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epub | 2.07 MB | English | ASIN:9780141188928 | Author: Brian Aldiss | Year: 2007 Description: Category:Fiction, Literary Anthologies, Science Fiction, Short Stories https://fikper.com/nU4X1PH9qE/Aldiss.Brian.-.A.Science.Fiction.Omnibus.-.2007.rar.html https://rapidgator.net/file/4536d609024058bc55161f15b9196812/Aldiss.Brian.-.A.Science.Fiction.Omnibus.-.2007.rar https://nitroflare.com/view/A650834DD18311B/Aldiss.Brian.-.A.Science.Fiction.Omnibus.-.2007.rar

BBC Science Focus - December 2024 English | 92 pages | True PDF | 165.8 MB With accessible features illustrated with the world's best photography, BBC Focus Magazine explains the theory behind scientific phenomena and really brings science to life. In every issue you'll find news of the latest major scientific developments, a lively Q&A section plus exclusive and astonishing photographic reports that range from the breathtaking to the downright odd. https://fikper.com/DhQo0gioY7/ https://ddownload.com/xk3j2x2rkqd2 https://rapidgator.net/file/0ddd470dd15fb4153b0e36e2f001124a/ https://nitroflare.com/view/30C43031AD46BE9/

The Week Junior Science+Nature UK - Christmas 2024 English | 52 pages | PDF | 47.8 MB Has your child ever asked why the sky's blue, but space is black? How they can help protect their planet? Or what the stretchiest substance in the Universe is? The Week Junior Science+Nature is the mind-blowing magazine with the answer to all these questions - and more! Each monthly issue delivers jaw-dropping facts, amazing activities and incredible photos, to inspire your children and fill them with wonder. https://fikper.com/Thbe558A4m/ https://ddownload.com/pzaupa69xppg https://rapidgator.net/file/206582ce18175caa241fead8a0db9329/ https://nitroflare.com/view/90A7913BC641AFB/

BBC Science Focus - November 2024 English | 92 pages | True PDF | 187.8 MB With accessible features illustrated with the world's best photography, BBC Focus Magazine explains the theory behind scientific phenomena and really brings science to life. In every issue you'll find news of the latest major scientific developments, a lively Q&A section plus exclusive and astonishing photographic reports that range from the breathtaking to the downright odd. https://fikper.com/0WYojhc6sc/ https://fileaxa.com/ml91axcqzxe5 https://ddownload.com/g1spj61xdxhj https://rapidgator.net/file/36b0f02ecf84771f1f0da0d6c47b05db/ https://turbobit.net/tdxvmz946sco.html

Science News - 30 November 2024 English | 36 Pages | True PDF | 10 MB https://fikper.com/iOQ8uArOtv/ https://fileaxa.com/tu5f2qhs2sx9 https://ddownload.com/1503isphkwzo https://rapidgator.net/file/74f902a904f3eb537f9de0508f525ccf/ https://turbobit.net/sf8mmr9h9bq0.html

Is Science Enough?: Forty Critical Questions About Climate Justice - [AUDIOBOOK] mp3 | 223 MB | Author: Aviva Chomsky | Year: 2022 Description: Category:Science & Technology, Ecology & Environmental Sciences, Earth Science, Environmental Sciences, Meteorology & Atmospheric Sciences, Climate & Climatology in Enviromental Science, Meteorology & Atmospheric Science - Pollution Download Link: https://fikper.com/K3FF54ejxT/ https://fileaxa.com/b6eyic90ygkt https://rapidgator.net/file/f031baf85843d82f92f5ae2818ba286f/ https://turbobit.net/dhbfxbcgjqcz.html

Science - 15 November 2024 English | 124 Pages | True PDF | 42 MB https://fikper.com/N2vI2gGedq/ https://fileaxa.com/srlgcs8j1sq9 https://ddownload.com/b7tktt6b8iax https://rapidgator.net/file/19ea18cbbcabcb625ec146fffc0d97f8/ https://turbobit.net/c7voucxc4kk1.html

Science Illustrated Australia - Issue 111 2024 English | 84 pages | True PDF | 36.7 MB Science Illustrated delivers natural science, break through discoveries and an understanding of the world for the entire family. Packed with stunning photography and in-depth editorial it's a visually spectacular gateway to the world looking into the beginning of life to distant objects in the universe. https://fikper.com/m5Q5kfQ8kE/ https://fileaxa.com/jf3fpnc79kyd https://ddownload.com/lkcfp3rnt19a https://rapidgator.net/file/436f7eb78f0d3eed9964a1ad45e7daa2/ https://turbobit.net/8lho04nvpyiw.html

Science - 8 November 2024 English | 174 Pages | True PDF | 113 MB https://fileaxa.com/hy860pz51jx1 https://ddownload.com/zwf8ed1llvzj https://rapidgator.net/file/1e3adbec6018cf31e992d3570866b651/ https://turbobit.net/dfcajsb1wdav.html

