computational language theory

Computational Learning Theory Vs Statistical Learning And ML Theory

Computational Learning Theory Vs Statistical Learning And ML Theory

Muhammad Imran


April 27, 2020

computational language theory

Computational learning theory is a sub-domain of Artificial Intelligence, in the field of computer science, which is dedicated to the design and development of machine learning algorithms.

Interest in Computational Learning Theory over Time

If we are to look at the geographic trend for the computational learning theory it is interesting to see that the subject is most popular and widely searched for in the USA and India. This may also be understandable given that both of these countries have the biggest silicon valleys in the world with hundreds and thousands of high-profile computer professionals.

Interest in Computational Learning Theory over Time

Statistical Learning theory

Statistical learning theory depends largely on statistics and functional analysis to forms the framework for the development of machine learning algorithms. Contrarily, machine learning is the demonstration of statistical learning techniques, which are executed using specialized software applications.

In simple terms, statistical learning theory is the basis to develop tools and techniques for a better understanding of data. In the context of statistical learning, the data may be represented in two ways:

1) Data independent of variables – which can be managed directly.

2) Data with dependency on variables – which can’t be managed directly ( this type of data requires prediction or estimation).

Now, if we are, to sum up, the statistical learning theory, it offers tools and techniques to identify hidden relationships in dependent and independent data.

Difference Between Machine Learning Theory and Statistical Learning Theory

The difference between computational learning and statistical learning theory is one of the most common questions which data scientists around the world are asked about. Now, while the difference between the two is quite obvious, still many young data scientists tend to be confused about the two approaches.

As a novice data scientists, it’s understandable to confuse between these two theories, mainly because of the way data science is taught in colleges. To put it out simply, to become an expert in data science, one needs to develop strong skills in multiple fields that include; programming, mathematics, statistics, SQL and other specialist fields. Which, is to say that while you are on your way to becoming data scientists, you will most probably begin with statistics, which is the foundation of data science, computational learning, and machine learning. Once you have developed sufficient skills in statistics, you can then expand your scope within the data science. So, it won’t be wrong to say that statistical learning theory is the first step towards computation learning theory.

Nonetheless, there are certain differences between these two theories as well and it’s important to understand those differences, to be able to really master the skills. Below, we have come up with some differences between statistical learning and machine learning, as well as, statistical learning theory and computational learning theory to assist you in your journey to success.

1) While, both the approaches are highly dependent on data, however, statistical learning is more dependent upon rule-based programming, where it needed to create certain relations between variables. On the other hand, machine learning is more automated programming independent, that is to say, it learns from data without too much reliance on rule-based programming

2) Machine learning can learn from billions of observations, whereas, statistical learning is limited to smaller datasets and a small number of attributes

3) Statistical learning is strictly operated based on assumptions like homoscedasticity or normality, and others. Whereas machine learning is independent of assumptions and in many cases utterly ignores the assumptions

4) Statistical learning is mostly restricted to inferences, with high dependence on samples, hypothesis and populating the variables. Contrarily, machine learning is more about forecasting and predictions

5) Statistical learning involves mathematical theories heavily, whereas, machine learning relies on iterations to identify the patterns in the input data

Computational Learning Theory vs. Statistical Learning Theory

1) Computational learning theory is the subfield of computer science (AI), whereas, statistical learning theory is the subfield of statistics and machine learning

2) The focus on computational learning theory is in development of systems that are able to learn and identify patterns from data, whereas, the focus on statistical learning is to develop set of tools that are able to understand complex data for modeling purpose

3) The main purpose of learning theory computer science is to optimize the accuracy for predictions, whereas, in case of statistical learning the purpose is the development of statistical models capable of understanding data and make predictions

4) Computational learning theory doesn’t require prior assumptions about data, whereas, statistical learning theory requires basic knowledge about data

5) Computational learning theory requires basic knowledge of stats, which is the basis for algorithm development, whereas, statistical learning required knowledge of statistics and some basic knowledge about machine learning

Best Books on Computational Learning Theory

While there are various online courses offering data science and computer graduates with valuable content to learn and enhance their skills in computational learning theory, however, going through some of the books on Artificial Intelligence and ML, will surely help them to have a better background knowledge and a strong foundation to build upon their careers. Here are some of the best books on AI which are worth reading:

1) Python Data Science Handbook By Jake VanderPlas

2) Neural Networks and Deep Learning By Michael Nielsen

3) Think Bayes By Allen B. Downey

4) Machine Learning & Big Data By Kareem Alkaseer

5) Statistical Learning with Sparsity: The Lasso and Generalizations By Trevor Hastie, Robert Tibshirani, Martin Wainwright

6) Statistical inference for data science By Brian Caffo

7) Convex Optimization By Stephen Boyd and Lieven Vandenberghe

8) Natural Language Processing with Python By Steven Bird, Ewan Klein, and Edward Loper

9) Automate the Boring Stuff with Python By Al Sweigart

10) Social Media Mining: An Introduction By Reza Zafarani, Mohammad Ali Abbasi and Huan Liu

Top Computational Learning Theory Influencers to Follow 

If you are interested in keeping up-to-date with the latest happenings in the computational learning theory, you can follow some of the leading influencers who maintains great resources for the readers. Here are some of the top computational learning theory influencers you should follow:

  • Olga Egorsheva, Co-Founder and CEO, Lobster
  • Kate Bradley Chernis, Co-Founder & CEO, Lately
  • Mike Rhodes, Founder & CEO, WebSavvy
  • William Ammerman, EVP of Digital Media, Engaged Media
  • RJ Talyor, Founder and CEO, Pattern89

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How to Install Lombok for Java Eclipse with Gradle Windows

How to Install Lombok for Java Eclipse with Gradle (Windows) Guide

How to Install Lombok for Java Eclipse With Gradle (Windows)

Farman Shah


April 21, 2020

In this guide, i will explain how to install lombok for java eclipse with gradle on windows. Dont forget to ask any question in comments.

How to Install Lombok for Java Eclipse with Gradle Windows

As per Project Lombok, “Lombok is a java library that automatically plugs into your editor and builds tools, spicing up your java. Never write another getter or equals method again, with one annotation your class has a fully-featured builder, Automate your logging variables, and much more.

