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.

A comprehensive collection of projects crafted by the talented students pursuing their first year in the Master of Big Data and Business Intelligence. This repository serves as a testament to the ingenuity and hard work of our students as they delve into the dynamic realms of data analytics, business intelligence, and big data technologies.

This is the final project I had to do to finish my Big Data Expert Program in U-TAD in September 2017. It uses the following technologies: Apache Spark v2.2.0, Python v2.7.3, Jupyter Notebook (PySpark), HDFS, Hive, Cloudera Impala, Cloudera HUE and Tableau.

Growth of the PIM industry include rising demand for PIM solution from flourishing eCommerce industry and increasing need to offering enhanced customer services are driving the growth of the PIM market globally. The global product information management market accounted for US$ 7.5 billion in 2019 and is anticipated to register a CAGR of 14.5%. The report "Global Product Information Management Market, By Enterprise Type (Large Enterprise, Small & Medium Enterprise), By Industry (BFSI, Healthcare, Telecommunication & IT, Government, Retail, Transportation & Logistics, Management, Energy & Utility, Media & Entertainment, and Others), and By Region (North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa) - Trends, Analysis and Forecast till 2029”. Key Highlights: In October 2020, Pimcore introduced new features and improvements. The company updated its Pimcore platform and added new features, such as an editable dialog box, cache performance improvement, and tree sorting. In June 2020, Winshuttle formed a partnership with ABBYY, a digital intelligence company. The aim behind the partnership is to help organizations and businesses in digital transformation, which involves extracting data from physical documents and automatically loading it into SAP. Analyst View: Increasing investment in product information management Rising demand for centralized data storage of information related to products is driving the product information market. Centralized data storage is helping companies to easily manage and organize all the data related to its products. Data sources are updated with a single change in the centralized data storage, saving time and cost required for data management. Also, compliance and verification requirements are increasing due to the growing number of threats to information security. This provides safe and secure access to information stored in the centralized database. Access is granted only after completing verification of all the security credentials required. Product information management facilitates quick and easy access to the repository of information, at the same time strategic data storage techniques help in maintaining the data quality. Indexing and linking helps in reducing the time required to complete various processes related to data storage, increasing the operational efficiency. Marketing and sales of products are important processes to generate revenue. Growing PIM industry The market enables manifestation of products to achieve client centricity and unified customer view and provides a centralized system for improving the efficiency of promotional activities. All the distribution channels are managed effectively by using this solution. Integration of Big Data and business intelligence applications with cloud storage offers tremendous growth opportunities to the market. Browse 60 market data tables* and 35 figures* through 140 slides and in-depth TOC on “Global Product Information Management Market”, By Enterprise Type (Large Enterprise, Small & Medium Enterprise), By Industry (BFSI, Healthcare, Telecommunication & IT, Government, Retail, Transportation & Logistics, Management, Energy & Utility, Media & Entertainment, and Others), and By Region (North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa) - Trends, Analysis and Forecast till 2029 Key Market Insights from the report: The global product information management market accounted for US$ 7.5 billion in 2019 and is anticipated to register a CAGR of 14.5%. The market report has been segmented on the basis of enterprise type, application, and region. Depending upon enterprise type, the large enterprises shares the highest market due to the adoption of PI solutions and services is higher in large enterprises. The large enterprises heavily invest in advanced technologies to increase their overall productivity and efficiency. By application, the media & entertainment segment holds the largest share in the market. As most of the populations are staying at home, the usage of media and entertainment has increased with double digit growth. Product information offers high visibility, scalability and service optimization that can handle challenges occurred due to sudden increased demand in media and entertainment industry vertical. By region, North America is the largest market for product information management. The emerging demand to maximize value from the centralized master data and reference data, with ongoing demands of gaining meaningful insights from this consolidated master data is expected to further influence the adoption of PIM systems positively in the North American region during the coming years. The market in Asia-Pacific is expected to witness potential growth opportunities owing to the fast adoption of multi-domain PI software which is expected to enable better services in terms of performance, quality and capacity during the forecast period. To know the upcoming trends and insights prevalent in this market, click the link below: https://www.prophecymarketinsights.com/market_insight/Global-Product-Information-Management-Market-4573 Competitive Landscape: The prominent player operating in the global product information management market includes SAP AG, IBM Corporation, Oracle Corporation., Informatica LLC, Riversand Technologies, Inc., Stibo Systems, ADAM Software NV, Agility Multichannel Ltd., InRiverAB and Pimcore GmbH. The market provides detailed information regarding the industrial base, productivity, strengths, manufacturers, and recent trends which will help companies enlarge the businesses and promote financial growth. Furthermore, the report exhibits dynamic factors including segments, sub-segments, regional marketplaces, competition, dominant key players, and market forecasts. In addition, the market includes recent collaborations, mergers, acquisitions, and partnerships along with regulatory frameworks across different regions impacting the market trajectory. Recent technological advances and innovations influencing the global market are included in the report.