Free Download Web Scraping APIs for Data Science 2021 - PostgreSQL+Excel Last updated 2/2023 Created by Dr. Alexander Schlee MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 24 Lectures ( 4h 25m ) | Size: 2.22 GB From Beginner to Advanced | 4 Hands-On Projects What you'll learn web scraping data extraction data mining create your own dataset output data in Excel output your dataframe in PostgreSQL run SQL commands on your dataframe Requirements basic understanding of Python programming Description In this course the students will get to know how to scrape data from the API of a website (if available). We start with the fundamentals and the beginner level project. After that, two different projects will be covered, followed by the advanced project. After scraping data of wach project, the results will be stored inside an Excel file. Within the advanced level project we will create two dofferent datasets with 5000 results each. The goal is to merge both dataframes (total: 10000 results), save it in Excel and output the data in the PostgreSQL database and run SQL commands on our own data.The requirement for this course is basic knowledge of Python Programming. Since we will not cover very difficult Python topics you do not have to be a professional. The most important characteristic is that you are curious about Web Scraping and Data Mining. You should be ready to invest time in gaining the knowledge which is taught in this course.After this course you will have the knowledge and the experience to scrape your own data and create your own dataset. With the help of the course resources you will always have documents you can refer to. If you have a question or if a concept just does not make sense to you, you can ask your questions anytime inside the Q&A - Forum. Either the instructor or other students will answer your question. Thanks to the community you will never have the feeling to learn alone by yourself.Disclaimer : I teach web scraping as a tutor for educational purposes. That's it. The first rule of scraping the web is: do not harm a certain website. The second rule of web crawling is: do NOT harm a certain website. Who this course is for Data Enthusiasts who want to create their own datasets Homepage https://www.udemy.com/course/web-scraping-apis-for-data-science-2021/ Screenshot TakeFile https://takefile.link/7mgui09x6890/wermw.Data.Science.2021..PostgreSQLExcel.rar.html Rapidgator https://rg.to/file/cbadcce3f81aa59180993d77933531a3/wermw.Data.Science.2021..PostgreSQLExcel.rar.html Fikper Free Download https://fikper.com/0Qm7rzvGER/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part2.rar.html https://fikper.com/7lA8et4IFq/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part1.rar.html https://fikper.com/Ei8ue4zEvK/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part3.rar.html https://fikper.com/UTDaWfqO84/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part3.rar.html https://fikper.com/ZohTjua3N7/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part1.rar.html https://fikper.com/oDLDbgjv97/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part2.rar.html https://fikper.com/ps3WIqEP9Y/wermw.Data.Science.2021..PostgreSQLExcel.rar.html No Password - Links are Interchangeable

Free Download Udemy - Diploma In Midwifery Science Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 4.06 GB | Duration: 2h 0m midwifery, midwife, midwifery science, online course, diploma, certification, pregnancy, childbirth, women's health What you'll learn Antenatal care Nutrition during pregnancy and childbirth. Clinical errors in healthcare Post partum Care Family planning And many more Reproductive health and sexual rights Research for midwifery Violence against healthcare professionals Professional development in Midwifery. Global challenges in Midwifery. Disaster management and midwifery Gender violence in midwifery Adolescent sexual health. Mental health during pregnancy Midwifery leadership Midwifery practice in different settings Requirements Basics of obstetrics and Gynaecology will help you in better understanding. Description Become a Certified Midwife and Empower Women Throughout Their Pregnancy Journey!Are you passionate about women's health and childbirth? Do you dream of making a difference in the lives of mothers and their newborns? This Diploma in Midwifery Science online course will equip you with the comprehensive knowledge and practical skills needed to excel as a qualified midwife.In this accredited program, you will:Gain a deep understanding of human anatomy, physiology, and the science of pregnancy and childbirth.Master the art of prenatal care, labor and delivery management, and postpartum care.Develop essential clinical skills in assessment, monitoring, and intervention during pregnancy and childbirth.Learn about the emotional and social aspects of pregnancy and childbirth, providing holistic care to mothers and their families.Explore the ethical and legal considerations of midwifery practice.Gain valuable experience through simulated scenarios and case studies.This course is perfect for:Aspiring midwives seeking a globally recognized qualification.Healthcare professionals who want to specialize in midwifery.Individuals passionate about women's health and empowerment.Here's what you'll get:Expert-led video lectures, tutorials, and demonstrations.Comprehensive study materials, including textbooks, articles, and online resources.Interactive quizzes and assessments to track your progress.24/7 access to a dedicated online learning