This blog focuses on Lombok installation into Eclipse IDE on Windows and additionally features the steps specific to the Gradle Apps.

Step 1

Download lombok.jar file from here:


Step 2

Open terminal and change directory to the path where the downloaded file is located.


Step 3

In the terminal, run this command: java -jar lombok.jar

With the above command, an installer will open and would auto scan for the installation of Eclipse IDE. If you already know the IDE installation location, provide it by clicking the “Specify Location” button.


Step 4

Once the wizard has located the IDE installation location, click the “Install button”. Close the Installer when done.


Step 5

Restart the Eclipse IDE if already running.

Steps Specific to Gradle


Step 6

The Installer will auto-add the dependency to your build.gradle file. If it didn’t, open your build.gradle file and add the following dependencies.

repositories {




dependencies {

// other dependencies

compileOnly 'org.projectlombok:lombok:1.18.8'

annotationProcessor 'org.projectlombok:lombok:1.18.8'

// other dependencies


Sync the Gradle build.


Step 7

Run the App.

That’s all. Now you can use Lombok’s annotations to save your efforts and reduce the overall size of your code.

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best machine learning courses

Best Machine Learning Courses 2020 List – Learn Via Online Programs/Class

Best Machine Learning Courses 2020 List - Learn Via Online Programs/Class

Muhammad Imran


April 20, 2020

We have come up with some of the best machine learning courses available online (free and paid). All machine learning programs listed below are chosen for quality and richness of content, as well as, customers’ reviews and ratings.

best machine learning courses

Machine learning has become the talk of the tech industry, disrupting traditional businesses with automation. With strong roots in statistics, it has become a core technology with the potential to impact all aspects of human life including personal, professional and the way we communicate with others. That’s one reason why industries and businesses are after machine learning software as a way to integrate the technology into their existing systems and processes.

From fraud detection to automated chatbots and from spam filtering to ad serving, machine learning is slowly redefining all aspects of everyday like. And that’s just the beginning, the tech that helps scientists build mathematical models or allow businesses to understand customers’ behavior pattern is none other than machine learning. In short, while most people may not notice, in reality, machine learning solutions have already taken over our daily lives and allowed us to do things that otherwise would be impossible to achieve.

But, what’s the best machine learning courses that can help you get started with the technology?

Well, in this blog, we have come up with some of the best machine learning courses available online (free and paid). All machine learning programs listed below are chosen for quality and richness of content, as well as, customers’ reviews and ratings.

It is important to note that unlike data science courses that cover various subjects like communication, exploratory data analysis, visualization techniques, and others, machine learning courses are more rigidly limited to the development of algorithms, the mathematical working of algorithms and the ability to utilize them in a programming language.

So, let’s now start our list of best machine learning courses available online:

10 Best Machine learning Courses and Certifications Skills Programs for 2020

  1. Professional Certificate Program in Machine Learning and Artificial Intelligence
  2. Machine Learning with TensorFlow on Google Cloud Platform Specialization
  3. Machine Learning Stanford Online
  4. Professional Certificate in Foundations Of Data Science
  5. Certification of Professional Achievement in Data Sciences
  6. eCornell Machine Learning Certificate
  7. Certificate in Machine learning
  8. Harvard University Machine Learning
  9. Machine Learning with Python
  10. Machine Learning at Udacity


5 Top Free Machine Learning Online Courses and Certifications Skills Programs for 2020

  1. Free Machine Learning Course (
  2. Machine Learning Course by Stanford University (Coursera)
  3. Free Machine Learning - Artificial Intelligence Course (Columbia University)
  4. Free Machine Learning Introduction Course (Udacity)
  5. Free Machine Learning Data Science Course (Harvard University)


Pre-Criteria and Pre-Requisite Before Taking Up any Machine Learning Course/Program

Well, if you want to learn machine learning online, the first prerequisite is to choose a machine learning class that delivers quality content that’s strictly focused on the development and implementation of machine learning algorithms. Here I have come up with well-defined criteria that can help you choose the best machine learning programs online and develop your first machine learning algorithm.


The best online machine learning courses should:

    • Firmly focused on development and implementation of machine learning algorithms
    • Must use open-source language like Python, R Packages
    • Must use open-source libraries of the programming languages (courses using commercial packages should be discarded altogether from consideration)
    • Must be based on hands-on experience with programming assignments
    • Explain the background mathematical working of algorithms
    • Be executed at self-paced
    • The instructors must deliver lectures in engaging ways
    • The lectures should be interactive
    • Have above average-rating for the program at forums and other aggregators

Now, once you apply all these filters, you will be left with some high-quality machine learning programs, which you can screen further to choose the best online course that’s worth your time and money.

To even fasten the pace of your learning the machine learning, you should read some comprehensive machine learning books, which will further strengthen your understanding of the technology and greatly boost your capacity to learn algorithm development.

Here we have come up with two great machine learning books, which you should check out before taking up online machine learning class.

This is an amazing book that brings much-needed clarity to the complex mathematical intuitions required for the development of machine learning algorithm development. The book is written in a straightforward approach and covers great details on the theory side of the things. Also, there are some exercises and examples to give a better understanding of using the R programming language.

This is yet another great machine learning book, which also nicely complements the previous book we discussed above. This book takes a more practical approach by detailing the application of machine learning using Python. This machine learning book together with some of the other books that are listed below will certainly help you to enhance your programming capacity for the development and applications of machine learning algorithms.

Best Books on ML and AI

Here are some other great books on artificial intelligence and machine learning; especially suited for beginners that will nicely complement the machine teaching class to build machine learning skills.

Machine Learning Yearning

Any list for the best Artificial Intelligence and machine learning books will be incomplete without mentioning this book by the legendary Andrew Ng. The book has become one of the most popular and widely read books on machine learning and AI.

The book is written in a manner to help readers enhance their skills to create AI systems. Taking an interactive course, the book helps beginners to understand various factors that are needed to be taken into consideration for machine learning projects. Andrew Ng. is definitely by far the best author that you should read to start your progress towards AI and ML algorithm development.

