and adjust assignments and lectures in real time allows for a more complete education that doesn't waste any time.

Personalized Learning

Students who attended traditional universities can probably testify that they seldom had personal interactions with their professors. In fact, it's possible to finish a course without direct communication at all. The kind of personal online education that e-learning offers can assure that students are viewed as individuals, allowing their education to be structured around their unique learning needs to best help students grasp information.

AI fueled e-learning can offer individual tutoring for students, a rarity that can enormously assist in gaining a more in-depth knowledge of complex subjects. Rather than wait for office hours to ask questions, e-learning technology allows students to clarify areas of confusion while they are learning. By acting as a virtual tutor, AI can answer questions when they arise to allow for students to waste less learning time due to confusion.

This method is particularly effective for students who are shy or have any type of personality disorder that prevents them from readily asserting themselves. Rather than fear looking stupid, students can feel free to ask questions privately to better understand content.

Dramatic Changes

Living in the age of technology, this generation is on the cusp of some major technological revolutions. Though online education might not be perceived as widely popular at the moment, advances in AI are helping to shape e-learning as the education of the future.

E-learning is now at its most accessible point in human history, offering an entirely new education service that could allow for struggling individuals to receive an education without sacrificing their savings and being faced with decades of student loans. E-learning costs and associated resources provide far less of a financial burden than traditional higher institutions to allow underprivileged students the chance to be on an equal playing field when it comes to careers.

The complex AI algorithm will be able to assess how well students grasp subjects to redirect teaching approaches when needed to provide for a maximum learning experience. Pioneers in AI are currently creating the most advanced and efficient system of mass education that can be offered to individuals.

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Yet, for leaders within the Learning and Development (L&D) spheres, it can be difficult to get everyone on board with data analytics programs, chiefly because there is an inherent lack of knowledge around how those insights can benefit everyone, across every department. When a company is led by data-driven thinking, it's able to create clearer forecasts, stronger marketing and outreach programs, and more effective products and services across the board.

Yet, some leaders and managers might not see data analytics as playing into their job descriptions and as such, consider meetings around such initiatives as a use of their time that could be better spent working on more pressing matters. The catch? These team members are often the ones with the most access to valuable, actionable data insights, such as sales reports and customer feedback, that their executive teams need the most.

To that end, here are a few ways that L&D leaders can amplify their data analytics programs and help encourage everyone to get on board with saving, reporting and actively using data to solve complex problems across the organization.

1. Align the data analytics process with strategic business goals

Disinterest might lie in the fact that some team leaders aren't sure how data analytics applies to their role or can be used to meet overarching business goals. As such, it's helpful to align the processes with key initiatives that employees in every department are tasked with supporting. Recent Gartner research reveals that 60% of data analytics failures occur because the strategies aren't properly aligned. Either they don't match up with the correct organizational talent, or they're too separate from the business goals as a whole.

To prevent this, L&D leaders should ensure they understand clearly where the business is headed, what the C-suite is seeking to accomplish in the short and long-term, and what the learning outcomes encompass. For instance, big, company-wide goals might be to improve employee productivity, grow sales, improve products and services and increase retention rates.

Then, in tandem with data analytics and other IT leaders, L&D professionals can work with individual teams to reveal how data insights can be used to solve these issues and meet these goals. You might find there are some employees who are eager to learn these tactics and welcome the opportunity to become early adopters. Some might be more resistant to the process change. Work with the most enthusiastic team members first to fine-tune a training strategy and curriculum.

2. Design the learning process to meet employee capability

The overarching goal of the data analytics program should be for employees to demonstrate both competence and capabilities. By working with their inherent talents, you can improve your chances of long-term user adoption. By leveraging a process known as Spaced Learning, you can enable employees to use what they've learned in your training program and apply it to their real-life job scenarios. This affords a more interactive, natural flow to the learning process than textbook-driven study sessions that don't allow for any hands-on work.