platform.Individualized support from experienced tutors and mentors.Upon successful completion, you will be eligible to:Sit for the international midwifery licensing exam.Register as a certified midwife with your local regulatory body.Pursue a rewarding career in midwifery, making a positive impact on the lives of women and families.Don't miss this chance to fulfill your dream of becoming a midwife! Enroll now and embark on your journey to a fulfilling and impactful career!Enroll now and start your journey to becoming a certified midwife! Overview Section 1: Introduction Lecture 1 introduction to Midwifery Lecture 2 Antenatal care Lecture 3 The recent advances in midwifery Lecture 4 Nutrition during pregnancy and childbirth. Lecture 5 Documentation and record keeping Importance Lecture 6 Documentation and record keeping introduction Lecture 7 Clinical errors in healthcare Lecture 8 Post partum period Lecture 9 Family planning Lecture 10 Reproductive health and sexual rights Lecture 11 Research for midwifery Lecture 12 Violence against healthcare professionals Lecture 13 Medical terminology overview Lecture 14 Professional development in Midwifery. Lecture 15 Research in midwifery Lecture 16 Technology and healthcare Lecture 17 Quality improvement in Midwifery Lecture 18 Global challenges in Midwifery. Lecture 19 Disaster management and midwifery Lecture 20 Gender violence in midwifery Lecture 21 Adolescent sexual health. 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Free Download Udemy - Data Science Using R (2024) Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 17.31 GB | Duration: 19h 22m "Data Science Using R: Comprehensive Training in Data Analysis, Visualization, and Machine Learning Techniques . What you'll learn Proficient R Programming: Develop a solid foundation in R programming for data manipulation, analysis, and visualization. Statistical Analysis Skills: Apply statistical methods and machine learning algorithms to derive meaningful insights from datasets. Data Visualization Mastery: Create compelling visualizations using ggplot2 to effectively communicate data findings and trends. Practical Application: Complete real-world projects that enhance problem-solving abilities and demonstrate proficiency in data science concepts. Requirements To enroll in the Data Science Using R course, parti[beeep]nts should have a basic understanding of programming concepts, as familiarity with any programming language will facilitate the learning process. A foundational knowledge of statistics is also beneficial, as it will help students grasp essential data analysis techniques more effectively. Additionally, proficiency in general computer literacy and software applications is required to navigate R and its associated tools. Most importantly, a strong eagerness to learn and a curiosity about data science are crucial for success in this course. These prerequisites will ensure that all students are well-prepared to dive into the exciting world of data science. Description This course, "Data Science with R," is designed for aspiring data scientists and analysts seeking to harness the power of R for data manipulation, analysis, and visualization. Parti[beeep]nts will begin by gaining a solid foundation in R programming, covering key concepts such as data types, structures, and essential functions.As the course progresses, students will delve into data wrangling techniques using packages like dplyr and tidyr, enabling them to clean and prepare datasets for analysis. The curriculum emphasizes statistical analysis, including hypothesis testing, regression models, and machine learning algorithms, empowering parti[beeep]nts to draw meaningful insights from their data.Visualization is a key focus, with instruction on using ggplot2 to create informative and engaging graphics that communicate results effectively. Real-world case studies and hands-on projects will provide practical experience, allowing students to apply their skills to actual data challenges.By the end of the course, parti[beeep]nts will have developed a comprehensive toolkit for data science, including proficiency in R, an understanding of statistical methodologies, and the ability to present their findings clearly. This course is perfect for those looking to kickstart a career in data science or enhance their analytical capabilities in any field and sorroundings.IIBM Institute of Business Management. Overview Section 1: R Introduction Lecture 1 R Introduction Section 2: R Implementation, R Data Structures, R Interfaces, R Interfaces Lecture 2 R Implementation, R Data Structures, R Interfaces, R Interfaces Section 3: Data Visualization Using R Software Lecture 3 Introduction to Visualisation - Line Plots and Bar Charts - Pie Chart and Histog Section 4: Predictive Customer Analytics using R - Linear Regression using R Software Lecture 4 Predictive Customer Analytics using R - Linear Regression using R Software-Part1 Lecture 5 Predictive