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Tag: machine learning certification

How do you balance Machine Learning theory and practice?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Machine learning is no longer a technology from the future. The technology giants like Google, Facebook, Netflix, etc. have been using machine learning to improve their user experience for a very long time. Now, the applications of machine learning are growing across the industries and this technology is driving businesses worth billions of dollars. Along with the applications,  the demand for professionals with expertise in ML has grown immensely in the past few years.
So, it is indeed a good time to learn machine learning for better career prospects. A machine learning course is the best practical way to start your learning process. However, often people get too much stuck to the theory and fall behind in the practical experience. Well, it is not the best way to learn anything. This article will help you balance learning machine learning theory and practice. Read on to find out more.
Theory vs Practice 
For practitioners of ML, the theory and practice are complementary aspects of their career. To become successful in this field, you will have to strike the balance between what you read and the problems in real life. So many people avoid building things because it is hard. Building involves constant tracing of bugs, endlessly traversing stack overflow, attempts to bring so many parts together and so many more work. Theory on the other hands is comparatively easy.
You can find all the concepts settled in place and we can just consume everything as to how we wish things will work. But if it doesn’t feel hard, you are not learning anything properly. It will be a lot easier for us to rip through journals and understand the concepts, but reading about the achievements of others will not make you any better in this field. You have to build what you read and fail so many times to get an understanding that cannot be achieved by reading alone.
Build what you read
It is the one simple thing you can do to strike a balance between theory and practice. Build a neural network. It may perform poorly, but you will learn how different it is from the journals. Attend a Kaggle competition and let your ranking stare at you even if it is low. Hack together a javascript application to run your ML algorithms in the back end only just to see it fail for unknown reasons.
Always do projects. Your machine learning certification program might have projects as part of their curriculum, but don’t be limited to those. Just remember that everything you make during the learning process does not have to work. Even the failures are great teachers in this process. They will provide you with the practical experience you will need to excel in the industry.
Practicing everything you read may make it harder for you, but once you learn to volley theory and practice back and forth, you will certainly get the results better than you were looking for. Only such a balanced approach towards ML will help you make an effect on the real world problems.

What are good ideas for Hackathon in Machine Learning?

Posted on by Imarticus Learning
Reading Time: 2 minutes

 

Hackathons are not merely fetes where you can show off your skills but are also huge opportunities aimed at engaging gainfully and celebrating solving business issues and problems.

The Indian hackathons are corporate sponsored glitz-and-swag events where developers can compete and push boundaries by tackling industry-relevant issues in an environment that is supportive, has fireside learning, exposure to the latest gadgetry and quite like a convoluted career fair.

Job opportunities, internships, different vertical exposure, startup offers, mentorships, peer interactions, rights to brag and prizes abound. For the ML starters, it can be the pitch to learn on, join a community, hone skills, get ideas, and find the right tools and projects in coding, discover the best training and even get placed.

Take your pick from popular and reputed hackathons like MachineHack, TechGig, Hackerearth, Kaggle, and OpenML. Here are some hackathon ideas that can be advantageously used.

  1. Reach out to the online community through online ongoing hackathons where tech and ML beginners can participate, work on third-party APIs and resolutions and learn from the community. Alexa is being tweaked by Amazon in this manner.
  2. Permit multiple categories, levels, and submissions: Teams can participate in multiple category hackathons as individuals or by submitting multiple solutions at hackathons. This builds team spirit, allows multiple submissions in various categories and promotes working in communicative teams.
  3. A balanced cross-functional team yields better results: This secret in hackathons helps teams compete better, ensures better team coordination, provides a platform for the newcomers to work with the experienced and definitely satisfies learning for the whole team. Go for the prize with your team!
  4. Count results to be superior to techniques:  All hackathon participants should use their stack wisely and well, and showcase in the prototype their algorithm and skills in programming. Many a failure occurs with developing scalable models instead of the prototype, using complex databases, algorithms, and a limited stack to develop the prototype in the specified timeframe.
  5. Hackathons are about prizes for quality problems: Nobody expects a complete solution. What does impress is a simple tweak, innovation or prototype that has the potential to solve and provide a scalable solution?
  6. The product demo is essential: While the presentation has a bearing on the success and winning becomes addictive the product demo is crucial as it sums up the learning, efforts, and technology used. The real winners are those who compete and learn from their mistakes.
  7. Follow the hackathon code: The opportunity to learn should not cause problems for others. Follow the guidelines and conduct codes to provide a supportive environment for all.
  8. Exploit the learning opportunity: To break into the ML field you will need to do a machine learning course and get practical machine learning training with a reputed institute like Imarticus. Then move on to hackathons because these are events akin to sprints where hardware/software is tweaked over the next 24 to 48 hours. The skills and tasks are graded and provide participants with the chance to come up with quick solutions without needing code understanding. Do explore the workshops and 101 sessions on coding to help pick-up the requisite skills.