The Hundred-Page Machine Learning Book

Author: Andriy Burkov

What makes this book unique is how easily the author has been able to bring clarity to the otherwise complex equations and masterfully summarize difficult topics in some 100-odd pages. The book is written succulently which opens up the complex theories and mathematical intuitions involved in machine learning for the easy understanding of beginners.

 Programming Collective Intelligence (PCI)

PCI is yet another great resource, ideally suited for beginners to start their learning of complex machine learning algorithm development. It’s also a highly recommended book, endorsed by some of the most famous and established data scientists from around the world, a lot of whom have themselves read the book multiple times.

Interestingly, this book predates the era when machine learning and data science became the cult, still the topics covered in the books like collaborative filtering techniques, Bayesian Filtering, Support Vector Machine, and others are extremely relevant even today.

Machine Learning for Hackers

Written by Drew Conway and John Myles White, this book covers in detail the data analysis in R. Written with great clarity, this book is ideally suited for beginners with limited knowledge and wanted to excel in R programming in data wrangling. The books also detail interesting case studies to help readers with clarity and a better understanding of the application of machine learning algorithms.

Machine Learning by Tom M Mitchell

Once, you are done with the above-mentioned books, you can now dive into greater depths of machine learning. And this Machine learning book is ideally suited to start your advanced learning journey in machine learning. The books detail quite nicely the ML theorems, while also offering pseudocode summaries and case studies as an example for easy understanding of the readers. This is a highly recommended book by experts, who endorse it for the richness of the content, and interactive learning experience.

Top 10 Machine Learning Certification

Now, let us see in detail some of the best machine learning courses available online that can help you boost your career:

1) Professional Certificate Program in Machine Learning and Artificial Intelligence by MIT

It’s a highly recommended course for undergraduates, as well as, for professionals looking to boost their careers. The course is equally beneficial for individuals and businesses alike, to get hands-on experience with the power of AI and Machine Learning. With various actionable knowledge and best practices to implement, it prepares the participants to develop and implement their first machine learning algorithm. Delivered by the MIT experts, the course offers latest content and introduce participants with the state-of-the-art technologies and research required to build AI systems. The knowledge gained by the course could be put into practice by businesses and individuals to advance in cognitive technology.


May 2020


$2500 - $5500

Course Duration:


Teaching Mode:



Undergraduate degree in computer science, physics, data mining, mathematics or electrical engineering with some basic understanding of programming languages.

Key Benefits
Highly qualified MIT faculty and industry practitioners.
Develop essential skills and knowledge required to develop practical AI systems.
Interactive mode of learning, discussions with instructors about challenges posed by AI in the real-world environment.
Latest techniques and state-of-the-art knowledge in AI and machine learning.
Networking opportunity with experienced professionals from across the world.

2) Machine Learning with TensorFlow on Google Cloud Platform Specialization by Coursera

This specialized high intensity machine learning class comes with five courses designed stepwise to take participants from beginner level to expert. The initial courses gives the introduction to the machine learning technology, comprising of beginner-level lessons that introduce participants to the machine learning technology, what makes it so popular and what it is capable of achieving. Whereas the advanced courses gives you the skills and competence to build machine learning algorithms, focusing on Tensorflow which is an open-source machine learning framework. 

The course is expertly designed in an interactive manner to train participants in development of machine learning algorithms, solve numerical problems and understand the work process that goes behind the development of ML models. The training course also include various assignments, offering excellent opportunity to participants to get a hands-on experience to build skills on ML model development leveraging the features available on Google Cloud Platform.


Every 2 Months (Coursera)


Financial Aid Available

Course Duration:

9 Weeks

Teaching Mode:



Computer Science or Engineering Discipline

Key Benefits

A comprehensive online course that takes you from beginners’ level to the development of your first ML model.

Develop first machine learning models and learn how to scale your models in Tensorflow. 

Hands-on assignments and labs to enhance your skills using Google cloud platform.

A great opportunity to share your learning and projects with Google and Publicis to be considered for direct hiring opportunities.

Specialization certification that can be shared with potential employers and professional network for better hiring opportunities.

3) Machine Learning Stanford Uni Online

This is one of the best machine learning courses that gives the overview of the statistical pattern recognition and machine learning. The program details the differences between supervised and unsupervised learning algorithms, as well as, reinforcement learning and control. The course also gives the introduction to the latest machine learning design, development and applications.


August - September



Course Duration:

3 Months

Teaching Mode:



Computer Science or Engineering Background

Key Benefits

Introduction to the fundamentals of machine learning.

Generative learning algorithms.

Learn algorithms evaluation and debugging.

Value and policy iteration.

4) Professional Certificate in Foundations Of Data Science by Edx

This is a unique machine learning program that brings in new angle to explore issues and problems in ML. the program offers guidance to build skills in combining data with python programming skills, which can then be used in any field of study or job. For data science professionals, the program offers great resources to learn analysis of real data sets, including geographic, economic and social data. The course also include learning of inference, which is essential is quantification of uncertainty and accuracy measurement of estimates. All of the knowledge is smartly packaged together to teach prediction and forecasting using machine learning.


2-4 Months on Edx



Course Duration:

4 Months (Self-Paced)

Teaching Mode:



This course is specifically designed for beginners who do not have any computer or statistics background and no programming experience

Key Benefits

Assess  estimates by critical thinking from incomplete information.

Hands-on learning for analysis and visualization in Python 3.

Computational thinking and data analysis skills.

Prediction and forecasting based on machine learning.

Data interpretation using real-world examples.

5) Certification of Professional Achievement in Data Sciences by Columbia Uni

The course is a complete package that covers various courses including algorithms for data science, exploratory data analysis, machine learning for data science, probability and statistics. This course is ideally suited for participants with some basic skills in programming, calculus, statistics and linear algebra. The course enable students to enhance their skills in machine learning and improve chances to move up the professional career.


Year Round



Course Duration:

12 Months

Teaching Mode:

Online and Campus


Undergraduate degree with prior quantitative coursework for calculus, linear algebra, statistics and programming skills

Key Benefits

Detailed overview of the computational thinking using Python.

Develop skills to use inferential thinking to come up with predictions about unknowns. 