Customer Analytics using R - Linear Regression using R Software-Part2 Section 5: Bank Loan Modelling using R Lecture 6 Logistic Function - Single Predictor Model. Section 6: Sales Promotion Effectiveness -Dimension Reduction using R Software. Lecture 7 Sales Promotion Effectiveness -Dimension Reduction using R Software - Part 1 Lecture 8 Sales Promotion Effectiveness -Dimension Reduction using R Software - Part 2 Section 7: Customer and Market Segmentation - Cluster Analysis using R Lecture 9 Customer and Market Segmentation - Cluster Analysis using R Software-Part 1 Lecture 10 Customer and Market Segmentation - Cluster Analysis using R Software-Part 2 Section 8: Retail Analytics: Market Basket Analysis (MBA) - Association Rule using R. Lecture 11 Association Rule Introduction - Apriori Algorithm - Multiple Association Rules Section 9: Customer Loyalty Analytics- Naïve Bayes Classification using R Software. Lecture 12 Naïve Bayes Introduction - Probabilistic Basics and Probabilistic Classification Section 10: K - Nearest Neighbour (KNN) Using R Software Lecture 13 K - Nearest Neighbour Introduction - K - Nearest Neighbour Algorithm. Section 11: Decision Trees using R Software Lecture 14 What is a Decision Tree? - How to create Decision Tree Section 12: Random Forest using R Software Lecture 15 Ensample of Decision Tree. Section 13: Support Vector Machine - SVM Lecture 16 Linear SVM using Hyperplane - Non-Linear Hyperplane using Kernal Trick. Section 14: Real Time Project - Customer Loyalty Analytics and its Application. Lecture 17 RFM Segmentation and Analysis - Propensity Modelling and its application. Lecture 18 Real Time Project - Customer Loyalty Analytics and its Application Section 15: Real Time Project - Finance Analytics and its Application using R Lecture 19 Credit Risk Analytics using Logistic Regression. Section 16: Course Complete Revision Lecture 20 Course Complete This course is for aspiring data scientists, analysts, and researchers seeking to enhance their R programming skills, gain insights from data, and apply analytical techniques in real-world scenarios. 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Free Download Python For Data Science Your Career Accelerator Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 3.83 GB | Duration: 10h 37m Master Python and Unlock Data Analysis, Visualization, and Machine Learning Skills What you'll learn Learn the basics of Python, including data types, variables, loops, conditionals, and string manipulation. Gain hands-on experience with essential libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization. Learn to clean, transform, and preprocess datasets for analysis, preparing them for real-world data science tasks. Understand the concepts of object-oriented programming (OOP) and apply them to structure Python code efficiently. By the end of the course, students will have completed a data science capstone project, where they will collect, analyze, and present insights from a real-world Requirements No programming experience or knowledge of Data Science required. Just come with a passion to learn. Description Are you ready to embark on an exciting journey into the world of data science? "Python for Data Science: Your Career Accelerator" is meticulously designed to transform beginners into proficient data science professionals, equipping you with the essential skills and knowledge needed to thrive in today's rapidly evolving, data-driven landscape.This comprehensive Python for Data Science course covers:Comprehensive Python Course: Master Python programming from the basics to advanced data science applications, including essential libraries like Pandas and NumPy.Data Analysis: Learn essential techniques to manipulate, clean, and analyze real-world datasets, ensuring your data is ready for actionable insights.Data Visualization: Create impactful visualizations using libraries like Matplotlib and Seaborn to present data in a meaningful way and drive decision-making.Machine Learning: Explore core machine learning concepts and algorithms, from linear regression to classification models, and apply them to solve real-world problems.Hands-on Projects: Work on real-world projects to build practical skills and a strong portfolio for your data science career, preparing you to excel in the field.Career-Focused: Gain the skills to excel in roles like Data Analyst, Data Scientist, or Machine Learning Engineer with the confidence to tackle industry challenges.With a focus on practical, project-based learning, this course equips you with both theoretical knowledge and hands-on experience, ensuring you're ready to succeed in the fast-growing field of data science. Overview Section 1: Python Essentials: From Basics to Collaboration Lecture 1 Welcome Note & Intro to python Lecture 2 Introduction to Google Colab Notebook Lecture 3 Introduction to GitHub Lecture 4 Print & Comment Section 2: Python Basics: Fundamental Concepts and Operations Lecture 