Python is point of fact the Next Big Thing to investigate. There is no need to be worried about its worth, profession possibilities, or accessible positions. Python's commitment to the advancement of your calling is huge, as its notoriety among designers and different areas is step by step waning. Python is "the one" for an assortment of reasons. It's a straightforward pre-arranged language that is not difficult to get. Subsequently, the general improvement time for the task code is diminished. It accompanies an assortment of structures and APIs that assistance with information examination, perception, and control. Employment opportunities in Python While India has a critical interest for Python engineers, the stock is very restricted. We'll utilize a HR master articulation to validate this. For both Java and Python, the expert was relied upon to employ ten developers. For Java, they got over 100 fantastic resumes, however just eight for Python. In this way, while they needed to go through an extensive method to get rid of resilient people, they had no real option except to acknowledge those eight individuals with Python. What does this say about the circumstance to you? Regardless of Python's straightforward language structure, we desperately need more individuals in India to update their abilities. This is the reason learning Python is a particularly colossal opportunity for Indians. With regards to work openings, there may not be numerous for Python in India. Notwithstanding, we have countless assignments accessible per Python developer. In the relatively recent past, one of India's unicorn programming organizations was stood up to with an issue. It had gotten a $200 million (Rs. 1200 crore) arrangement to develop an application store for a significant US bank. Be that as it may, the organization required talented Python developers. Since Python was the best language for the undertaking, it wound up paying a gathering of independent Python developers in the United States multiple times the charging sum. For sure and Naukri, for instance, have 20,000 to 50,000 Python work postings, showing that Python vocation openings in India are copious. It is an insightful choice to seek after a profession in Python. The diagrams underneath show the absolute number of occupation advertisements for the most well known programming dialects. Python Job Descriptions Anyway, what sorts of work would you be able to get in the event that you know Python? Python's degree is broad in information science and investigation, first off. Customers regularly demand that secret examples be separated from their informational indexes. In AI and man-made reasoning, it is additionally suggested. Python is a top choice among information researchers. Furthermore, we figured out how Python is used in web advancement, work area applications, information examination, and organization programming in our article on Python applications. Python Job Profiles With Python on your resume, you might wind up with one of the accompanying situations in a presumed organization: 1. Programmer Investigate client necessities Compose and test code Compose functional documentation Counsel customers and work intimately with other staff Foster existing projects 2. Senior Software Engineer Foster excellent programming engineering Mechanize assignments by means of prearranging and different apparatuses Survey and troubleshoot code Perform approval and confirmation testing Carry out form control and configuration designs 3. DevOps Engineer Send refreshes and fixes Break down and resolve specialized issues Plan systems for support and investigating Foster contents to mechanize representation Convey Level 2 specialized help 4. Information Scientist Recognize information sources and mechanize the assortment Preprocess information and dissect it to find patterns Plan prescient models and ML calculations Perform information representation Propose answers for business challenges 5. Senior Data Scientist Manage junior information experts Construct logical devices to create knowledge, find designs, and foresee conduct Execute ML and measurements based calculations Propose thoughts for utilizing had information Impart discoveries to colleagues While many significant firms are as yet utilizing Java, Python is a more seasoned yet at the same time well known innovation. Python's future is splendid, on account of: 1.Artificial Intelligence (AI): Machine knowledge is alluded to as man-made consciousness. This is as a conspicuous difference to the regular astuteness that people and different creatures have. It is one of the most up to date advances that is clearing the globe. With regards to AI, Python is one of the main dialects that rings a bell; truth be told, it is probably the most ideally equipped language for the work. We have different structures, libraries, and devices devoted to permitting AI to swap human work for this objective. It supports this, however it additionally further develops productivity and precision. Discourse acknowledgment frameworks, self-driving vehicles, and other AI-based advancements are models. The accompanying devices and libraries transport for these parts of AI: AI – PyML, PyBrain, scikit-learn, MDP Toolkit, GraphLab Create, MIPy General AI – pyDatalog, AIMA, EasyAI, SimpleAI Neural Networks – PyAnn, pyrenn, ffnet, neurolab Normal Language and Text Processing – Quepy, NLTK, genism 2. Enormous Data Enormous Data is the term for informational collections so voluminous and complex that conventional information handling application programming is insufficient in managing them. Python has assisted Big Data with developing, its libraries permit us to break down and work with a lot of information across groups: Pandas scikit-learn NumPy SciPy GraphLab Create IPython Bokeh Agate PySpark Dask 3. Systems administration Python additionally allows us to design switches and switches, and perform other organization mechanization undertakings cost-viably. For this, we have the accompanying Python libraries: Ansible Netmiko NAPALM(Network Automation and Programmability Abstraction Layer with Multivendor Support) Pyeapi JunosPyEZ PySNM Paramiko SSH Python Course