Learn machine learning for pattern identification with focus on regression and classification to make better predictions.

6) eCornell Machine Learning Certificate

As one of the best machine learning programs available online, the Cornell’s certification program equip students with the knowledge and skills to develop and execute machine learning algorithms using Python language. The program help students to use the math and intuitions to solve complex machine learning problems and develop mental models as a mean to understand the professional data scientists’ approach to solve these problems programmatically. Some of the machine learning algorithms covered in the programs includes; regression trees, k-nearest neighbors, naïve Bayes and others. 

The course also enable students to apply algorithms on real-data, as well as, practice debugging to enhance the models through SVM and other methods. The course also give introduction to the working of neural networks, covering essential topics to equip students with the skills to develop and adapt neural networks from different data sets. The program use open-source Python programming language and Numpy library for exercises and assignments.


Year Round


$3,600 or $565/Month

Course Duration:

3.5 Months

Teaching Mode:

Online and Campus



Key Benefits

Learn the essentials of machine learning to develop models and perform debugging.

Create face-recognition system.

Execute naïve Bayes algorithm.

Estimate probabilities distribution from various data sets.

Develop spam filter for emails using linear classifier.

Learn bias-variance trade-off to enhance the estimation and accuracy of algorithms.

Train a neural network.

7) Certificate in Machine Learning

This is a three-course certification that covers all aspects of machine learning. The program explores various machine learning concepts including statistical methods and probability which forms the foundation of machine learning algorithms. The course takes a practical learning method to implement machine learning concepts using open-source tools, while also assisting in developing judgement and intuitions to prepare students for real-world challenges.


Year Round



Course Duration:


Teaching Mode:



Not Available

Key Benefits

Concepts of probability, statistical analyses, mathematical modeling, and optimization techniques.

Supervised and unsupervised learning models for tasks such as forecasting, predicting and outlier detection.

Advanced machine learning applications, including recommendation systems and natural language processing.

Deep learning concepts and applications.

How to identify, source and prepare raw data for analysis and modeling.

8) Harvard University Machine Learning

This machine learning course discuss in detail about principal component analysis and other widely used machine learning algorithms used in various industries. the course also explores the techniques to train datasets and develop predictive relationships, as well as, teaches overtraining techniques like cross validation.


Year Round



Course Duration:

8 Weeks

Teaching Mode:



ML Engineer

Key Benefits

Learn fundamentals of machine learning.

Learn to perform cross validation.

Learn different industry use machine learning algorithms.

9) Machine Learning with Python by IBM

The program covers the basics of machine learning technology using open-source Python programming language. The program content can be divided into two broad components, first where you learn about the purpose of machine learning and second where you learn the application of machine learning to the real world.

Key Benefits

Learn the basics of machine learning.

Develop skills in specific machine learning algorithms like SciPy, regression, classification etc.

Certificate of proficiency in machine learning for better hiring opportunities.

10) Machine Learning at Udacity

This course covers two distinctive model, supervised learning and unsupervised learning in machine learning. For supervised learning, you get to develop model for email spam filtering and voice recognition, whereas, for unsupervised learning, you learn how Amazon knows what you want to buy or Netflix knows which movies you will like.


Year Round (Udacity)


Free Trial

Course Duration:

4 Months

Teaching Mode:



Not Available

Key Benefits

Supervised Learning for beginners.

Unsupervised Learning for noobs.

Reinforcement Learning.

Best Cheap Machine Learning Courses/Programs Alternative


How to follow Andrew ng machine learning course programming on python?

Here’s what you can do:

The course will include the programming assignments for Octave or Matlab, however, you would have to execute the equations in the assignment for the algorithms, which you can easily complete in Python using Numpy. Which means that you can essentially implement the assignments in the Andrew Ng. machine learning course in Python using Numpy. However, this would be more difficult, as here you would have to come up from the scratch for everything that’s needed to be done.

You should also start exploring the Scikit-learn, which completing the course. The Scikit-learn is a ML library in Python and comprise of algorithms, which you can import and use on different datasets you get in the course.

Which computer science course is best for machine learning?

Most employers looking for a ML expert would like to have applicants with a Masters’ degree in computer science, mathematics or related disciplines with proven experience in development and implementation of machine learning algorithms.

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best languages for machine learning

Best Languages for Machine Learning 2020 (Updated List)

Best Languages for Machine Learning in 2020 - Updated List

Muhammad Imran


April 13, 2020

There are many languages readily available for a engineer to pick and start developing their own work. Checkout the best languages for machine learning in 2020.

best languages for machine learning

Artificial intelligence is an art of creating intelligent systems through science and engineering. These intelligent systems can be in the form of hardware, such as robots or robotic arms, that could help in performing repetitive tasks, as software, like machine languages, algorithms, or a mix of both. For example, autonomous machinery that can take its own decisions based on certain events.

Machine learning is a part of artificial intelligence, whereby computer programs are developed, which can help in analyzing available data to assess trends and experiences. Folio3 provides machine learning solutions, along with the best languages for machine learning. To find out more information on machine learning, and artificial intelligence itself, you can also search many other websites, for example, Expert Systems, that break down and define the terms in an easy language for a better grasp.

Data Mining is also a branch of learning about technologies and applying it scientifically to different situations being presented to us. In its actual terms, it is meant to describe the extraction of important information from a large pool of available data. It works in different patterns in different events and is an extension of the business analytics that is present in the world today. Data mining works alongside machine learning as a service to construct different models to see how the available information can especially help in predicting the future.

The Top Most Popular Languages Trend for Machine learning and Artificial Intelligence in 2020

The interest of a topic is gaged by how popular a certain term has been in web searches. We have calculated the results, through Google trends, for the past 12 months and have colour coded the results for a better understanding of the viewers.

According to calculated trends, over the past 2 years, R peaked the most over the whole year and has proved to be the most popular, by touching the 100 (maximum points) bar quite a few times. This means the people have searched mostly for R when it comes to programming languages.

Following R, the next two most commonly searched for items are Python and Java. This shows that after R, Python, and Java are the most popular programming languages as the trends have presented.