5 Variables & Assignment Operators Lecture 6 Understanding Data Types Lecture 7 Understanding Expressions Lecture 8 Arithmetic & Assignment Operators Lecture 9 Relational/Comparison Operators Lecture 10 Logical Operators Lecture 11 Identity & Membership Operators, Type Lecture 12 User Input Section 3: Mastering Conditional Branching in Python Lecture 13 Conditional Statements with Logical Operators Lecture 14 If-elif-else Statements Lecture 15 Switch Case Section 4: Mastering Loops in Python Lecture 16 For Loop Lecture 17 While Loops Lecture 18 Do-While Loop Lecture 19 Break and Continue Statements Section 5: Exploring Functions in Python Lecture 20 Introduction to Functions & Pass Statements in Python Lecture 21 Working with Function Arguments Lecture 22 Functions with Return Types Lecture 23 Understanding Local and Global Variables Lecture 24 Lambda Functions in Python Section 6: Mastering Strings in Python Lecture 25 Creating Strings Lecture 26 Understanding Strings as Arrays Lecture 27 Looping Through Strings Lecture 28 String Manipulation Lecture 29 Essential String Operations Lecture 30 Exploring Useful String Methods Section 7: Mastering Lists in Python Lecture 31 Introduction to Lists Lecture 32 Iterating Through List Items Lecture 33 Exploring List Properties Lecture 34 Mastering List Manipulation Lecture 35 Exploring List Methods in Python Section 8: Mastering Tuples in Python Lecture 36 Introduction to Tuples Lecture 37 Advanced Tuple Operations Lecture 38 Mastering Tuple Operations Lecture 39 Exploring Tuple Methods and Operations Section 9: Mastering Dictionaries in Python Lecture 40 Introduction to Dictionaries Lecture 41 Dictionary Operations Lecture 42 Looping through Dictionaries Lecture 43 Essential Dictionary Methods Section 10: Exploring Sets in Python Lecture 44 Understanding Sets Lecture 45 Exploring Set Operations and Looping Lecture 46 Set Operations Lecture 47 Exploring Set Methods Section 11: Machine Learning with K-Nearest Neighbors Lecture 48 KNN Theory Explained Lecture 49 KNN Regression from Scratch using Python Lecture 50 KNN Classification from Scratch using Python Section 12: Machine Learning with Support Vector Machine Lecture 51 SVM Theory Explained Lecture 52 SVM Regression using Python Lecture 53 SVM Classification using Python Section 13: Machine Learning with K-Means Clustering Lecture 54 Detailed Overview of K-Means Clustering Lecture 55 K-Means Clustering using Python Beginners,Career Switchers,Students,Data Enthusiasts Homepage https://www.udemy.com/course/python-for-data-science-your-career-accelerator/ Rapidgator https://rg.to/file/0b96396af360738b70f969c9f56645d7/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part3.rar.html https://rg.to/file/560bf988f4484f5d21009ea8a33aa391/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part2.rar.html https://rg.to/file/732b91242f1e89a3d5d627f9231d28c2/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part1.rar.html https://rg.to/file/aa873b7af3d02ebdac6ed3f2b635364d/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part4.rar.html Fikper Free Download https://fikper.com/A5VoQywqSj/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part1.rar.html https://fikper.com/ngsEG1P93X/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part2.rar.html https://fikper.com/ww0lblGIhg/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part4.rar.html https://fikper.com/yJgJBFN38T/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part3.rar.html No Password - Links are Interchangeable

Free Download Professional Certificate in Data Science 2024 Last updated 1/2024 Created by Academy of Computing & Artificial Intelligence MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 170 Lectures ( 26h 36m ) | Size: 12 GB Learn All the Skills to Become a Data Scientist[ Machine Learning,Deep Learning, CNN, DCGAN, Python, Java, Algorithms] What you'll learn Python Programming Basics For Data Science Machine Learning -[A -Z] Comprehensive Training with Step by step guidance Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest) Unsupervised Learning - Clustering, K-Means clustering Evaluating the Machine Learning Algorithms : Precision, Recall, F-Measure, Confusion Matrices, Data Pre-processing - Data Preprocessing is that step in which the data gets transformed, or Encoded, to bring it to such a state that now the machine can easily parse it. Algorithm Analysis For Data Scientists KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step Deep Learning -Handwritten Digits Recognition[Step by Step][Complete Project ] Deep Convolutional Generative Adversarial Networks (DCGAN) Java Programming For Data Scientists Kaggle - Covid 19- Classification (Chest X-ray.) - Covid-19 & Pneumonia Developing a CNN From Scratch for CIFAR-10 Photo Classification Requirements Computer & Internet Connection Description At the end of the Course you will have all the skills to become a Data Science Professional. (The most comprehensive