Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.

The purpose of the laboratory practical was to gain knowledge on the basics of MATLAB and its application to Statistical Signal Processing. The experiment was divided into two parts; the first part was on basics of MATLAB and its application to signal processing problems, the second part was on Speech processing using MATLAB. In the first chapter, after getting acquainted with basic matlab programming concepts, we proceeded to generating noise using the rand() and randn() functions. The concept of linear estimation was modelled to estimate the message in a signal corrupted by noise and the designed estimators were compared and justified to satisfy the Cramer-Rao Lower Bound theorem. In addition, a non linear estimation techniques; Least-Square criterion was developed to estimate the variables in a sum of sinusoidal signal corrupted by noise. The second chapter covered parameter estimation of an AR process. A sound signal was analyzed and a sample of the sound signal was synthesized and compared with the recorded sound signal.

A very basic concept is used that is using the speech recognition tool of python and creating a query to listen, process, and give the result. Wikipedia module is also used in the code. Pyttsx3 module (sapi5) is used for the voice in the code. Voice Commands that work are: Voice Command - Open Youtube Voice Command - Open google Voice Command - Play music Voice Command - (what you want to search) wikipedia eg:- Barack Obama Wikipedia

Basic Concepts: Articulatory Phonetics – the development and classification of speech sounds; Acoustic Phonetics – the acoustics of speech production; Review of Digital Signal Processing concepts; Short-Time Fourier Transform, Filter-Bank, and LPC Methods Techniques for Speech Analysis: Features, Feature Extraction, and Pattern Comparison: Log Spectral Distance, Cepstral Distances, Weighted Cepstral Distances and Filtering, Likelihood Distortions, Spectral Distortion using a Warped Frequency Scale, LPC, PLP, and MFCC Coefficients are both statistical and perceptual speech distortion measures. Multiple Time – Alignment Paths, Dynamic Time Warping, and Time Alignment and Normalization Remarks

An Autonomous Driving Vehicle (ADV) is fundamentally defined as a passenger vehicle that drives itself. In future the automated systems are used to avoid accidents and reduce congestion. ADV will be capable of determining the best route and warn each other about the conditions ahead. Our ADV designed is a basic type, which can work using lane detection, pedestrian detection and obstacle detection using cameras, laser range finder and sensors. The concepts involved are deep learning and image processing. The control mechanism consists of stepper motors, servo motors and DC motors. Using speech processing, ADV can also be driver assistance

As we know Python is an emerging language so it becomes easy to write a script for Voice Assistant in Python. The instructions for the assistant can be handled as per the requirement of user. Speech recognition is the process of converting speech into text. This is commonly used in voice assistants like Alexa, Siri, etc. In Python there is an API called SpeechRecognition which allows us to convert speech into text. It was an interesting task to make my own assistant. It became easier to send emails without typing any word, Searching on Google without opening the browser, and performing many other daily tasks like playing music, opening your favorite IDE with the help of a single voice command. In the current scenario, advancement in technologies are such that they can perform any task with same effectiveness or can say more effectively than us. By making this project, I realized that the concept of AI in every field is decreasing human effort and saving time. Functionalities of this project include: 1. It can send emails. 2. It can read PDF. 3. It can send text on WhatsApp. 4. It can open command prompt, your favorite IDE, notepad etc. 5. It can play music. 6. It can do Wikipedia searches for you. 7. It can open websites like Google, YouTube, etc., in a web browser. 8. It can give weather forecast. 9. It can give desktop reminders of your choice. 10. It can have some basic conversation. Now the basic question arises in mind that how it is an AI? The virtual assistant that I have created is like if it is not an A.I, but it is the output of a bundle of the statement. But fundamentally, the mail purpose of A.I machines is that it can perform human tasks with the same efficiency or even more efficiently than humans. It is a fact that my virtual assistant is not a very good example of A.I., but it is an A.I.

A simple Python-based tool that converts audio or speech into text using the SpeechRecognition library. It supports microphone and audio file input, making it useful for voice notes, transcription tasks, and learning basic speech processing concepts.

playing with concepts for a word processor based on a raspberry pi & text to speech, so you can use it without a screen. very basic concept playing stuff at the moment...

text to speech is a process to convert any text into voice . Text to speech projects takes words on digital devices and convert them into audio with a button click or finger touch. To implement this project , we had use the basic concepts of Python,Tkinter , gTTS and playsound libraries

🎙️ Speech-to-Text Recognition A mini project that converts spoken audio into text using Python and the SpeechRecognition library. The system captures real-time voice input and transcribes it using Google’s Speech Recognition API, demonstrating basic audio processing and NLP concepts.Tech Used: Python, SpeechRecognition, PyAudio

🎙️ Speech-to-Text Recognition A mini project that converts spoken audio into text using Python and the SpeechRecognition library. The system captures real-time voice input and transcribes it using Google’s Speech Recognition API, demonstrating basic audio processing and NLP concepts.Tech Used: Python, SpeechRecognition, PyAudio

Python-based voice assistant that performs basic tasks using voice commands such as opening websites (Google, YouTube, WhatsApp), searching on the web, and telling date and time. Built using Python with SpeechRecognition, pyttsx3, and webbrowser modules to apply concepts of automation, speech processing, and real-world application development.

Text to speech is a process to convert any text into voice. Text to speech project takes words on digital devices and converts them into audio with a button click or finger touch. Text to speech python project is very helpful for people who are struggling with reading. To implement this project, we will use the basic concepts of Python.

A very basic concept is used that is using the speech recognition tool of python and creating a query to listen, process, and give the result. Wikipedia module is also used in the code. Pyttsx3 module (sapi5) is used for the voice in the code. Voice Commands that work are: Voice Command - Open Youtube Voice Command - Open google Voice Command - …