Analyzing the trends have also brought into light that Lisp does have searches on it, though, they are almost next to nothing, making it not too popular of interest among people. And lastly, we can see that Prolog is a deadline, still at zero, and has had no search for it over the past twelve months, making it totally unpopular in these past two years.

best languages for machine learning trend

Regional Interest

This trend analyzes all the areas where a certain search term is the most popular. Some countries search for a certain term more than others, and we can also see which terms are most commonly searched for.

top languages for machine learning

What we have analyzed looking at the areas presented above is:


Python is most commonly searched for in countries such as Israel, Taiwan, Russia, Singapore, Hong Kong, South Korea, Norway, Iran, Ukraine, Switzerland, and many others. As it is also quite visible, Python is extremely popular all over the globe, and most countries rely on searches related to Python for their work.

Another analysis that is presented to us is the key terms or the search keywords that people use across the world to search for Python. Some essential key terms related to Python are: “python for,” “python list,” python string,” “python with” and “python if” that have been searched for the maximum number of times, as these have been given a more than 50 weightage on a scale of 1 – 100. 

There is also a list of key terms that are on the rise in the searching area, and for python, these terms include: “python 3.8”, “monty python terry jones,” “new colt python,” “google colab” and “geeksforgeeks python.” These words are being searched in multi-folds and are still on a rising scale, because, as we can see, people know a lot about python already and are not new to it, they just need further information on how and where it is used today.


The countries where R is the most popular search are Thailand, South Africa, Philippines, Canada, Brazil, Indonesia, Italy, United Arab Emirates, Australia, and New Zealand, among a few others. These areas search for R as a term more than even Python. It is found to be the second most searched term in the world.

The certain key terms related to R that people use to search for it are: “type r,” r and r,” and many others. Some rising keywords that are being searched the most and have kept on increasing are: “xhamstervideodownloader apk for mac download r,” “insta360 one r,” among others.


Lisp is one of the very lowly searched items and is not quite popular in any certain country of the world. Yet, some of the countries where it is searched and is known are Canada, South Africa, Bangladesh, India, Philippines, Singapore, Israel, South Korea, Nigeria, and the United States of America, though, in China, it is has a search interest of complete 100. The trends do show that the Asian continent is more popular, with regards to Lisp, than the others.

These countries use these key terms to search for when looking for material related to Lisp: “autocad lisp,” “common lisp,” “lisp meaning,” “lisp programming,” “lisp language” amongst other terms. These keywords actually depict how people want to find out more about lisp, and are relatively new to this term, leading to them trying to search about it. The rising in popularity keywords, however, are: “lisp tính tổng diện tích,” “lisp cắt dim” and others in the same foreign language, which makes it even more evident that it is only popular in some specific regions.


As the interest trends over time showed, Prolog was hardly ever searched, gaging the least amount of audience on the web. People usually do not even search for it as such. Though some countries need to search upon it, for example, Indonesia, Peru, Serbia, Canada, South Africa, Bangladesh, India, Philippines, Singapore, and Israel. Again, we can see a trend that the major technology hubs are not interested in this term; rather, the developing countries and regions that are on the growth stage are searching for it.

Some of the keywords people search for, regarding Prolog, are: “online prolog,” “prolog program,” “prolog programming,” “prolog download,” and “prolog language.” This, again, like Lisp, shows that people are new to this language and need to find out more about this term. The terms that people are more interested in for Prolog are also not that many, and not quite related to the language itself, giving the idea that it is not too popular, and if it wants to succeed, it needs to work in a better way, and give customers what is needed from these machine languages.


Being the 3rd most popular from the group of languages listed above and depicted in the map, Java mostly has its popularity in regions such as Bangladesh, Nigeria, Vietnam, Kenya, Ukraine, Serbia, South Korea, India, Hungary, and Morocco. This shows a good spread of its search over the web, and its popularity is still seen to be on the rise.

Java has a lot of key terms that it has been searched by, for example: “string java,” java download,” “java array,” “list java,” “java program,” and “java minecraft.” Some keywords that are rising in searches since last year, for java, are: “java jazz 2020”, “minecraft java edition,” “tlauncher,” “java minecraft” and java 11 download”, etc. These terms show that java is needed by most people to either play a program in their machines or play some games where java is required as a plug-in, more like in a supporting program way, though it is pretty popular around the world.

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The Top 10 Best Languages for Machine Learning

There are many programming languages readily available for a person to pick and start developing their own work. For machine learning and making, too, these programming languages are highly essential. Whether it be gaming software, artificial intelligence works, or any other computers and gadgets related to technological work, one has to know programming languages. Checkout our best machine learning r packages if you want to learn a programming language, especially R, for web development, with more than 20 options to choose from.

Although, we do understand that some people may not want to learn a language on their own, which is why there is always an option of outsourcing the work. Folio3 predictive analytics solutions are available for machine learning and cater to multiple different aspects of it, such as Product Conceptualization, Predictive Engineering, Maintenance Analysis, Design, Automation, Data Acquisition and Analysis, Product Planning, Utilizing Operational Data and many other services that pertain to this certain field. They also help in finding which best learning software and tools are available for the developer this year.

Machine learning helps technological advancement and development in many different sectors of an economy, for instance, the Banking Sector, Health Sector, Transportation Sector, Trading Sector, Food Sector, and of course, the IT sector itself.

Different programming languages are used for different types of machines that are performing different kinds of tasks. The top 10 best languages for machine learning are as follows:

1) Python

Python is an open-source platform that provides a tremendous amount of community support, and has an extensive library, dealing with a multitude of different systems. It can scale different complex applications and lets the user test their ideas and build their prototypes in the least amount of time. The only drawback it may have is that it is slower and not preferred when it comes to mobile computing. Otherwise, it is the number one choice for machine language around the globe, and the most preferred to use by millions of developers and big companies.

Python has another higher level of a framework known as “Django.” This is also an open-source and completely free for people who want to start working on it. It is an all-inclusive framework that has everything available for you under one roof, and it encourages rapid development and a platform for pragmatic design. Many big applications nowadays use this framework for their development, such as Mozilla, Pinterest, National Geographic, and many more.