Data Science course )1) Python Programming Basics For Data Science - Python programming plays an important role in the field of Data Science2) Introduction to Machine Learning -[A -Z] Comprehensive Training with Step by step guidance3) Setting up the Environment for Machine Learning - Step by step guidance4) Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest)5) Unsupervised Learning6) Evaluating the Machine Learning Algorithms7) Data Pre-processing8) Algorithm Analysis For Data Scientists9) Deep Convolutional Generative Adversarial Networks (DCGAN)10) Java Programming For Data ScientistsCourse Learning OutcomesTo provide awareness of the two most integral branches (Supervised & Unsupervised learning) coming under Machine LearningDescribe intelligent problem-solving methods via appropriate usage of Machine Learning techniques.To build appropriate neural models from using state-of-the-art python framework.To build neural models from scratch, following step-by-step instructions. To build end - to - end solutions to resolve real-world problems by using appropriate Machine Learning techniques from a pool of techniques available. To critically review and select the most appropriate machine learning solutionsTo use ML evaluation methodologies to compare and contrast supervised and unsupervised ML algorithms using an established machine learning framework.Beginners guide for python programming is also inclusive. Introduction to Machine Learning - Indicative Module ContentIntroduction to Machine Learning:- What is Machine Learning ?, Motivations for Machine Learning, Why Machine Learning? Job Opportunities for Machine Learning Setting up the Environment for Machine Learning:-Downloading & setting-up Anaconda, Introduction to Google CollabsSupervised Learning Techniques:-Regression techniques, Bayer's theorem, Naïve Bayer's, Support Vector Machines (SVM), Decision Trees and Random Forest.Unsupervised Learning Techniques:- Clustering, K-Means clusteringArtificial Neural networks[Theory and practical sessions - hands-on sessions]Evaluation and Testing mechanisms :- Precision, Recall, F-Measure, Confusion Matrices, Data Protection & Ethical PrinciplesSetting up the Environment for Python Machine LearningUnderstanding Data With Statistics & Data Pre-processing (Reading data from file, Checking dimensions of Data, Statistical Summary of Data, Correlation between attributes)Data Pre-processing - Scaling with a demonstration in python, Normalization , Binarization , Standardization in Python,feature Selection Techniques : Univariate SelectionData Visualization with Python -charting will be discussed here with step by step guidance, Data preparation and Bar Chart,Histogram , Pie Chart, etc..Artificial Neural Networks with Python, KERASKERAS Tutorial - Developing an Artificial Neural Network in Python -Step by StepDeep Learning -Handwritten Digits Recognition[Step by Step][Complete Project ]Naive Bayes Classifier with Python[Lecture & Demo]Linear regressionLogistic regressionIntroduction to clustering[K - Means Clustering ]K - Means ClusteringThe course will have step by step guidance for machine learning & Data Science with Python.You can enhance your core programming skills to reach the advanced level. By the end of these videos, you will get the understanding of following areas the Python Programming Basics For Data Science - Indicative Module ContentPython ProgrammingSetting up the environmentPython For Absolute Beginners : Setting up the Environment : AnacondaPython For Absolute Beginners : Variables , Lists, Tuples , DictionaryBoolean operationsConditions , Loops(Sequence , Selection, Repetition/Iteration)FunctionsFile Handling in PythonAlgorithm Analysis For Data Scientists This section will provide a very basic knowledge about Algorithm Analysis. (Big O, Big Omega, Big Theta)Java Programming for Data Scientists Deep Convolutional Generative Adversarial Networks (DCGAN)Generative Adversarial Networks (GANs) & Deep Convolutional Generative Adversarial Networks (DCGAN) are one of the most interesting and trending ideas in computer science today. Two models are trained simultaneously by an adversarial process. A generator , learns to create images that look real, while a discriminator learns to tell real images apart from fakes.At the end of this section you will understand the basics of Generative Adversarial Networks (GANs) & Deep Convolutional Generative Adversarial Networks (DCGAN) .This will have step by step guidance Import TensorFlow and other librariesLoad and prepare the datasetCreate the models (Generator & Discriminator)Define the loss and optimizers (Generator loss , Discriminator loss)Define the training loopTrain the modelAnalyze the output Does the course get updated?We continually update the course as well.What if you have questions?we offer full support, answering any questions you have.Who this course is for:Beginners with no previous python programming experience looking to obtain the skills to get their first programming job.Anyone looking to to build the minimum Python programming skills necessary as a pre-requisites for moving into machine learning, data science, and artificial intelligence.Who want to improve their career options by learning the Python Data Engineering skills. 