Spaced learning is just that, in that it's spaced out and allows for time between learning sessions. During those interim periods, employees are actively applying their lessons learned to solve on-the-job problems and meet customer needs. While this can be an excellent foundation, it's also helpful to design the data analytics training around a proven framework, such as the BADIR approach, to simplify complicated concepts and give students a defined program to adhere to. Standing for Business question, Analysis plan, Data collection, Derive insights and Recommendations, BADIR is a simple acronym that employees can remember as they work through their data analytics steps.

3. Remember to reward and motivate.

Especially if your early adopters are few or if your company has never implemented a robust data analytics program before, you may need to spend some extra time reinforcing the practice through reward and motivation tactics. You may be surprised at how effective simple badges or certificates can be in terms of boosting employee morale and motivation. Or, you may decide to start recognizing employees who have gone above and beyond their data analytics requirements or have uncovered a particularly valuable insight as a result of their implementation. If you go this route, it's helpful to define set metrics at the onset so employees know what to expect, which levels they need to reach and which perks are granted at each level so they can consistently strive for more.

Moving forward, implementing a data analytics program within your organization doesn't have to be overwhelming. By employing the right personnel, resources and training tools, you can help encourage everyone to take a second look at the value of the information they handle every day, and how it can be used to propel the organization forward. Success lies in making the most of every insight that passes across your desk, and as Big Data continues to proliferate, this initiative has never been more important.

The post Three Ways L&D Leaders Can Set Internal Data Analytics Programs Up for Success appeared first on Datafloq.

Factors contributing to Python's growth

One of the main reasons why Python is growing at a great pace is that it can be utilized for a large array of purposes, starting from web development to data science to DevOps. Therefore, it has become very important and useful to understand the concepts of Python development.

Programmers who were earlier using programming languages, like C, and never believed that scripting could be as powerful, unless they were introduced to Python. The language plays a major role in lowering down the workload through tasks automation. Plus, it is pretty reliable and useful as well, hence, it is regarded as a high-level programming language. Plus, it can be used in performing various tasks related to data preprocessing, I/O, data science, data mining, web scraping, serial communication etc. The availability of the free libraries make it all the more preferable.

Where is Python used?

Data science, machine learning and academic research are three amongst the fastest adaptors of Python, and when we talk about industries, electronics, government, university, manufacturing etc., it is being used almost everywhere.

Here are the 5 best ways to learn Python:

1. Figure out the whys'

Firstly, just ask yourself why you want to learn python. Find out the things that motivate you to learn a new programming language. Leaning Python might need a lot of time, dedication and the journey to mastering it may not be full of rosebuds. Hence, it is important to find the key reasons of learning the languages. Plus, in your mind, you should always know what motivates you to learn the language. You will need this motivation whenever you get stuck in the midway. Figure out how you want to make the full use of your Python learning:

• By becoming a Data scientist or a Machine learning expert
• By building world-class Mobile apps
• By creating avant-garde Websites
• By making Games
• By using it on Hardware / Sensors / Robots
• By Scripting to automate your work

2. Understand the core Syntax

Once you are ready to embark on your Python journey, start by learning the basics of Python syntax before you go deeper into every part of the programming language. It is very important to solidify the roots before becoming a master of Python. Many Python tutorials, are available online to learn the basics of the language. CodeAcademy, Dataquest, Learn Python the hard way are some of them.

3. Create structured projects

After learning the basics, start making projects on your own. One of the best ways to thoroughly learn the language is by practically applying the knowledge. Projects are a great way to push your capabilities and will also help you create a portfolio to show to potential employers.

4. Master every part of the project development

To become a topnotch Python expert, you should also have a solid handle on debugging issues. Start critically working on your own projects to learn every aspect of the project development. Here are a few tips for finding interesting projects:

• Add more functionality to your own project
• Collaborate with people who work on projects that interest you
• Look for open source packages
• Start working for nonprofits
• Look for projects other people have made

5. Keep jumping onto tougher projects

Once you get the hang of the easy projects, it's time to shift your gears towards tougher projects. You can choose to make your projects more intricate as well. The best way to grow is to work with seniors on relatively tougher projects and contribute in the project development.

