Development, Data Science

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In this self-paced course, you will learn how to apply Naive Bayes to many real-world datasets in a wide variety of areas, such as:computer visionnatural language processingfinancial analysishealthcaregenomicsWhy should you take this course? Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it.This course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You'll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm. You'll learn about when and why to use the different versions of Naive Bayes included in Scikit-Learn, including GaussianNB, BernoulliNB, and MultinomialNB.In the advanced section of the course, you will learn about how Naive Bayes really works under the hood. You will also learn how to implement several variants of Naive Bayes from scratch, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes. The advanced section will require knowledge of probability, so be prepared!Thank you for reading and I hope to see you soon!Suggested Prerequisites:Decent Python programming skillComfortable with data science libraries like Numpy and MatplotlibFor the advanced section, probability knowledge is requiredWHAT ORDER SHOULD I TAKE YOUR COURSES IN?Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including my free course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeLess than 24 hour response time on Q & A on averageNot afraid of university-level math - get important details about algorithms that other courses leave out

Development, Data Science

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Development, Data Science

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Welcome to this course on Data Science bootcamp with Microsoft Power Bi and Python. In this course, you will learn various concepts with hands on examples where you will learn to create powerful BI reports and analytics dashboard. You will learn right from creating data visualization charts in Power BI with and without using python programs. You will learn to create various kinds of charts such as Bar, Pie, Ring, Treemap and more that are available as default charts in Power BI. Moreover you will also learn to create advanced custom charts by writing python programs such as line, scatterplot and violin chart. After that, you will also learn to create slicer filters for categories and date based on which you can filter the data that is visually displayed on the chart. This feature helps in focused decision making based on decided parameter such as region, category or date.After learning lessons on Data Visualization, you will learn Data Cleaning and Data Preparation by using Power Query Editor. Here, you will learn to perform various kinds of operations on rows, columns or individual cells of the dataset. You will learn to create new custom column or field in a table based on a certain condition such as conditional column, and you will also learn to create index column. You will learn to perform row operations such as row deletion. For columns, you would learn to perform Split, Merge, Extract and other operations in Power Query editor.You could use the skills learned in this course for various domains such as Data Science, Business Intelligence, Data Analysis, Data Preparation and Data Visualization.Topics discussed under Data Visualization and Analytics with Python and Power BI-Bar chartLine chartPie chartRing chartTreemap chartTable and MatrixDrill downInstall python librariesCreate line chart with matplotlibPutting labels and creating a dashed line chartViolin chart with seabornSlicer FilterDate SlicerCreating a calculated measureUsing live web dataTopics covered under Data preparation with Power Query-Row deletion and column SplitReplace column valuesColumn MergeAdding Suffix and PrefixAdd and transform columnExtract functionAdding conditional and Index columnDate function in power query

Development, Data Science

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Development, Data Science

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Overview: Explore the application of key mathematical topics related to linear algebra with the Python programming language.Expected Duration: After completion of this course, you should be able to accomplish the objectives from the following lessons and topics. 1. Lessons on Math for Data Science & Machine Learning: 2. Understand how to work with vectors in Python3. Basis and Projection of Vectors: Understand the Basis and Projection of Vectors in Python4. Work with Matrices: Understand how to work with matrices in Python5. Matrix Multiplication: Understand how to multiply matrices in Python6. Matrix Division: Understand how to divide matrices in Python7. Linear Transformations: Understand how to work with linear transformations in Python8. Gaussian Elimination: Understand how to apply Gaussian Elimination9. Determinants: Understand how to work with determinants in Python10. Orthogonal Matrices: Understand how to work with orthogonal matrices in Python11. Eigenvalues: Recognize how to obtain eigenvalues from eight decompositions in Python12. Eigenvectors: Recognize how to obtain eigenvectors from eigendecomposition in Python13. PseudoInverse: Recognize how to obtain pseudoinverse in Python

