Firstly, Machine learning as well as artificial intelligence-based projects are obviously things that point out what the future holds. We want better personalization, smarter recommendations, as well as improved search functionality. Our apps should be able to see, hear, and respond – that’s what artificial intelligence (AI) has brought to the world, enhancing the user experience as well as creating value across many industries.

Particularly, Now you likely to face two major questions: How can I bring these experiences to life? and What programming language is used for AI and ML? You should consider using Python for AI and machine learning.
 

What makes Python the best programming language for both machine learning as well as AI applications?

Particularly, When the question is about AI, its projects differ from traditional software projects. Firstly, the differences lie in the technology stack that is being used, the skills that are required for an AI-based project, as well as the ability or the necessity of deep research. In order to implement your AI aspirations, you should consider making use of a programming language that is stable, flexible, as well as has all the tools available. Python offers all of this to the developers and programmers, which is why we see lots of Python AI projects today thus making it apt for the best fit.

From development to deployment as well as maintenance, Python helps developers and programmers be productive and confident about the software that they’re building and making. Benefits that make Python the best fit for machine learning and AI-based projects are simply abundant. These benefits include simplicity and consistency, access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community. These are going to add to the overall popularity of the language.

 

Simple and consistent:

in Conclusion, Python offers concise and easily understandable and readable code. While there are complex algorithms and versatile workflows that stand behind machine learning and AI, Python’s simplicity and easy to understand code allows developers and programmers to write reliable systems. Developers are able to get together and put all their effort into solving an ML problem instead of focusing on the technical nuances of the language.

Additionally, Python is appealing to many programmers and developers as it’s easy to learn. Python code is understandable by humans just as plain English, which makes it easier to build models for machine learning.

Many programmers say that Python is going to be much more intuitive than other programming languages. Others point out the many frameworks, libraries, as well as extensions that simplify the implementation of different Python-based functionalities. It’s generally accepted and acknowledged that Python is suitable for collaborative implementation. When the need stand to use multiple developers, python support comes first. Since Python is a general-purpose language that can be used for almost anything, it can do a set of complex machine learning tasks and enable developers and programmers to build prototypes quickly. This in turn allows you to test your product for machine learning purposes.
 

An extensive selection of libraries and frameworks:

Implementing AI and ML algorithms can be sometimes or at times be tricky and require a lot of time. It’s vital to have a well-structured as well as a compatible and well-tested environment to enable developers to come up with the best coding solutions.

In order to reduce development time, programmers turn to a number of Python frameworks and libraries. A software library is nothing but a pre-written code that developers and programmers use to solve common programming tasks. Python, with its rich technology stack, has an extensive set of libraries that is used for artificial intelligence and machine learning. Here are some of them:

• • Keras, TensorFlow, and Scikit-learn for machine learning
  • NumPy for high-performance scientific computing as well as for data analysis
  • SciPy  used for advanced computing
  • Pandas for general-purpose data analysis
  • Seaborn for data visualization

Scikit-learn features have been including various classification, regression, and clustering algorithms, these include support vector machines, random forests, gradient boosting, k-means, and also DBSCAN. These features are designed to work with the Python numerical and scientific libraries NumPy and SciPy.

With these solutions, you can develop and create your product faster. Your development team won’t have to reinvent the wheel. You can make use an existing library to implement necessary features.

 

Also Read: How AI and ML have Revamped Mobile App Development Industry

 

What is Python good for?- Python for Artificial intelligence and Machine Learning

Here’s a table of сommon AI use cases and technologies that are best suited for them. We recommend using these:

Data analysis and visualization	NumPy, SciPy, Pandas, Seaborn
Machine learning	TensorFlow, Keras, Scikit-learn
Computer vision	OpenCV
Natural language processing	NLTK, spaCy

 

Platform independence:

Platform independence has always referred to a programming language or framework that allows developers and programmers to implement things on one machine and use them on another machine without any (or with only minimal) changes. One key to Python’s popularity is that it is a platform-independent language. Python provides and enables supports for many platforms including Linux, Windows, and macOS.

What’s more, developers and programmers usually use services such as Google or Amazon for their computing needs. However, you can often find companies and data scientists who use their own machines that has powerful Graphics Processing Units (GPUs) in order to train their ML models. And the fact that Python has always been platform independent, this makes training a lot cheaper and easier.
 