On a concluding note, there will be various platforms hosting events and hackathons both offline and online which provide participants for everyone.

How do you learn math quickly for machine and deep learning?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Synopsis
Math is integral to machine learning and deep learning. It is the foundation on which algorithms are built for artificial intelligence to learn, analyze and thrive. So how do you learn math quickly for AI? 
Machines today have the ability to learn, analyze and understand their environment and solve problems on the basis of the data given to them. This intelligence of the machines is known as artificial intelligence and the ability to learn and thrive is known as machine learning. Algorithms form the crux of everything you do in technology and a machine learning course provides you with an understanding of the same.
Today, individuals who are proficient after completely a machine learning certification are highly sought after and employed. Companies invest a large sum of money to have professionals trained in AI as the applications of AI are vast and cost-effective.  It is a lucrative career to pursue one that involves complex and challenging problems which need to be solved in creative ways.
Mathematics forms the foundation of building algorithms as all programming languages use the basics. Binary code is the heart of machines and the language used to teach them things is a programming language. So do you pursue a machine learning training, and also learn math quickly at the same time?
Data Science Course
Here are a few ways to understand how math is applicable in AI 
Learn the Basics
Important sections such as  Statistics, Linear Algebra, Statistics, Probability and Differential Calculus are the basics of math that one needs to know in order to pursue learning a programming language. While this may sound complicated, they form the basis for machine learning, so investing in courses that teach the above-mentioned functions will go a long way in programming.  There are plenty of online resources that are useful repositories when it comes to learning math for deep learning.
Invest Sufficient Time
Learning math depends on the ability to absorb and apply the math learned in machine learning. Applications of statistics, linear algebra is important in machine learning and hence investing 2-3 months to brush up on the basics go a long way. Constant applications of the lessons learned also helps when it comes to math for AI. Since the principles are the same but the various derivatives and applications can change with the algorithm constant practice and brushing up will help while learning the code.
Dismiss The Fear
One of the biggest ways to learn math quickly for machine learning is by dismissing the fear associated with numbers. By starting small and investing efforts, one can move forward in the code. Since there is no shortage of resources when it comes to learning math, taking the initial step and letting go of any fear towards the subject will greatly help.
Conclusion
Learning a programming language whose principles are based on mathematics can sound daunting and tedious but it is fairly simple once you understand the basics of it. This can be applied while programming for machine learning and artificial intelligence.

What are some really interesting Machine Learning projects for beginners?

Posted on by Imarticus Learning
Reading Time: 3 minutes

 

We are witnessing an era of the data revolution. Every organization across the world are trying to make use of data to improve their business. As a result, the demand for skilled Data scientists is skyrocketing. We know that Machine Learning is an important part of Data Science and the Best way to learn it is, of course, practicing it. Any professional taking a Machine learning course should be doing their own projects. Practicing your lessons will help you get familiar with the common ML libraries. Here are a few projects you could try along with your machine learning training.

1. Iris Flowers Classification ML Project

It is the “Hello world” of Machine Learning. This project involves classifying the flowers into 3 different species according to the size of their petals. You can use the Iris Flowersdataset which consists of the numeric attributes of each flower. This data set is considered to be the best available in this classification genre. You will have to use Supervised Machine Learning algorithms to load and handle this data. Also, you can work on this small data without any special transformation or scaling.

2. BigMart Sales Prediction ML Project

The product of this project is a regression model that can predict the sales in 10 BigMart outlets spread across 10 different cities. You can use the BigMart Dataset which consists of the sales data of 1559 products from 10 different outlets. Using Unsupervised Machine Learning algorithms, you can predict the sales of each 1559 product in each outlet.

3. Analysis of Social Media Sentiment Using Twitter Dataset

There are huge amounts of data created by our social media platforms on a regular basis. By mining these data we can understand a lot about the trends, public opinions, and sentiments going on the world. Among them, the data created by Twitter is fund to be best suited for beginners. Using the Twitter data set which consists of around 3 MB data, you can find out what is world talking about the various topics such as movies, elections or sports. This project will help you develop skills in social media mining and classifiers.