Development, Data Science

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Development, Data Science

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¡Bienvenido al apasionante mundo de la Ciencia de Datos y Machine Learning en R! En este curso, te embarcarás en un viaje transformador para descubrir el poder de los datos y cómo convertirlos en conocimiento significativo. Aprenderás a dominar las herramientas y técnicas más avanzadas de R para analizar, visualizar y manipular datos caóticos. Además, desbloquearás el potencial de la inteligencia artificial al desarrollar modelos de aprendizaje automático capaces de predecir tendencias, clasificar información y comprender el lenguaje humano. ¡Prepárate para convertirte en un experto en la ciencia detrás de los datos y llevar tu capacidad analítica a un nivel completamente nuevo! ¿Listo para desafiar tus límites y cambiar el juego con la ciencia de datos y el aprendizaje automático en R? ¡Únete a nosotros y comienza tu emocionante aventura hacia el futuro de la tecnología y la innovación! Lo mas importante de este curso es que haremos un proyecto real para que puedas tener conocimientos adecuados y útiles en tu vida profesional. Cada que repliques este curso que realizaremos acá, iras aumentando tu probabilidad de tener {éxito en esta área. Es fundamental que tengas toda la disposición de retarte a entender este apasionante mundo. No olvides que cualquier duda puedes contactarme para que nada obstaculice tu aprendizaje

Development, Data Science

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Development, Data Science

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EGAL ob du Python professionell für deinen Job oder privat für dein Hobby erlernen willst. Dieser Kurs ist konzipiert dich ohne Vorkenntnisse zum Data Science Profi mit Python zu machen.Nutze den Python-Kurs mit exzellenten Bewertungen auf Udemy:„Ich bin sehr begeistert! Bin mit fast keinem Wissen hier rein und hab jetzt ein super Verständnis was denn Machine Learning überhaupt ist, man denkt immer das ist absolute Raketenwissenschaft, aber Jannis kann das wirklich super gut erklären, super Investition, danke!!!" (★★★★★, Peter G.)Deine Entscheidung ein Data-Scientist zu sein, kann Dir viele Türen öffnen!Der Bedarf an qualifizierten Leuten ist groß. Mit diesem Kurs legst Du den Grundstein, ein gefragter Experte zu werden für ein Berufsfeld, wo du laut Indeed Jobbörse ein weit überdurchschnittliches Gehalt beziehen kannst!Du wirst Schritt für Schritt an das Thema Python herangeführt und erlaubt dir den direkten Einstieg in die Welt der Data-Science.Der All-Umfassende Python Kurs für Data Science auf Udemy.Mit 252 Lektionen und 29+ Stunden HD-Videos, unzählige Quizze, Tests, Praxisprojekte, Merkblätter und Übungen.Kurz-Überblick:Verstehe alle Python-GrundlagenEntwickle Data-Science ToolsTrainiere dich mit Quizzen und ÜbungenEinfaches Wiederholen von Wissen mit MerkblätternUmfassende Praxisbeispiele, z.B.:Sage das Brustkrebs-Risiko von Patienten vorherErmittle die Gründe für DiabetesWerte echte Gehälter der Stadt San Francisco ausSchätze den Wert von Gebrauchtwagenund noch viel viel mehrLerne mit dem erfolgreichstem deutschen Udemy Dozenten.Skills die dich zum gefragten Data-Science-Experten machen!Nutze ein einzigartiges Kurskonzept - das dir die Möglichkeit gibt, mit praxisorientierten Konzepten und Daten Python mit Ausrichtung Data-Science zu lernen.Was mein Konzept so beliebt macht?Ich lehre praxisorientiert mit Erfahrung und nicht trockene Theorie wie an der Uni.Komplett-Kurs perfekt aufeinander abgestimmtSupport, der auf deine Rückfragen eingehtPraxis erprobtes Lernkonzept mit grafischen, lerneffektiven VeranschaulichungenDu arbeitest mit echten Daten: So macht Machine Learning besonders viel SpaßIdeal für die Job-Vorbereitung, Uni-Klausur oder anderen