Thursday 25 March 2021

Advanced AI: What is Advanced Artificial Intelligene & Top AI Courses


Advanced AI

The rapidly growing importance of artificial intelligence (AI) shows that it is the skill of the future. Indeed, currently, the demand for people having skills and knowledge of AI and machine learning (ML) outpaces the supply. It is estimated that by 2030, the AI market will contribute over 15 trillion US dollars to the world economy. For those interested, an advanced artificial intelligence course will help open up career avenues and greatly enhance career prospects.

Why AI?

Artificial intelligence (AI) builds intelligent machines that can make and carry out decisions similar to human beings. AI allows these machines to absorb and synthesize data similar to how a human mind works, before finally coming up with a final decision. AI depends on deep learning, natural language processing, neural networks, and analysis. Machine learning (ML) is a specialised subset of AI. Here, computer systems use self-generated feedback (through trial and error) and data to perform tasks without any instructions from programmers.

AI and ML are more widely used today than most people realise.  A popular example of machine learning is the algorithm on social media apps such as Facebook and Instagram. Top banks and financial institutions use machine learning for fraud detection and self-driving cars are also an example of AI and ML.

For those looking for exciting, revolutionary, and in-demand jobs, this is the right time to take up an advance artificial intelligence course. And if you are a working professional or you want to take up a course that’s not in the same state or even country as you are in, don’t worry. Some of the best artificial intelligence and machine learning courses are available online.

Where to start?

When it comes to the best introductory course on AI and ML, you can’t go wrong with Professor Andrew Ng’s course on Machine Learning. It is available on Coursera and is offered by Stanford University. The course introduces students to the core ideas of machine learning. It provides a solid foundation in the fundamentals of computer science, mathematics, and statistics, which form the basis of ML.

Why advance artificial intelligence?

Once students have covered the basics and have a solid and robust knowledge of the fundamentals, they need to move on to more advanced learning. The idea is that the advanced AI courses should build on the base of the fundamentals and expand from there. So, the advanced AI courses should have the following two features

      They should cover more diverse and advanced topics than the introductory course

      They should have a highly specialised emphasis on AI or ML

How to select an advanced AI course?

A high-quality advanced AI course should fulfil the criterion given below:

  1. The course should not have a marked emphasis on any particular programming tool or framework, such as Python
  2. The course should ideally be offered by universities providing a strong focus on a theoretical foundation
  3. The course should be taught by renowned researchers or university teachers
  4. The course should not be focused on data engineering, data science, digital analytics, and applied statistics. While these subjects are important, the course you select should be focused purely on machine learning and artificial intelligence.

There is no dearth of artificial intelligence and machine learning courses available today, which makes it trickier to select a worthwhile one. A high-quality course should be beneficial for intermediate and advanced students and the list above will help separate the wheat from the chaff.

Top advanced AI courses

Wading through pages of research trying to narrow down the best course can be a daunting task. So to make things easier, here is a list of the top advanced AI courses available online today categorised into three groups:

  1. Deep learning and general machine learning
    1. ‘Machine Learning’ by George Tech available on Udacity. This is a comprehensive machine learning course covering supervised and unsupervised learning, randomised optimisation techniques, introductory game theory concepts, and reinforcement learning
    2. ‘Advanced Machine Learning Specialisation’ available on Coursera by National Research University Higher School of Economics. This is a set of five courses by Russian researchers covering foundational concepts and practical deep learning techniques
    3. ‘Machine Learning Fundamentals’ available on edX by University of California San Diego. This course teaches core machine learning theoretical and practical concepts with an emphasis on algorithmic issues
    4. ‘Machine Learning’ by the California Institute of Technology taught by Professor Yaser Abu-Mostafa. It teaches learning theory as well as the deep mathematical aspects of machine learning.

  2. Reinforcement Learning
    1. ‘Reinforcement Learning’ by George Tech available on Udacity. Perhaps the most comprehensive course on the subject, it covers a wide range of topics such as Q-learning, Bandit problem, goal abstraction techniques, Rmax analysis, Temporal Difference (TD) learning, and much more.
    2. ‘Practical Reinforcement Learning’ available on Coursera by National Research University Higher School of Economics. This is also a comprehensive and practical type course covering a range of topics. However, it does not include game theory and focuses more on Q-Learning.

  3. AI and Game Theory
    1. ‘Introduction to Artificial Intelligence’ available on Udacity. This is perhaps the most comprehensive core AI course and is taught by renowned researchers Peter Norvig and Sebastian Thrun. It covers a huge range of topics from Markov processes, representational logic, robotics, and much more.
    2. ‘Artificial Intelligence’ available on edX offered by Columbia University.  This is a good course that covers a broad range of essential topics such as the history of AI, logic, games, computer vision, and so on.
    3. ‘Game Theory’ available on Coursera offered by Stanford University. This is a great course for learning all that is to do with game theory such as Mixed strategy, Subgame perfect, Bayesian game, and so on.