Great community and popularity:

In the Developer Survey 2018 by Stack Overflow, Python was among the top 10 most popular programming languages, which ultimately means that you can find a development company with the necessary skill set to build your AI-based project.

 

Recommended Read: Difference Between Artificial Intelligence And Machine Learning

 

Conclusion

So you see these are the major reasons why Python is the most preferred language as far as AI and ML are concerned. Pythons increasing popularity is key to using it for different ML and AI applications. Hope you like this blog on Why Use Python for Artificial intelligence and Machine Learning? If you are looking to Hire Python Developer then do mail us on enquiry@nimapinfotech.com

 

[yasr_overall_rating] Over 258 people have rated [5/5]

Overview

Firstly, Artificial Intelligence and Machine Learning are commonly used technologies. Companies use them interchangeably. Particularly, They are not the same thing but the perception that they look similar can often lead to confusion among these terms.  Secondly, Both these terms come up very frequently when the topic is Big data, analytics and when the broader waves of technological change that is sweeping the world.

AI and ML have led to the birth of a plethora of different kinds of applications as well as technologies. These technologies have long helped mankind to implement and use their decision-making ability and creative thinking in its applications. Man has never been helped in such situations. New inventions and capabilities have identified and distinguished due to AI and ML. Many instances are there where a real-time problem is solved due to the implementation of AI and ML. These technologies go hand in hand to solve and provide solutions to the most intricate problems that occur to man.

Let us get to the basics of Artificial Intelligence(AI) and Machine Learning(ML):

 

In conclusion, Artificial Intelligence is the broader concept of machines that are capable enough of carrying out tasks in a way that we consider as “Smart”.

 

Particularly, Machine learning is the current application of AI that is based around the idea that we should provide machines access to data and letting them learn for themselves.

 

Inception

Firstly, Artificial Intelligence has been around for quite a long time, the Greek myths have contains many stories of mechanical men designed to mimic human behavior. European computers that were invented in the olden days. These were used as logical machines by reproducing the capabilities such as basic arithmetic and memory. Engineers saw their Job fundamentally as attempting to create mechanical brains.

 

As the technologies evolved, our understanding of how our minds work has progressed, our concept of what constitutes AI has changed. Instead of increasing complex calculations, the main work in the field of AI constitutes mimicking human behavior.  Also mimicking decision making capability and carrying out tasks in human ways.

Direct Comparison- Artificial Intelligence vs Machine Learning

Artificial Intelligence is nothing but imparting human intelligence and behavior to a machine that allows them to act in a smart way. Many games and modern robots interact and function using Artificial intelligence. There are two types of AI.  These are Applied AI and General AI. Applied AI is far more common. This AI is designed to smartly and intelligently trade stocks and shares or imparting the capability to manoeuvre an autonomous vehicle is something that would fall in this category

 

Read More: Technical Approach To Artificial Intelligence For Beginners

 

Generalized AI is something that imparts machines the capability to handle any task in common, but this is where some of the most exciting advancements are happening today. This is the area that has to lead to the development of Machine Learning. This is referred to as a subset of AI. It is really more accurate to think of it as the current state-of-the-art.

Let us dive deep into what are the subtle differences between AI and ML:

 

Artificial Intelligence vs Machine Learning

 