persönlichen ZielenTop-Aktuelle Kursinhalte die auf langjährige Erfolge gebaut sindEin komplett durchdachter, praxisorientiert Python Komplett-Kurs, der dich in 4 Schritten systematisch sicher ans Ziel führt, ein Experte zu werden!Schaue dir meine Video-Nachricht and dich an!Die 4 Themen für dich im Einzelnen.Dieser Python Kurs ist speziell entwickelt, um dich auf die 4 wichtigen Themen eines Data-Scientist optimal vorzubereiten. Perfekt aufeinander abgestimmt und interessant gestaltet, sodass dein Lernprozess praxisorientiert und effizient ist.Thema 1: Python Grundlagen (für Einsteiger) Thema 2: Data ScienceThema 3: Machine LearningThema 4: Deep Learning (Neuronale Netze)Thema 1: Python GrundlagenPython zeichnet sich durch eine leicht zu erlernende Syntax aus. Python ist performant und objektorientiert.Lerne die Grundlagen von Python kennen. Du lernst alle Datentypen und Funktionen kennen. Bereits nach ein paar Stunden schreibst du schon eine kleinen Spamfilter as dein erstes Praxisprojekt. Am Schluss bist Du in der Lage schon selbst kleinere Programme zu entwickeln.Hast Du schon Programmiererfahrung mit Python?Dann kannst du diesen ersten Abschnitt überspringen und direkt im zweiten Thema einsteigen!Thema 2: Data ScienceWenn Du ein Data-Scientist bist, geht es für dich um fundierte Methoden der Datenanalyse. Ein extrem wichtiges Gebiet in der Wirtschaft, Wissenschaft, Gesundheitswesen und sogar öffentliche Einrichtungen.All diese Institutionen benötigen die Datenanalysen, um z. B. Handlungsempfehlungen abzuleiten, Qualität und Effizienz zu optimieren, u. a.In diesem Abschnitt lernst du, Daten nach Python einzulesen, zu filtern und grafisch auszuwerten.Das heißt, du lernst Daten brauchbar zu machen.Du lernst mit Tools wie Numpy, Pandas, Matplotlib und Seaborn zu arbeiten!Außerdem lernst du in diesem Abschnitt anhand eines echten Praxisprojektes das gelernte umzusetzen: Wir analysieren die Gehälter der Stadt San Francisco.Thema 3: Machine LearningIn Thema 2 hast du die Grundlagen gelernt, um sich jetzt mit dem Machine Learning zu beschäftigen. Was ist Machine Learning?Kurz erklärt, Machine Learning ist ein Teil der künstlichen Intelligenz (KI). Das heißt, es geht um Algorithmen, die die Muster und Gesetzmäßigkeiten der Daten erkennen.Jetzt lernst die unterschiedlichen Arten und Methoden von Machine Learning und wie du diese für Lösungen verwendest.Dazu gehört auch die Aufbereitung von Daten und wie du die Genauigkeit eines Modells beurteilst. Wir werden das in diesem Abschnitt an eigenen, unterschiedlichen Modellen trainieren und nachvollziehen.Als Beispiel wirst du sehen, wie du Diabetes vorhersagen oder Spamfilter verbessern kannst. Python Anwendungstool hier: Sklearn.Thema 4: Deep Learning / Neuronale NetzeJetzt, wo du in Thema 3 Machine Learning erfolgreich gelernt hast, können wir uns dem Thema Deep Learning (Neuronale Netze) widmen.Mit Deep Learning lernst du die spezielle Methode des maschinellen Lernens und die dazugehörige Informationsverarbeitung. Das schliesst die Neuronale Netze Anwendung ein, um die Arbeitsweise des menschlichen Gehirns nachzustellen.Du beginnst mit einem einzelnen Neuron. Mit Fortschritt dieses Lernabschnittes erweitern wir das Modell, damit du am Ende an einem ganzen neuronalen Netz trainierst.All das, was du in Abschnitt 3 gelernt hast, wird dir jetzt hier weiterhelfen, denn es hilft dir, viele Zusammenhänge im Machine Learning 1:1 auf das Prinzip der Neuronalen Netze anzuwenden.Teil deines Lerninhaltes hier ist auch eine Bilderkennung zu schreiben mit Tools wie Keras und Tensorflow.Klingt gut? Dann würde ich mich sehr freuen, wenn ich dir noch heute die Tür zur Welt der Data Science mit Python öffnen kann. Du lernst mit dem erfolgreichsten, deutschsprachigen Dozenten auf Udemy. Schau dir die Bewertungen zu diesem Kurs an und überzeuge dich selbst:)