Where to learn Python?

Listed below are some of the best online places to learn Python:

1. The Complete Python Programming Boot Camp: Beginner to Advance
2. Python for Programmers
3. Learn Python with 70+ Exercises
4. Advanced Machine Learning in Python with TensorFlow
5. Selenium WebDriver with Python 3.x: Novice to Ninja

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In this article, I will be providing you with a comprehensive definition of supervised, unsupervised and reinforcement learning in the broader field of Machine Learning. You must have encountered these terms while hovering over articles pertaining to the progress made in AI and the role played by ML in propelling this success forward. Understanding these concepts is a given fact, and should not be compromised at any cost. Here we discuss the concepts in detail, while making sure that the time you spend understanding these concepts pays off and that you are constantly aware of what is happening during this progress towards an Artificially Intelligent society.

Supervised, unsupervised and reinforcement Machine Learning basically are a description of ways in which you can let machines or algorithms lose on a data set. The machines would also be expected to learn something useful out of the process. Supervised, unsupervised and reinforcement learning lead the way into the future of machines that is expected to be bright, and will over time assist humans in doing everyday things.

Supervised Learning

Before we delve into the technical details regarding supervised learning, it is imperative to give a brief and simplistic overview that can be understood by all readers, regardless of their experience in this growing field.

With supervised learning, you feed the output of your algorithm into the system. This means that in supervised learning, the machine already knows the output of the algorithm before it starts working on it or learning it. A basic example of this concept would be a student learning a course from an instructor. The student knows what he/she is learning from the course.

With the output of the algorithm known, all that a system needs to do is to work out the steps or process needed to reach from the input to the output. The algorithm is being taught through a training data set that guides the machine. If the process goes haywire and the algorithms come up with results completely different than what should be expected, then the training data does its part to guide the algorithm back towards the right path.

Supervised Machine Learning currently makes up most of the ML that is being used by systems across the world. The input variable (x) is used to connect with the output variable (y) through the use of an algorithm. All of the input, the output, the algorithm, and the scenario are being provided by humans. We can understand supervised learning in an even better way by looking at it through two types of problems.

Classification: Classification problems categorize all the variables that form the output. Examples of these categories formed through classification would include demographic data such as marital status, sex, or age. The most common model used for this type of service status is the support vector machine. The support vector machines set forth to define the linear decision boundaries.

Regression: Problems that can be classified as regression problems include types where the output variables are set as a real number. The format for this problem often follows a linear format.

Unsupervised Learning

Since we now know the basic details pertaining to supervised learning, it would be pertinent to hop on towards unsupervised learning. The concept of unsupervised learning is not as widespread and frequently used as supervised learning. In fact, the concept has been put to use in only a limited amount of applications as of yet.

Despite the fact that unsupervised learning has not been implemented on a wider scale yet, this methodology forms the future behind Machine Learning and its possibilities. We always talk about ML bringing forth unlimited opportunities in the future, but fail to grasp the detail behind the statements made. Whenever people talk about computers and machines developing the ability to teach themselves in a seamless manner, rather than us humans having to do the honor, they are in a way alluding to the processes involved in unsupervised learning.

During the process of unsupervised learning, the system does not have concrete data sets, and the outcomes to most of the problems are largely unknown. In simple terminology, the AI system and the ML objective is blinded when it goes into the operation. The system has its faultless and immense logical operations to guide it along the way, but the lack of proper input and output algorithms makes the process even more challenging. Incredible as the whole process may sound, unsupervised learning has the ability to interpret and find solutions to a limitless amount of data, through the input data and the binary logic mechanism present in all computer systems. The system has no reference data at all.