Data Science Course

 

4. Recommender system with Movielens Dataset

The modern customers are looking for more customized content everywhere. The applications like Netflix and Hulu are using recommender systems to find content matching each of their customers. This project is about making such a recommender system. You can use the Movielens Dataset which contains around 1.000,200 movie ratings of 3,900 movies made by 6,040 users. You can start building this recommender system with a World-cloud visualization of the movie titles.

5. Stock Prices Predictor

If you would like to work in the finance domain, this project is an excellent choice for you. The aim of this project is to build a predictor system which can learn about the performance of a company and forecast its stock price. You will have to deal with a large variety of data such as prices, volatility indices, fundamental indices and many more. The dataset required for this project can be found at  Quandl.

These projects will introduce you to some challenges and their solutions in machine learning. Your machine learning certification will be complete only with such a hands-on experience with ML.

 

How can you start programming machine learning and artificial intelligence?

Posted on by Imarticus Learning
Reading Time: 2 minutes

One of the biggest developments in the world of computing over the last few years has undoubtedly been artificial intelligence. The ability for a machine to automatically learn and apply methods to improve the quality of output is one of the most in-demand jobs in the world today. Companies are willing to pay big bucks to create systems that can understand their user and make predictive techniques based on their behavior.
These techniques and systems are already being employed by some of the biggest companies in the world. If you’re looking to start your machine learning course, here are a few basics you need to know:
R or Python:
Python and R are two of the most commonly used programming languages with algorithms in all fields dependent on them. While Python is used more in the field of machine learning, it is also easy to understand and learn. Organizations have already implemented it in places to develop applications on analytics. It makes it easier for users to implement any type of algorithms as well.
R is used to create better and more statistical processes. R is generally used to create and formulate statistical processes and companies dependent on data analytics tend to use R.
Statistics:
A basic understanding of Statistics is necessary to comprehend machine learning. While you might need to know what the algorithm does, knowing how the tools can be used for the end result is also necessary. With time, you’ll be able to implement your own algorithms as well and create inferential and descriptive statistical methods at some point.

Artificial Intelligence Course
What skills would you require?
If you’re looking to get a job in the field of artificial intelligence or machine learning, there are a few essential skills, including:

  1. Communication skills – An ability to communicate is crucial in addition to professionally having a good knowledge of spoken English
  2. Education – A good graduation degree or A.I. certificate is also required to begin a career in the field of education. This is needed to create your base in this particular field.
  3. Programming languages – A knowledge of programming languages – understanding of python string, variables, statement, operators, conditions, modules, and sense are super necessary.
  4. Machine learning techniques – Knowing all about Artificial Intelligence, especially Machine Learning as it is the most lucrative field. It has helped to create powerful websites, given realistic speech popularity and more
  5. TensorFlow – This is a software program that is periodical for the dataflow to be streamlined to execute different duties. It is generally used for gaining practice in the systems, including the relation to nerve networks.
  6. Deep Learning – Knowing about deep learning is necessary to use strategies that are rooted in the evaluation of learning statistics, in order to create a unique set of rules and processes.

Hence, with the time you will be able to understand everything about the field of machine learning and artificial intelligence. With Imarticus, you can get the machine learning certification and begin your journey right away!

What’s so trendy about machine learning? Why’s everyone crazy about it?

Posted on by Imarticus Learning
Reading Time: 4 minutes

Machine learning has become quite the trend, you must be noticing a lot of people opting for this particular course. So today we will tell you what the fuss is all about. To put it in simple terms, machine learning is basically learning from data. It involves tweaking of parameters and adjusting data, to get the best possible inference. It takes a little bit of practice to master machine learning, but it is not rocket science, you will get there sooner all later, just make data and algorithms your very best friends.
What is machine learning?
To start off, machine learning is all about feeding data into a generic algorithm and help it build its own logic, based on the data fed to it. This way, you don’t have to write codes. The subject can be divided into two main categories; supervised learning and unsupervised learning.
If you are tired of nodding at conversations about machine learning without understanding a thing, it is time you change that by getting hold of a machine learning courses. Believe it or not, it is an amazing skill to have, which will hold a very strong place in your resume or C.V. In fact, in today’s tech-savvy era, not knowing about machine learning is going to have a negative impact on your job. If you have no idea about what is machine learning then be a sport and start from scratch, there is plenty of study material available online and offline. Try to go through the theories, understand the basics and when you are ready, do opt for a machine learning certification course.
What is the hype all about?
Truth be told, the hype around machine learning is not going to fizzle out any time soon. It is a very important subject in a number of domains, as the subject has yielded some amazing results and there you can expect even better things in the future. At its core, the subject is really simple, and it involves lots and lots of data. It is very important to have access to as much data as you can possibly derive, and having documentation of the same. The progress made in the field of machine learning within the past decade has been absolutely phenomenal. This is a brand of artificial intelligence which is heavily based on data. The algorithms, as well as the data, helps the model to make accurate decisions, with the least human intervention.
Machine learning is one subject with the help of which we can easily, also very quickly analyze and understand, complex, big data and yield accurate results from it. This can be done on a very large scale, which increases the chances of identifying profitable opportunities.
The trend of machine learning
If machine learning facts and trends are anything to go by, then some major breakthroughs are on their way. Organizations can make better decisions without relying on human intervention. By using an algorithm to build models with the help of machine learning. Any industry working with a large amount of data, can make the most of progress and work more efficiently to gain an edge over their competitors. Many people are buying the machine learning trends and are more than willing to imbibe it in their organization whilst making the best use of it.
Why is everyone going gaga over machine learning?
With a  machine learning certification, you can make yourself useful in the following fields:

  • Financial services: Banks make use of machine learning to understand investment opportunities, trading trends and identify the clients with high-risk profiles. In fact, acts of fraudulence can be pinpointed with the help of machine learning surveillance. With such cut-throat completion in the finance sector, having a machine learning certification will most certainly prove to be an asset.
  • Transportation: Transportation is one field, where analyzing data helps in making some of the key decisions. The data analysis of machines learning can help both public and private sector transportation in many different ways.
  • Healthcare: All thanks to sensors and wearable devices which can assess a patient’s health, a lot of data can be gathered. With the use of machine learning, medical experts will be able to look at the various health trends, point out hazards and even stop epidemics from spreading. This will lead to better diagnosis, treatments, and prevention as well.
  • Government: The government deals with various different kinds of data, especially in areas such as public safety and utilities. Machine learning can really help in analyzing different kinds of data and find solutions to the impending problems with regards to the civilians. It can also minimize identity theft, online frauds and much more.
  • Marketing and sale: If you wish to build your career in this field, then you must opt for a machine learning certification course. Capturing data and analyzing upcoming marketing trends, alongside planning new campaigns based on them will become easy.

A course in machine learning opens many vistas of opportunities for candidates in the various fields. It is perhaps because of this reason, people are growing crazy about this particular area of computer studies. It is not the most difficult to master and people with the non-technical background can get a hang of it as well. The bottom line is, machine learning trends are on the high, so you might as well, think of opting for a course and strengthen your position in your organization, as it is a very important skill set in today’s times.

First Bench – Practicing Math Learning by Machine Learning

Posted on by Imarticus Learning
Reading Time: 3 minutes

It’s a common trend that even though a student studied the subject math in the classroom it is often difficult for him to grasp the things taught with accuracy. The same concept was realized by Salai Arjun, the founder of First Bench who claims that there should always be a balance maintained between the things being taught and the level of understanding of the things learned.
This particular urge to actively encourage a balance between math learning and understanding introduced First Bench into the market wherein individual assessment of students’ abilities are done and accordingly the future path is laid out for respective students. From the conceptualization of First Bench, their key goal is to develop an environment of interactive learning with the culmination of in-depth learning. First Bench has been operating since the past five years and has constantly been engaged in comprehending the study patterns, important behavioral feedbacks, and various other data which ultimately led to the development of the application of Machine Learning Technology and the implementation of Machine Learning Tools in their learning practices.
So, how do they actually function? With the help of Artificial Intelligence, First Bench makes use of Machine Learning Technology to operate in situations where human capability becomes limited. With a huge classroom size, it usually becomes tough for a teacher to offer attention to individual students. In such a situation, the Machine Learning Technology finds its valuable application. The Machine Learning Tools used by First Bench are highly engaged in assessing each student and providing tailor-made knowledge.
In the initial stage, the Machine Learning Tools assess individual students effectively before the commencement of any lesson. With the help of this Machine Learning Technology, this enterprise is successful in understanding the capability of each student to effectively comprehend the lessons to be taught. This type of Machine Learning Analysis takes into account the student’s knowledge about the basics and fundamentals of the topics to be learned.
Through Machine Learning Course and the proper implementation of Machine Learning Tools, the respective student’s answers are recorded. With the aid of this information, the Machine Learning Technology will conclude upon the learning course for a specific student and come up with suitable lessons. Thus, with this successful Machine Learning Analysis, a set of the learning system is devised for each student which is exclusive to individual people and is adaptive to the learning structure of the student.
In turn, what will be the results of such a Machine Learning Analysis which makes use of Machine Learning Technology? These Machine Learning tools will consistently assess the student’s performance and guarantee that each individual is able to efficiently learn the subjects and thereby, proceed to the next lessons. With this Machine Learning Analysis, it is obvious that along with the transition from one topic to another the student effectively develops the knowledge about each topic from a very basic level to a more challenging level. A result of this adaptive and efficient Machine Learning Analysis is that every student has the chance to proceed in their learning procedure by taking into account their abilities and inherent intelligence quotient.
The best thing about this is that not a single student is overlooked in this learning process. This method adopted by First Bench is an example of how AI and Machine Learning Technology are capable of adhering to individual students and their learning disabilities, thus creating a powerful learning system. With the success witnessed from the incorporation of Machine Learning Analysis a large number of schools are becoming increasingly interested in participating in such a program. Machine Learning sure has the potential to transform the complete education system in the upcoming generations.