2) C++

C++ is a machine language that is extremely efficient in resource management and is super easy to use for new developers or even professional ones. It is an extension of the former “C” language, but a step higher this time. It was initially developed for embedding and system programming on a larger scale, with performance, flexibility, and efficiency being its core values, though, over time, it has developed into many other key areas of computing and software.

C++ provides a software infrastructure that is resource-constrained, which is much faster than other languages and gives the developer the opportunity to work on multi-device and multi-platform applications. It runs close to the system hardware and has a support backup in case of function overloading and real-time mathematical simulations. Although, it does not work in run-time checking, strict type checking, garbage collection, and has a smaller library.

Some opinions of the C++ language also indicate that it is a not a great choice for the back-end functioning of a website, and only in a few cases would it work, for example, if an extremely efficient type of resource management is required.

Banks could use this kind of programming for their databases, though, where calculations are the essence of the job. Even Google and Amazon could well use C++ for their backend development, but some areas where it has really proved successful are the video games sector, SQL servers, and desktop applications.

3) Java

Java is another great name after Python in the development area, used worldwide as one of the best languages for machine learning. It is not only easy but also object-oriented and has a general-purpose outlook to it. A Java compiled code can usually work on any Java application or virtual machine and does not need any specific underlying computer system. Last year, it was termed as one of the most popular languages in client-server applications, with a net of 9 million developers working on it.

Java is not only fast, but it is also reliable and highly secure. Many of the applications around us do not even work unless you have Java installed in your computing facilities. Many large companies have already adopted and are working on Java, as is the Android App Development, which shows how big of a market value Java has, that it is used in one of the most used application systems.

It has an abundance of open-source libraries, a stack, and an automatic memory allocation system, has a security manager that accesses and keeps a check on the classes, supports the concept of multithreading, and is an ideal source for distributed computing. The only negative features of Java are that its memory management is expensive and does not have templates available for data structures, but the JVM feature gives it a high degree platform of independence.

Java is sure to bag you the best and most number of job opportunities, and it provides a solution to every possible problem existing within its ecosystem. As already mentioned, Android is using Java, so is it an important back end for the Oracle platform. Thus, using Java is not only beneficial for the developer but also for the company itself.

4) JavaScript

JavaScript (JS) is primarily, and best used as a front end language in the programming world, and is used to make interactive and creative front end applications, such as pop-ups that may show when you click a button or open a website. NodeJS is a run-time environment based on JavaScript, which is widely in use nowadays and lets the developer in creating dynamic content for webpages before it is sent to the user end.

JavaScript is a first-class programming language that is light in weight, helps in scripting for web pages, and functions as both, object-oriented, as well as a procedural language. It also visualizes the results of machine learning through dashboards. With JS, you can use a single programming language for your back end, as well as front end work, making it much easier to handle on the whole.

This can cause server demand to decrease, which is an advantage, along with JS having high versatility, speed, simplicity, a platform to exercise diversity, and it is friendly when used with other programming languages. Although, different browsers interpret it differently, as do the users who disable the JavaScript with fear of being hacked or bugged and not so readily welcomed as other front or back end machine learning languages.

5) C#

C# was developed by Microsoft as a general-purpose programming language and got fame in the 2000s for supporting object-oriented programming. It is one of the widely used languages for backend frameworks as well, and for the .NET framework, it has claimed to be one of the most powerful languages. 

It has a great ability to work along shared codebases; it is automatically updateable, has a syntax that is similar to C, ideal for Windows development of all kinds and has a very quick execution and compilation time. Though some negative aspects it does have attached are the unsafe blocks, has memory deallocation and no garbage collection, is less flexible as compared to other languages, takes time to learn and work with, and error resolution takes up a lot of effort and time.

It is well suited for applications on Windows and even its mobile apps, along with working on Android and iOS, but for that, it takes help from the integrated development environment. It can be used to build games using Unity, and many popular websites like Dell, Bing, and Visual Studio currently use the C# programming language at their backend.

6) R

The popularity of R has been proved earlier in the article as well, and we would still emphasize on R being one of the most famous and commonly used languages for programming around the world. It is especially used in machine learning and data analysis and is capable of creating powerful and great learning algorithms and frameworks, as it also works on graphics, apart from statistical computations.

It seamlessly works on different operating systems and is an open-source and free language. Being highly extensible, it is also very comprehensive when it comes to statistical analysis and provides a highly powerful ecosystem. Although it does lack security features, with no strict guidelines for programming, its memory management is poor, and some packages offered by it are not up to the level at all.

R is ideal to be used by people who want to join big companies for analytics purposes or by statisticians who want to compute data such as regression analysis, classifying information, and decision trees, with also high demand in the bioengineering and bioinformatics fields. One time projects like reports and researches could make good use of R.

7) Julia

Julia was always designed to be a high performance and fast language. It is highly efficient, works on multiple platforms, and is quite interactive and dynamic in terms of scripting. It is easy to express patterns on it, which are functional and object-oriented too, because it has a multiple dispatch mechanism.

Julia is very easy to use for all levels of developers because it has a high level of syntax, which makes it easily accessible and understandable. It is open-source and free, under the license of MIT, and all information and source codes for it are available on GitHub. Though, debugging on it is a problem of its. Moreover, Julia’s allocation and garbage collection is low, so is the memory management system it possesses.

As was the case with JavaScript, even Julia can be used for both the server-side and the client-side interface. Again, as it is highly efficient in computational analysis and numerical science, it is best suited for statisticians, the field of analytics and bioinformatics.

8) GO

Go is also widely known as Golang, and is similar to C++, having fantastic speed, and compiles to machine code, making it even faster. It is relatively simpler to other languages, can do great work in the concurrency area, and over the past few years, has gained quite a mindshare.

The advantages Go has is that it is backed by the Google company itself, is easier to learn due to syntax and more secure, and has a smart method of documentation. But it finds it difficult to work on complex programs, has next to zero versatility and library support, and as much as the working environment seems exciting, it is equally hectic and chaotic, too.

There are also, however, not many varieties of packages available, and designing a stable architecture of the server is pretty difficult. It is also not as common as Java and is only mostly seen being used by the startups of Silicon Valley.