Since we expect readers to have a basic imagery of unsupervised learning by now, it would be pertinent to make the understanding even simpler through the use of an example. Just consider that we have a digital image that has a variety of colored geometric shapes on it. These geometric shapes needed to be matched into groups according to color and other classification features. For a system that follows supervised learning, this whole process is a bit too simple. The procedure is extremely straightforward, as you just have to teach the computer all the details pertaining to the figures. You can let the system know that all shapes with four sides are known as squares, and others with eight sides are known as octagons, etc. We can also teach the system to interpret the colors and see how the light being given out is classified.

However, in unsupervised learning, the whole process becomes a little trickier. The algorithm for an unsupervised learning system has the same input data as the one for its supervised counterpart (in our case, digital images showing shapes in different colors).

Once it has the input data, the system learns all it can from the information at hand. In fact, the system works by itself to recognize the problem of classification and also the difference in shapes and colors. With information related to the problem at hand, the unsupervised learning system will then recognize all similar objects, and group them together. The labels that it will give to these objects will be designed by the machine itself. Technically, there are bound to be wrong answers, since there is a certain degree of probability. However, just like how we humans work, the strength of machine learning lies in its ability to recognize mistakes, learn from them, and to eventually make better estimations next time around.

Reinforcement Learning

Reinforcement Learning is another part of Machine Learning that is gaining a lot of prestige in how it helps the machine learn from its progress. Readers who have studied psychology in college would be able to relate to this concept on a better level.

Reinforcement Learning spurs off from the concept of Unsupervised Learning, and gives a high sphere of control to software agents and machines to determine what the ideal behavior within a context can be. This link is formed to maximize the performance of the machine in a way that helps it to grow. Simple feedback that informs the machine about its progress is required here to help the machine learn its behavior.

Reinforcement Learning is not simple, and is tackled by a plethora of different algorithms. As a matter of fact, in Reinforcement Learning an agent decides the best action based on the current state of the results.

The growth in Reinforcement Learning has led to the production of a wide variety of algorithms that help machines learn the outcome of what they are doing. Since we have a basic understanding of Reinforcement Learning by now, we can get a better grasp by forming a comparative analysis between Reinforcement Learning and the concepts of Supervised and Unsupervised Learning that we have studied in detail before.

1. Supervised vs Reinforcement Learning ‘

In Supervised Learning we have an external supervisor who has sufficient knowledge of the environment and also shares the learning with a supervisor to form a better understanding and complete the task, but since we have problems where the agent can perform so many different kind of subtasks by itself to achieve the overall objective, the presence of a supervisor is unnecessary and impractical. We can take up the example of a chess game, where the player can play tens of thousands of moves to achieve the ultimate objective. Creating a knowledge base for this purpose can be a really complicated task. Thus, it is imperative that in such tasks, the computer learn how to manage affairs by itself. It is hence more feasible and pertinent for the machine to learn from its own experience. Once the machine has started learning from its own experience, it can then gain knowledge from these experiences to implement in the future moves. This is probably the biggest and most imperative difference between the concepts of reinforcement and supervised learning. In both these learning types, there is a certain type of mapping between the output and input. But in the concept of Reinforcement Learning, there is an exemplary reward function, unlike Supervised Learning, that lets the system know about its progress down the right path.

2. Reinforcement vs. Unsupervised Learning

Reinforcement Learning basically has a mapping structure that guides the machine from input to output. However, Unsupervised Learning has no such features present in it. In Unsupervised Learning, the machine focuses on the underlying task of locating the patterns rather than the mapping for progressing towards the end goal. For example, if the task for the machine is to suggest a good news update to a user, a Reinforcement Learning algorithm will look to get regular feedback from the user in question, and would then through the feedback build a reputable knowledge graph of all news related articles that the person may like. On the contrary, an Unsupervised Learning algorithm will try looking at many other articles that the person has read, similar to this one, and suggest something that matches the user's preferences.

The realms in Machine Learning are endless. You can pay a visit to my YouTube channel to get to know more about the world of AI and how the future will be dictated by the use of data in machines.

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