The Ultimate Glossary of Terms About Machine Learning

Posted on by Imarticus Learning
Reading Time: 3 minutes

Machine learning is an artificial intelligence application which provides computer systems with the ability to learn on its own and improve with experience without any explicit requirement of additional programming. Machine learning has its focus on developing computer programs whole can access data and utilize the data to learn on its own.
Some of the commonly used terminology used in Machine Learning are as follows:

  • Adam Optimisation

It is an algorithm utilized to train models of deep learning and is an extension of the Stochastic Gradient Descent. In this algorithm, the average is run employing both gradients and using the gradient’s second moments. It is useful for computing the rate of adaptive learning for every parameter.

  • Bootstrapping

It is a form of the sequential process wherein each subsequent model tries to correct the errors in the earlier models. Each model is dependent on its previous model.

  • Clustering

It is a form of unsupervised learning utilized for discovering inherent groupings within a set of data. For instance, a grouping of consumers on the basis of their buying behavior which can be further used to segment the customers. It provides useful data which the companies can exploit to generate more revenues and profits.

  • Dashboard

It is an informative tool which aids in the visual tracking, analysis of data by displaying key indicators, metrics and data points on a single screen in an organized manner. Dashboards are often customizable and can be altered based upon the preference of the user or according to the requirement so of a project.

  • Deep Learning

It is a form of a Machine Learning algorithm which utilized the concepts of the human brain towards facilitation of the modeling of arbitrary functions. It requires a large volume of data, and the flexibility of this algorithm enables multiple outputs of different models at the same time.

  • Early Stopping

It is a technique of avoiding overfitting while training an ML model using iterative methods. Early stoppings are set in such a manner that it halts the performance of improvement on validation sets.

  • Goodness of Fit

It is a model which explains a proper fitment with a set of observations. Its measurements can be summarised into the discrepancies between its observed values with that of the expected values using a certain model.
This Machine Learning Course is a good fir when the errors on the models which are on training data along with the minimum test data. With time, this algorithm learns the errors in a model and corrects the same.

  • Iteration

It is the number of times the parameters of an algorithm is updated during training of a dataset on a model.

  • Market Basket Analysis

It is a popular technique utilized by marketers for identification of the best combination of services and products which are frequently purchased by consumers. It is also known as product association analysis.

  • MIS

Also known as Management Information System, it is a computerized system comprising of software and hardware which serve as the heart of a corporation’s operations. It compiles data from various online and integrated systems, conducts an analysis on the gathered data, and generates reports which enable the management to make informed and educated business decisions.

  • One Shot Learning

This form of machine learning trains the model which a single example. These are generally utilized for product classification.

  • Pattern Recognition

It is a form of machine learning which focuses on recognizing regularities and patterns in data. Some examples of pattern recognition used in many daily applications include face detection, optical character recognition, object detection, facial recognition, classification of objects etc.

  • Range

It is the difference between the lowest and the highest value in a data set.