repositorio del master Big Data aplicado al Business Intelligence de la UNED

Source code and documentation of my final project for the Master in Business Intelligence and Big Data at the UOC

Esta librería de Github recoge los diferentes modelos creados por Clarissa Solino, Sergio Romero, Sebastián Fernández y José Barroso para el TFM del Máster de Next Education "Big Data & Business Intelligence". El título del TFM es Oportunidades de Inversión en Criptomonedas Análisis Predictivo y Estrategias Basadas en Big Data.

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Repositorio para entrega Asignatura 5

Master Big Data and Business Intelligence

Obtención y limpieza de datos financieros a partir de la API Yahoo Finance.

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Conception d’un Système de Gestion de File d’Attente pour l’Optimisation des Données et de l’Analyse Statistique

Trabajo Final del Master Big Data and Business Intelligence 2026

Final project repository - Master Big Data & Business Intelligence at CICE

Repositorio del Trabajo Final del Master del equipo 15 para el Master en Big Data & Business Intelligence 2026

This project is part of my computing master's Big Data and Business Intelligence assessment

Memoria técnica y trazabilidad del proyecto de TFM del Grupo 7 del Máster en Big Data & Business Intelligence: Sistema de Location Intelligence.

Final assignment in the Business Intelligence Module for the Master's Degree in Data Science, Big Data and Business Analytics (Universidad Complutense de Madrid)

Este es el proyecto para el curso de obtencion de datos del master de Big Data & Business Intelligence

Homework relativo al superbonus edilizio 110% per il modulo Big Data&Analytics del Master in Business Intelligence & Big Data Analytics incentrato su web scraping e data quality

Project for the Machine Learning module of the Master's in Data Science, Big Data, and Business Intelligence at Complutense University.

Exercise, Homeworks and Group project for the Master in Business Intelligence & Big Data Analytics (2023-2024) at university of Milano-Bicocca

Repositorio con documentos relacionados con la ejecución del trabajo final de la Asignatura 5 del Master en Big Data y Business Intelligence.

Repositorio con documentos relacionados con la ejecución del trabajo final de la Asignatura 6 del Master en Big Data y Business Intelligence.

Trabajo Final para graduarme del master en Big Data & Business Intelligence de IMF Smart Education, optimización de rutas de transporte público mediante el algoritmo de colonia de hormigas

Ce projet de management réalisé dans le cadre du Master Big Data & Business Intelligence (BIDABI) à l’Université Sorbonne Paris Nord. Le projet porte sur l’analyse des pratiques managériales et de la prise de décision stratégique, dans un contexte d’innovation, de transformation numérique et de performance organisationnelle.

Data science, analytics, AI, big data are becoming widely used in many fields, that leads to the ever-increasing demand of data analysts, data scientists and other data professionals. Due to that, data science education is now a hot topic for educators and entrepreneurs. In this project, I will first re-design the course curriculum for “MIE1624: Introduction to Data Science and Analytics” course at University of Toronto, such that students acquire an introduction to the most relevant topics and skills in data science and AI. Second, I will need to design a curriculum for a new “Master of Data Science and Artificial Intelligence” program at University of Toronto with focus not only on technical but also on business and soft skills, see, e.g., http://www.rotman.utoronto.ca/Degrees/MastersPrograms/MMA, http://smith.queensu.ca/grad_studies/mmai/, http://mbai.kse.ua, or http://schulich.yorku.ca/programs/mmai/, that contains optimal courses (and internships, projects, extra-curricular activities, etc.) for students to obtain necessary technical and business skills to pursue a successful career as data scientist, analytics and data manager, data analyst, business analyst, AI system designer, etc. Third, I will need to develop your own analytics-based solution in education field.