9) ErLang

Erlang is another programming language used to build soft and real-time systems that are massively scalable. The requirements of it would be on high availability, and it is open to working with other languages.

Erlang has an extensive library and designing principles that help develop systems easily and in lesser time than other languages. It has a distributed database, along with debugging tools and better memory management. Though, it still tends to be slower than other languages in terms of providing solutions and is mostly only useful for bigger projects.

Most of the users of this machine learning language pertain to the telecommunication industry, the banking sector, e-commerce, computerization, and instant messaging. Without a doubt, it is the same programming language that is used for applications that use concurrency, for example, WhatsApp, for whom it works alongside another rlanguage known as Elixir.

10) Scala

Scala is one of the main languages that have the support of the Apache Spark platform. It is a comprehensive region and proves appropriate libraries for the big data processing, along with its functionalities and analysis of machine learning. Using Spark, Scala makes the development, designing, coding, and deployment of machine learning algorithms in the best possible manner.

Like many other programming languages, Scala is also well-versed in computations and arithmetic, with having good control over number generation, linear algebra, and other scientific computing. Though its allocation methods are not efficient, with no great memory management, making the whole system suffer, it uses the virtual machine of Java in run-time, and so is faster than even Python.

For these advantageous reasons, Scala is a popular language and is increasing in its popularity with companies dealing in machine learning and big data. Some data libraries worth mentioning for Scala are the Saddle, which helps it deal with automatic alignment of data, filling in missing values itself and support data manipulation in 2D structures. Aerosol is another one of its famous libraries that is user-friendly and is known for a high speed due to being a GPU and a CPU – accelerated library.


Best Languages for Machine Learning on Reddit

Best languages for machine learning on Reddit

As proved by the above comments gaged from Reddit, Python is the most recurring name, which is seen, time and again, as the first choice of most people. From our earlier trend analysis, as well as from sources that have listed the top 10 best languages for machine learning, and now from these comments as well, we can clearly see that Python is the most famous programming languages of all, though, C++, Julia, and R are not so far behind, as some people have referred to them as their preferred choices too.

Best Backend Languages for Machine learning

The backend of any system is the server-side of the website that cannot be seen, but does it work in the background. The backend is important for organizing the data, storing information, and keeping everything in working condition at the front side for the client. It is constantly working with the front end in sending and receiving information for smooth running. The top 10 backend best languages for machine learning are as follows:

  1. Python
  2. Java
  3. JavaScript
  4. C++
  5. PHP
  6. Ruby on Rails
  7. Rust
  8. Golang
  9. C#
  10. Erlang


What functional language is best for machine learning? 

Python has been declared as one of the top and easy-to-use programming language across the charts for the best languages for machine learning, having 57% of the world’s data scientists and machine learning professionals using it commonly, as declared by multiple sources. You can find it in desktop apps, in network servers, in web applications, and many different areas, and is said to keep on growing further in the programming language region.

As described above in the article, Python is an easy-to-use language, which gives you a great platform to create your own systems and backend framework, as well as the opportunity to test your idea in a short span of time. So if you are looking for quick solutions to your ideas and want to develop something great and are short on time, Python is your best bet. Though, R, Java, and C++ are also readily available free options.

Is C++ good for machine learning beginners? 

C++ is a great language for developers who are steady in web development and already have the experience, but for beginners, it is not. Even though it is flexible and reliable, it is still built for large scale purposes, and beginners should not jump that high at their first go. Also, C++ is prone to bugs and debugging it becomes a problem for beginners, as it consumes a lot of time and effort. In addition, the C++ machine learning language is very fast, but a beginner would not be that fast in his work, making the action slower, which means there is no use of using the fast C++ language in the first place. People prefer Python or R as the best languages for machine learning for beginners.

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Gradient Descent Optimizer – Regression Made Easy Using TensorFlow

Gradient Descent Optimizer - Regression Made Easy Using TensorFlow

Muhammad Imran


April 07, 2020

An amazing tool for machine learning, gradient descent optimizer can reduce function by repetitively moving in the direction of descent that is steepest.


A big aspect of machine learning is optimization. Almost all machine learning algorithms have optimization algorithms at their core. In the present age, businesses are able to enjoy benefits by using machine learning solutions so as to enhance their capabilities as well as by using highly customized machine learning algorithms, organizations are enabled to maximize their functionality.

There are many benefits that machine learning solutions can provide such as speech-to-text, computer vision applications to scale up visual data analysis, natural language processing to drive better values from your data, predictive analysis for making smarter decisions, etc.

Gradient descent optimizer is an optimization algorithm that is basically used so as to reduce some functions by repetitively moving in the direction of descent that is steepest as explained by the gradient’s negative. We make use of gradient descent optimizer for machine learning to update our models’ parameters i.e. weights in neural networks and coefficients in linear regression.

To simply put TensorFlow gradient descent optimizer is used to find out the values of coefficients (parameters) of a certain function (f) which reduces a cost function (cost).

The use of TensorFlow gradient descent optimizer is best when the calculation of parameters cannot be done analytically, such as by using linear algebra, and it becomes necessary to make use of an optimization algorithm to search for their values.

How to Setup Regression Using Gradient Descent Optimizer in Tensorflow

First thing first. Load the necessary libraries.

Follow this URL for complete code.

How to Load a Dataset from Gradient Descent Optimizer in Tensorflow

Load data sets and run checks on stats.

Now follow this URL for complete data set code.

How to Build Your First Model

You want to predict the salary which will be your label. So, you will use projects as your input features and in order to train your model, you will use the linearRegressor interface that is given by the TensorFlow Estimator API. Many low-level model fixings are usually taken care of by this API and it provides suitable methods for training your model, performing evaluation and inference.

Step 1 – Defining Features and Configuring Feature Columns

There is a construct called feature column in TensorFlow that is basically used to indicate the data type of the feature. They do not have already added feature data.

To begin, you will use a single numeric input feature i.e. projects.

Coding in this ref link.

Step 2 – Defining The Target

In this step, you will state your target i.e. salary. This can also be extracted from your data frame.