Development, Data Science

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Development, Data Science

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¿Quiere aprender a crear visualizaciones impactantes con las que dominar el área de Data Science?-------------------------Escuche de otros alumnos por qué este es el curso de Visualización en Data Science MEJOR VALORADO en español:"Excelente Instructor..el desarrollo del tema es muy secuencial.bien estructurado y fácil de seguir..se entiende muy bien.lo recomiendo" - Juan Carlos Murcia--------------------------Al finalizar este curso podrá crear sorprendentes visualizaciones con base estadística de una manera muy sencilla con Python y sus librerías Matplotlib y Seaborn, lo que le hará pasar al siguiente nivel en el análisis de datos para obtener conclusiones que provoquen alto impacto en su entorno.Además, será capaz de analizar series temporales y crear previsiones basadas en tendencias y estacionalidades, algo muy útil para cualquier reto de negocio.Aprenderá también a automatizar sus tareas diarias, puesto que Python es un lenguaje que le permite explotar áreas adicionales al Data Science.No necesita conocer previamente Python o estadística, puesto que habrá bloques opcionales enfocados en enseñarle Python desde cero y los fundamentos estadísticos si lo necesita.Este curso tendrá un enfoque eminentemente práctico, se explicará paso a paso y en detalle cada nueva funcionalidad, pero el objetivo es que sea capaz de aplicar los nuevos conocimientos ejecutando los múltiples casos prácticos reales propuestos para poner a prueba las destrezas adquiridas.A su vez, tendrá a su disposición un material extenso de consulta y todos los scripts de Python explicados durante esta especialización de tal manera que le sea muy sencillo reutilizarlos para su caso de uso concreto.Es el momento de que pase a la acción, prepárese para un futuro dominado por los datos adquiriendo una habilidad muy importante para poder destacar sobre el resto y conseguir sacar el máximo provecho de la información.Apúntese a la carrera profesional de mayor potencial del siglo XXI.*Este curso forma parte de una carrera en Data Science complementada con cursos adicionales.

Development, Data Science

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Development, Data Science

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Course Description: In this comprehensive course, you will learn and practice SQL interview questions tailored specifically for Data Science roles in the year 2023. The course is designed to cover essential SQL topics and equip you with the skills needed to excel in your SQL-related interviews.Course Topics:SQL Basics and Syntax:Understand the fundamental SQL commands and syntax required for querying databases.Practice writing SELECT, FROM, WHERE, and ORDER BY clauses.Mastering joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN) and subqueries.Data Manipulation and DDL:Learn how to manipulate data in SQL using INSERT, UPDATE, and DELETE statements.Understand Data Definition Language (DDL) for creating and modifying database structures.Work with constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL) to ensure data integrity.Data Analysis and Aggregation:Explore the power of SQL aggregations with SUM, COUNT, AVG, MAX, MIN, and other functions.Utilize GROUP BY and HAVING clauses to perform complex data analysis.Gain proficiency in window functions for advanced analytical queries.Advanced SQL Techniques:Master the art of writing complex queries with Common Table Expressions (CTEs).Dive into recursive CTEs for hierarchical data processing.Learn how to pivot and unpivot data for reporting and analysis.Database Design and Normalization:Grasp the principles of database design and normalization.Identify and apply normalization techniques to improve database efficiency.Handle denormalized data and understand trade-offs.Performance Optimization and Query Tuning:Optimize SQL queries for performance.Learn indexing strategies to speed up data retrieval.Interpret query execution plans for query optimization.Sample Questions:How do you use Common Table Expressions (CTEs) to simplify complex queries?What are window functions in SQL, and how are they used?Explain the differences between temporary tables, table variables, and CTEs.How do you handle hierarchical data using SQL?What is the difference between UNION and UNION ALL, and when would you use each?Explain the concepts of database transactions and ACID properties.How do you ensure data consistency and integrity in a multi-user database environment?How do you use the MERGE statement for performing insert, update, and delete operations in a single query?Explain the concept of database denormalization and when it might be appropriate to use it.What are triggers in SQL, and how do they work?How do you use window functions to calculate running totals and moving averages?Explain the differences between PRIMARY KEY, UNIQUE, and FOREIGN KEY constraints.What are recursive common table expressions (CTEs), and when would you use them?How do you use the LAG and LEAD functions to access data from previous and subsequent rows?Explain the purpose of the PARTITION BY clause in window functions. and many more[Enroll NOW]