Artificial Intelligence	Machine Learning
Artificial Intelligence is the way to imparting human intelligence to machines.  This is done in such a way that they are able to mimic human behavior and cognitive thinking	Machine learning means allowing machines to learn through experience. This is done by allowing them to access to different data sets that enable machine to predict patterns inside the data.
AI is human intelligence that has been demonstrated by machines to perform simple to complex tasks	ML provides machines the ability to learn as well as understand without being explicitly programmed
The idea behind AI is to program machines to carry out tasks in more human ways or smart ways.	The key to teaching computers to think and understand as humans do is the concept of machine learning
AI is based on the characteristics of human intelligence	ML is based on the system of probability
AI is used in healthcare, finance, aviation, transportation, marketing, education, media, etc.	ML is used for optical character recognition, web security, imitation, learning, and so on.
Artificial Intelligence is a method of data analysis that makes your model intelligent.	Machine Learning is a method of data analysis that is able to automate analytical model building.
Artificial Intelligence results in knowledge or making your system intelligent.	Machine learning results in the creation of more data.
The aim is to extend the probability of success.	The aim is to extend accuracy.
AI is the higher cognitive process	ML permits the system to be told newer things from the knowledge base
The goal of AI is to simulate natural intelligence in order to solve complex problems	The goal of ML is to learn from the data on certain tasks in order to maximize the performance(reduce the error) of machines on these tasks.
Artificial Intelligence is a decision-making capability	ML allows the system to learn new things from the data that is presented to it.
Artificial Intelligence can be interpreted as the ability to incorporate human intelligence to machines.	Machine Learning can be interpreted as  empowering computer systems with the ability to “learn”
AI leads to developing a system in order to mimic human behavior for responding to circumstances	ML involves the creation of self-learning algorithms that are able to make a decision based on the data that is provided to them.

 

 

Know More: Artificial Intelligence on smartphones – why it’s important

 

 

Conclusion:

 

So you see that there is a vast difference between Artificial Intelligence and Machine Learning. We hope that you have found this article useful and have gained some knowledge about AI vs ML topic. If you’re looking to take up artificial intelligence and machine learning course in Mumbai then do contact us. We have highly experienced professionals to guide you in your AI and ML venture. Partnering with us will provide you the advantage to be the best in your industry and solve real-time user problems and provide efficient and effective solutions.

 

 

[yasr_overall_rating] Over 388 people have rated [5/5]

 

 

A chatbot is nothing but an Artificial Intelligence powered software inside a device. (Alexa, Siri, Google Assistant, etc) application website or other networks that try to gauge the customer’s needs and assist them in order to perform a particular task like a commercial transaction, hotel booking, submission and so on. Every company nowadays has deployed a chatbot on its website to help users as well as to engage with them. Some ways in which companies are using chatbots are as follows:

• Delivering Flight Information
• Connecting with customers and their finances
• Assist and provide customer support

 

Inner workings of a Chatbot:

There are broadly two main types of chatbot variants: Rule-Based and Self Learning

 

In the rule-based approach, a bot answers questions on some rules that it is trained on. The rules can be made to be very simple or very complex. The bot can handle simple queries but often fails to manage complex ones.

 

Self-learning bot is those that use Machine learning-based approaches and are definitely more efficient than the rule-based bots, these bots are further subdivided into two types. Retrieval based or Generative.

 

In the Retrieval based models, a chatbot has the option to use heuristics to select a response from a library of predefined response. The chatbot can use the message as well as the context of the conversation for selecting the best response from a predefined list of bot messages. The content can include a previous message from the dialogue tree, all previous messages in the conversation, previously saved variables. The Heuristics for selecting a response can be engineered in a variety of ways, from rule-based conditional if-else logic to machine learning classifiers.

 

Generative bots are capable of generating the answers and not always replies with one of the answers from a set of answers. This makes them more intelligent as they take the word by word query and generate the answers.

 

You will require the following packages scikit library and NLTK

 

Downloading and installing NLTK
Install NLTK: run pip install nltk
Test installation: run python then type import nltk

 

Installing NLTK Packages

 

Import NLTK and run nltk.download(). This will open the NLTK downloader from where you can choose the corpora and models to download. You can also download all packages at once.

 

Would like to know more: NLP for chatbot

 

Importing the necessary libraries

 

import nltk
import numpy as np
import random
import string # to process standard python strings

 

Reading in the data

 

We will read in the corpus.txt file and convert the entire corpus into a list of sentences and a list of words for further pre-processing.

 

f=open(‘chatbot.txt’,’r’, errors = ‘ignore’)

raw=f.read()
raw=raw.lower()# converts to lowercase

nltk.download(‘punkt’) # first-time use only
nltk.download(‘wordnet’) # first-time use only

sent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences
word_tokens = nltk.word_tokenize(raw)# converts to list of words

 

 

Let see an example of the sent_tokens and the word_tokens

 

ent_tokens[:2]

 

[‘a chatbot (also known as a talkbot, chatterbot, bot, im bot, interactive agent, or artificial conversational entity) is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods.’,
‘such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test.’]