Summing up

Artificial intelligence is an exciting field that makes the dreams of the future possible today. It is also a field with a growing need for skilled people. So take the first step to a brighter future by enrolling in a course designed to teach you all about AI. 

Thursday 11 March 2021

Hybrid App: What are Hybrid Mobile Apps & Hybrid App Development


What are Hybrid Apps?

As of April 2021, in a report by GSMA Intelligence, it is estimated that there are 5.27 billion mobile users in the world. The majority of the time that people spend on their smartphones is spent on various apps. App development is very important today and developers have a wealth of choice when it comes to making an app. One important choice will be—should the app be a hybrid app? Read on to know more about hybrid apps.

What is a native app?

Before we understand what a hybrid app is, let us understand what a native app is. A native app is created in a specific programming language and for a particular platform such as Android or iOS. The programming language they are written in is the one that is accepted by the device platform. So, for most Windows phones, the native app is written in C#, while for Android and iOS, the programming languages are Java and Swift/Objective-C, respectively.

What is a hybrid app?

A hybrid app is built to run on different platforms, whether they are Android, iOS, or Windows.  A hybrid app is created using the same code for all platforms. Hybrid apps are downloaded from a platform app’s store. Hybrid apps combine features from web applications and native apps. These apps are created using web technology such as CSS, HTML, and JavaScript. Essentially, hybrid apps are web apps that are put in a native app shell. Once a hybrid app is downloaded from the play store, the shell connects to all features that the mobile platform has through a browser that is embedded within the app.

What are some examples of hybrid apps?

An astonishing number of popular apps today are hybrid apps. Here are some examples of famous, high-performance apps that enjoy a high level of user engagement:






What are the features of hybrid apps?

Here are some of the features of hybrid apps that set them apart from other app types:

      Integrated with the smartphone’s file system

      Integrated with web-based services but runs as a real app

      Can function even when the smartphone is not connected to the net

      Browser is embedded so access to dynamic online content is enhanced

What are the hybrid app advantages?

  1. Single codebase:
    All hybrid app advantages
    stem from the fact that it has a single codebase. So instead of building two apps with different codes, developers build a single app using a single codebase, which is then tweaked to run on any platform.

  2. Scalability:  
    As hybrid apps use a single codebase, scalability is far easier than with native apps. Updates and patches are also easier to launch with hybrid apps.

  3. Lower cost development:
    When making a hybrid app, fewer resources are used to build the app since a single code is used for all platforms. This makes hybrid apps much more affordable than native apps.

  4. Time-efficient:
    Since developers use a single codebase for multiple platforms, there is much time saved that would have otherwise been used to develop apps suited for specific platforms.
  5. Online and offline functionalities:
     Hybrid apps work both online and offline because of the native shell infrastructure. When offline, the app may not display real-time data, but it will still load and display data that was previously loaded. 

Why are hybrid apps so popular among businesses?

  1. Quick loading speeds:
    Hybrid apps can deliver quick loading speeds due to their lightweight user interface that helps load graphics and content quickly and effectively.

  2. Consolidated development:
    Cross-platform frameworks such as Flutter and React Native have improved the development of hybrid apps. These frameworks have greatly simplified the process of app development making it easier for developers to build apps using a single code for multiple platforms.

  3. Broader market reach:
    Hybrid apps make it easier for businesses to reach a broader market by enabling the app to be launched on all platform types with a single codebase. This makes it more cost convenient as well.

  4. Offline capacity:
    Hybrid apps allow users to access most, if not, all features and data of the app even when the device is not connected to the internet.

  5. Offers a Native experience:
    Often Hybrid apps are capable of offering a Native experience, especially when these apps are developed on frameworks such as Flutter. Hybrid apps can deliver a superior user experience, which engages the maximum number of users, thereby, increasing sales and generating revenue for businesses.

  6. Easy to maintain:
    Maintenance is greatly simplified with a hybrid app. Hybrid apps bypass complicated updating processes by allowing an update to happen as easily as updating a webpage.

The bottom line

The convenience of a single cost and enhanced user experience as well as the cost and time saved, has made hybrid apps a popular choice among developers, investors, and businesses. Today, it is a leading choice for app development. But before you settle on an app type, choose one whose features and hybrid app advantages will best fulfil the parameters and deliver the expected results you want.



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