Development, Data Science

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Development, Data Science

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Welcome to the course " Outlier Detection Techniques ". Are you Data Scientist or Analyst or maybe you are interested in fraud detection for credit cards, insurance or health care, intrusion detection for cyber-security, or military surveillance for enemy activities? Welcome to Outlier Detection Techniques, a course designed to teach you not only how to recognise various techniques but also how to implement them correctly. No matter what you need outlier detection for, this course brings you both theoretical and practical knowledge, starting with basic and advancing to more complex algorithms. You can even hone your programming skills because all algorithms you'll learn have implementation in PYTHON, R and SAS. So what do you need to know before you get started? In short, not much! This course is perfect even for those with no knowledge of statistics and linear algebra. Why wait? Start learning today! Because Everyone, who deals with the data, needs to know "Outlier Detection Techniques"!The process of identifying outliers has many names in Data Mining and Machine learning such as outlier mining, outlier modeling, novelty detection or anomaly detection. Outlier detection algorithms are useful in areas such as: Data Mining, Machine Learning, Data Science, Pattern Recognition, Data Cleansing, Data Warehousing, Data Analysis, and Statistics.I will present you on the one hand, very popular algorithms used in industry, but on the other hand, i will introduce you also new and advanced methods developed in recent years, coming from Data Mining.You will learn algorithms for detection outliers in Univariate space, in Low-dimensional space and also learn innovative algorithm for detection outliers in High-dimensional space.I am convinced that only those who are familiar with the details of the methodology and know all the stages of the calculation, can understand it in depth. So, in my teaching method, I put a stronger emphasis on understanding the material, and less on programming. However, anyone who interested in programming, I developed all algorithms in R , Python and SAS, so you can download and run them. List of Algorithms:Univariate space:1. Three Sigma Rule ( Statistics , R + Python + SAS programming languages)2. MAD ( Statistics , R + Python + SAS programming languages )3. Boxplot Rule ( Statistics , R + Python + SAS programming languages )4. Adjusted Boxplot Rule ( Statistics , R + Python + SAS programming languages ) Low-dimensional Space:5. Mahalanobis Rule ( Statistics , R + Python + SAS programming languages )6. LOF - Local Outlier Factor ( Data Mining , R + Python + SAS programming languages)High-dimensional Space:7. ABOD - Angle-Based Outlier Detection ( Data Mining , R + Python + SAS programming languages) I sincerely hope you will enjoy the course.

Development, Data Science

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Business, Business Analytics & Intelligence

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Learn data visualization through Microsoft Power BI and create opportunities for you or key decision makers to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks. You'll learn all of the features in Power BI that allow you to explore, experiment with, fix, prepare, and present data easily, quickly, and beautifully. Use Power BI to Analyze and Visualize Data So You Can Respond Accordingly Connect Power BI to a Variety of Datasets Drill Down and Up in Your Visualization and Calculate Data Visualize Data in the Form of Various Charts, Plots, and Maps Convert Raw Data Into Compelling Data Visualizations Using Power BI Because every module of this course is independent, you can start in whatever section you wish, and you can do as much or as little as you like. Each section provides a new data set and exercises that will challenge you so you can learn by immediately applying what you're learning. Content is updated as new versions of Power BI are released. You can always return to the course to further hone your skills, while you stay ahead of the competition. Contents and Overview This course begins with Power BI basics. You will navigate the software, connect it to a data file, and export a worksheet, so even beginners will feel completely at ease. To be able to find trends in your data and make accurate forecasts, you'll learn how to work with hierarchies and timeseries. Also, to make data easier to digest, you'll tackle how to use aggregations to summarize information. You will also use granularity to ensure accurate calculations. In order to begin visualizing data, you'll cover how to create various charts, maps, scatterplots, and interactive dashboards for each of your projects. You'll even learn how to join multiple data sources into one in order to combine diverse sources of information in one analytical solution. Finally, you'll cover some of the latest and most advanced custom visualizations in Microsoft Power BI, where you will create histograms, brickcharts and more. By the time you complete this course, you'll be a highly proficient Power BI user. You will be using your skills as a data scientist to extract knowledge from data so you can analyze and visualize complex questions with ease. You'll be fully prepared to collect, examine, and present data for any purpose, whether you're working with scientific data or you want to make forecasts about buying trends to increase profits.

Business, Business Analytics & Intelligence

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Development, Data Science

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Are you a fan of sport but also interested in the numbers? Deep dive into the world of sports analytics with this course on 'Data Science for Sports - Sports Analytics and Visualization', created by The Click Reader. This course provides insights and knowledge into how you can perform analysis on sports data and then, visualize it using Python. We will start the course by looking at the games in the 2018 NFL season. Then, we will move on to look at the player statistics in order to understand the players in the season. We will also look at the plays of the NFL season and finally, end the course by building a data visualization project where we will be visualizing the American Football Field and players on top of it.After completing this course, you will be able to play around with the various available datasets and visualize them in different ways. This course contains hands-on exercises at the end of each lecture and the knowledge you gain through this course can be extended to any other domain of sports. Why you should take this course?Updated 2022 course content: All our course content is updated as per the latest technologies and tools available in the marketPractical hands-on knowledge: This course is oriented to providing a step-by-step implementation guide rather than just sticking to the theory.Guided support: We are always there to guide you through the Q/As so feel free to ask us your queries.

Development, Data Science

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