What are The Best Machine Learning Prediction Models for Stocks?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Predicting stock prices has been at the focus for a long time due to monetary benefits it can yield. Prediction of the future stock price is trying to determine the future value of a company stock which is traded on a stock exchange. Traditionally investors have relied upon fundamental research and technical analysis to predict the stock price movements.  Fundamental analysis is concerned with the performance of the company and its business environment. Investors mainly consider the current price and likely future performance of the company while picking the stocks.
Technical Analysis is concerned with past patterns of the stock price movements and predicting future trends. Lately,  machine learning models are also used in technical analysis to process the historical and current data of public companies to predict their stock prices. Mathematical models can be developed which process historical data about quarterly financials, trading data, latest announcements, and news flow etc and machine learning techniques can identify patterns and insights that can be used to make predictions for stocks. Trading signals can be generated and because correlation based on which the trading call is given is often weak, the time window in which profit can be made by the execution of the trade is usually very small.  Therefore, firms that specialize in ‘quant’ trading keep their machine learning algorithms simple and secretive so their trading strategies can be optimized for speed and reliability.
Now, we take a brief look at some of the machine learning models for prediction of stock prices.
Moving Average – Moving average is average of past ‘n’ values and is considered widely in technical analysis.  20 day, 50 days and 200-day moving averages of stock prices and indices are critical data points in predicting future trends.
Exponential Moving Average (EMA) differs from simple moving average in that it gives greater weightage to the most recent values compared to the older values.
Linear Regression is another commonly used statistical approach to model the relationship between a scalar response and one or more independent variables.
Support Vector Machines (SVM) is a machine learning technique based on binary classification, which is now greatly used in predicting whether the price of a stock will be higher or lower after a specific amount of time-based on certain parameters.
There are also a few non-statistical models that are being used to forecast stock price movements. A textual analysis of financial news articles is one such method. In this method, a crawler is trained to scan all the financial news articles and look for the patterns that are likely to have an impact on prices of specific stocks. Text mining of historical news articles with concurrent time series analysis can be done to figure out the impact of various types of news articles. Different weightage for articles based on the credibility of their sources can be given.
Thus, Machine learning can be applied to stock data and mathematical models can be developed to predict stock prices. Trading strategies can be optimized for speed relying on these models while simultaneously eliminating human sentiments from decision making.
There is a lot to explore with regards to stock predictions and machine learning models that need further explanation cannot be expatiated in a concise article like this.  The machine learning future in India is very bright.  If you need to pursue machine learning courses, learn from pioneers like Imarticus.

Can You Integrate AIML with Android App?

Posted on by Imarticus Learning
Reading Time: 2 minutes

 

Artificial Intelligence has quickly become one of the most important fields to humanity today. The subject of an increased amount of research, AI is currently one of the few fields which are soaring with no end in sight today. It can be said that the very future of humankind now depends upon AI, and how it develops in the future – such is the reach of Artificial Intelligence in the modern world.

With such a rapid rise in the field of AI, there is no doubt that the demand for talented people in the field is higher than ever. If you want a career which is challenging yet satisfying, Artificial Intelligence is definitely one of the best options. However, you should start learning more about AI quickly, and what better way to put your skills into test than building a chatbot?

Chatbots are one of the latest sensations sweeping over AI practitioners. Chatbots are now increasingly becoming a part of most companies, and most of the internet users have already interacted with a chatbot in some form or other. Being an AI aficionado or a prospective practitioner, you can surely try to build a chatbot from scratch in order to gain some practice in Artificial Intelligence. A conversational assistant is a challenge to create because it has to give a new answer to the same questions and learn from the answers of the user, too. You can build simple chatbots with ease, and port it into android apps too, in many ways.

AIML was one such language which was used in the development of early chatbots.

What is AIML?

Artificial Intelligence Markup Language or AIML was created by Dr Richard Wallace and is currently offered as an open source framework for developing chatbots. It is offered by the ALICE AI Foundation so that users can create intelligent chatbots for their use from scratch. AIML is an extremely simple XML, just like HyperText Markup Language or HTML.

It contains a lot of standard tags and tags which are extensible, which you use in order to mark the text so that the interpreter which runs in the background understands the text you have scripted.

Steps to Integrate Chatbots into Android Apps

The steps covered here are not comprehensive in any way, but only an outline which you can follow in order to make what you want. These do not contain any codes, because that would defeat the purpose of creating an android app chatbot from scratch.

However, you can always skip the parts you are uninterested in, like the design aspects of the app and the likes.

The first step is to create a chat UI and interface using Android Studio. Using XML, you can do this with only a basic understanding of the language. It should have an adapter too, for the different view types.

The, import the AIML files that you have written beforehand to your app. Then, the task you have is to modify the MainActivity.java in such a way so as to include the class Bot in it.

Obviously, there is a lot of coding involved if you want to build the bot from scratch. However, integration is definitely possible, too. If you find yourself interested in learning more, you should check out the artificial intelligence courses in India on offer at Imarticus Learning.