 

word_tokens[:2]

 

[‘a’, ‘chatbot’, ‘(‘, ‘also’, ‘known’]

 

Pre-processing the raw text

 

We shall now define a function called LemTokens which will take the tokens as an input and return normalized tokens.

 

lemmer = nltk.stem.WordNetLemmatizer()
#WordNet is a semantically-oriented dictionary of English included in NLTK.

def LemTokens(tokens):
return [lemmer.lemmatize(token) for token in tokens]
remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)
def LemNormalize(text):
return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))

 

 

Keyword matching

 

Next, we shall use and create a function for a greeting by the bot i.e if a user’s input is a greeting, the created bot shall return a comforting greeting response. ELIZA uses a simple keyword matching for greetings. We will utilize the same concept here.

GREETING_INPUTS = (“hello”, “hi”, “greetings”, “sup”, “what’s up”,”hey”,)
GREETING_RESPONSES = [“hi”, “hey”, “*nods*”, “hi there”, “hello”, “I am glad! You are talking to me”]

def greeting(sentence):

for word in sentence.split():
if word.lower() in GREETING_INPUTS:
return random.choice(GREETING_RESPONSES)

 

 

Generating Response

 

To generate a response from our bot for input questions, the concept of document similarity will be used. So we begin by importing the necessary modules.

• From scikit learn library, import the TFidf vectorizer to convert a collection of raw documents to a matrix of TF-IDF features.

from sklearn.feature_extraction.text import TfidfVectorizer

• Also, import cosine similarity module from scikit learn library

from sklearn.metrics.pairwise import cosine_similarity

 

This will be used to find the similarity between words entered by the user and the words in the corpus. This is the simplest possible implementation of a chatbot.

 

We define a function response which searches the user’s utterance for one or more known keywords and returns one of several possible responses. If it doesn’t find the input matching any of the keywords, it returns a response:” I am sorry! I don’t understand you”

 

def response(user_response):
robo_response=”
sent_tokens.append(user_response)
TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words=’english’)
tfidf = TfidfVec.fit_transform(sent_tokens)
vals = cosine_similarity(tfidf[-1], tfidf)
idx=vals.argsort()[0][-2]
flat = vals.flatten()
flat.sort()
req_tfidf = flat[-2]

if(req_tfidf==0):
robo_response=robo_response+”I am sorry! I don’t understand you”
return robo_response
else:
robo_response = robo_response+sent_tokens[idx]
return robo_response

 

Finally, we will provide the lines that we want our bot to speak while starting and ending a conversation that depends on the user’s input.

 

flag=True
print(“ROBO: My name is Robo. I will answer your queries about Chatbots. If you want to exit, type Bye!”)
while(flag==True):
user_response = input()
user_response=user_response.lower()
if(user_response!=’bye’):
if(user_response==’thanks’ or user_response==’thank you’ ):

flag=False
print(“ROBO: You are welcome..”)
else:
if(greeting(user_response)!=None):
print(“ROBO: “+greeting(user_response))
else:
print(“ROBO: “,end=””)
print(response(user_response))
sent_tokens.remove(user_response)
else:
flag=False
print(“ROBO: Bye! take care..”)

 

 

Read More: Creating Chatbot with Deep Learning

 

 

So that’s pretty much it. We have coded our first chatbot in NLTK. Now, let us see how it interacts with humans:

 

 

 

Conclusion

Though it is a very simple bot that does not have any cognitive skills, its a good way to get into NLP and get to know about chatbots.Though ‘ROBO’ responds to user input. It cannot fool your friends, and for a production system you’ll want to consider one of the existing bot platforms or frameworks, but this example should help you think through the design and challenge of creating a chatbot. The Internet has many resources and after reading this article we’re sure, you will want to create a chatbot of your own. So happy tinkering!! We hope with this article you would have some knowledge about AI and ML for creating a chatbot. If you liked this article please check out our other articles also.

 

 

If you want to learn more about artificial intelligence and machine learning, then do check out our website iphonedevelopmentguide.com. You might be looking to pursue an artificial intelligence and machine learning course in Mumbai, in that case, check out the various training programs that we offer. You can also pursue an artificial intelligence developer course by contacting us at enquiry@nimapinfotech.com