Step 3 – Configuration of the LinearRegressor

Here you will configure a linear regression model by using the LinearRegressor, and this model will be trained by using gradient descent optimizer which is responsible for implementing mini-batch Stochastic Gradient Descent (SGD). The size of the gradient step is controlled by the learning_rate.

Step 4 – Defining The Input Function

In order to perform salaray data import into LinearRegressor, you are required to define an input function. This is actually to instruct TensorFlow about how to preprocess the data and also how to shuffle, batch, and repeat it during the training of the model.

Begin with converting your pandas feature data into NumPy arrays. After which you can use the TensorFlow dataset API for constructing a dataset object from your data followed by breaking up of the data into batches of batch-size that are to be repeated for a certain number of epochs (num_epochs).

Moving forward, if shuffle is set to True, you are required to shuffle the data so as to pass it randomly to the model during its training. The buffer_size argument is responsible for specifying the size of the dataset from which shuffle will sample randomly.

Lastly, your input function will be done constructing an iterator for the dataset and gives back the next batch of data to the LinearRegressor.

Step 5 – Training The Model

For training your model, you can call train() now on your LinearRegressor. You are required to wrap my_input_fn in a lambda so that it can be passed in my_feature and targets as arguments. To start, you will train for a hundred (100) steps.

How to Optimize Model in TensorFlow?

For this function, we are going to move forward in ten fairly divided periods in order to understand the improvement of the model in a better way.

For every single period, we will be computing and graph training loss. It helps to know when a training model is converged and whether or not it needs any more iterations.

Plotting feature weight and bias term values that are learned by the model can laso help you to understand the process of convergence better.

Take Away

Some essentials you must always remember:

  • Optimization is the most essential aspect of machine learning.
  • Gradient descent optimizer is an optimization algorithm that can be used with various machine learning algorithms.
  • The use of gradient descent optimizer is best when the calculation of parameters cannot be done analytically, such as by using linear algebra, and it becomes necessary to make use of an optimization algorithm to search for their values.

If you have any questions about TensorFlow Gradient Descent Optimizer for machine learning, contact us today and we will address your concerns right away.


What is convergence in TensorFlow ai?

It refers to a process or state that is reached during a model training where validation loss and training loss experience some changes or no changes at all with every single iteration after a specified number of iterations. Simply put, a model reaches the convergence state when training it any further on the existing data stops improving the model any more. In deep learning, the values of loss oftentimes stay constant even after many iterations before they start to descend. This can be misleading by giving you a false sense of convergence.

What is TF train Adam optimizer for machine learning?

TensorFlow train Adam optimizer is basically an optimization algorithm that can be used in place of classical stochastic gradient descent TensorFlow in order to update network weights iterative based in training data.

These are some benefits of using Adam optimizer:

  • Straightforward in terms of implementation.
  • It is computationally efficient.
  • Its memory requirements are little.
  • It is best to use for problems that are bigger in terms of parameters and/or data.
  • Suitable for objectives that are non-stationary.
  • Suitable for problems that are noisy or sparse gradients … and so on.

What is the TensorFlow optimizer for beginners?

TensorFlow is known as the second machine learning framework that is created by Google and it is used to build, design, and train models involving deep learning. TensorFlow library can be used to perform numerical computations that are done using data flow graphs.

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HoloGAN – Unsupervised Learning of 3D Images

HoloGAN - Unsupervised Learning of 3D Images

Muhammad Imran


April 06, 2020

HoloGAN is a generative adversarial network (GAN) proposed by a group of researchers. It uses natural images to perform unsupervised learning for representations that are 3D.


In the present age, deep learning and machine learning solutions are enabling enterprises and businesses to level up their functionality and enabling them to proceed towards a future that depends on smart decision making and intelligent learning. HoloGAN is yet another product of artificial intelligence that appears to be bringing remarkable improvements.

HoloGAN is a generative adversarial network (GAN) proposed by a group of researchers. It uses natural images in order to perform unsupervised learning for representations that are three-dimensional (3D).

Different from all other GAN models that use two-dimensional (2D) kernels to create blurry artifacts or images for tasks that call for strong and clear 3D learning, HoloGAN depends on 3D models to showcase a realistic representation. It allows for rigid-body transformations and provides excellent control over the generated objects.

What is Unsupervised Learning?

You might already be aware of machine learning solutions and their impact. However, there are still some advancements in deep learning that are yet to be explored.

Speaking of unsupervised learning, it is basically the training of an algorithm of artificial training which is done by making use of information that is neither labeled nor classified. The algorithm is allowed to act on the given information without having any further guidance.

An artificial intelligence system, in unsupervised learning, works to group information that is unsorted based on similarities and differences without requiring you to provide categories. Such AI systems that are capable enough are mostly associated with models of generative learning. Though they will probably also make use of a retrieval-based approach (that is not associated with unsupervised learning). Self-driving vehicles, chatbots, robots, facial recognition programs, expert systems are examples of systems that will either use supervised or unsupervised learning.

In unsupervised learning, uncategorized and unlabelled data is presented to an AI system and the algorithms of the system act on that information with having no prior training. The output entirely depends upon the algorithms that are coded.

Unsupervised learning algorithms are capable of performing tasks that are complex to process and which cannot be performed through supervised learning. Unsupervised learning is however unpredictable. It can surely sort dogs from cats on its own but it might also add unnecessary categories such as their breeds which will only create clutter rather than order.

What is the Novel Generative Adversarial Network (GAN)

Generative adversarial networks (GANs) are known as algorithmic architectures that depends on two neural networks, posing one against another, so as to generate new and synthetic instances of information/data. These are widely and commonly used in image, voice, and video generation.

GANs were originally introduced by Ian Goodfellow and some other researchers in a paper at the University of Montreal, in 2014. The AI research director of Facebook referred to GANs and called adversarial training as the most amazing idea of the decade in ML.

The potential for generative adversarial networks is huge in both good and bad terms because they are capable of learning to mimic any sort of distribution of data, which is to say that GANs can create worlds very much similar to ours and in any domain including music, images, prose, speech, etc. They are robotic artists and their performance is extra ordinary. But they can also be used for creating fake and false media content.

Hologan GitHub

Here is the HoloGAN dataset for GitHub

Hologan Youtube Video Tutorial

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