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Free Download Math 0-1 Matrix Calculus in Data Science & Machine Learning Last updated 10/2024 Created by Lazy Programmer Inc. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 40 Lectures ( 6h 55m ) | Size: 2.75 GB A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers What you'll learn Derive matrix and vector derivatives for linear and quadratic forms Solve common optimization problems (least squares, Gaussian, financial portfolio) Understand and implement Gradient Descent and Newton's method Learn to use the Matrix Cookbook Requirements Competence with Calculus and Linear Algebra Optional: Familiarity with Python, Numpy, and Matplotlib to implement optimization techniques Description Welcome to the exciting world of Matrix Calculus, a fundamental tool for understanding and solving problems in machine learning and data science. In this course, we will dive into the powerful mathematics that underpin many of the algorithms and techniques used in these fields. By the end of this course, you'll have the knowledge and skills to navigate the complex landscape of derivatives, gradients, and optimizations involving matrices.Course Objectives:Understand the basics of matrix calculus, linear and quadratic forms, and their derivatives.Learn how to utilize the famous Matrix Cookbook for a wide range of matrix calculus operations.Gain proficiency in optimization techniques like gradient descent and Newton's method in one and multiple dimensions.Apply the concepts learned to real-world problems in machine learning and data science, with hands-on exercises and Python code examples.Why Matrix Calculus? Matrix calculus is the language of machine learning and data science. In these fields, we often work with high-dimensional data, making matrices and their derivatives a natural representation for our problems. Understanding matrix calculus is crucial for developing and analyzing algorithms, building predictive models, and making sense of the vast amounts of data at our disposal.Section 1: Linear and Quadratic Forms In the first part of the course, we'll explore the basics of linear and quadratic forms, and their derivatives. The linear form appears in all of the most fundamental and popular machine learning models, including linear regression, logistic regression, support vector machine (SVM), and deep neural networks. We will also dive into quadratic forms, which are fundamental to understanding optimization problems, which appear in regression, portfolio optimization in finance, signal processing, and control theory.The Matrix Cookbook is a valuable resource that compiles a wide range of matrix derivative formulas in one place. You'll learn how to use this reference effectively, saving you time and ensuring the accuracy of your derivations.Section 2: Optimization Techniques Optimization lies at the heart of many machine learning and data science tasks. In this section, we will explore two crucial optimization methods: gradient descent and Newton's method. You'll learn how to optimize not only in one dimension but also in high-dimensional spaces, which is essential for training complex models. We'll provide Python code examples to help you grasp the practical implementation of these techniques.Course Structure:Each lecture will include a theoretical introduction to the topic.We will work through relevant mathematical derivations and provide intuitive explanations.Hands-on exercises will allow you to apply what you've learned to real-world problems.Python code examples will help you implement and experiment with the concepts.There will be opportunities for questions and discussions to deepen your understanding.Prerequisites:Basic knowledge of linear algebra, calculus, and Python programming is recommended.A strong desire to learn and explore the fascinating world of matrix calculus.Conclusion: Matrix calculus is an indispensable tool in the fields of machine learning and data science. It empowers you to understand, create, and optimize algorithms that drive innovation and decision-making in today's data-driven world. 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Free Download Master Advanced Data Science -Data Scientist AIML Experts TM Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 31h 29m | Size: 15.6 GB Real-World Case Studies and Practical Applications in Data Science What you'll learn Data Science Sessions Part 1 & 2: Understand the foundational methodologies and approaches in data science. Data Science vs Traditional Analysis: Compare modern data science techniques to traditional statistical methods. Data Scientist Journey Parts 1 & 2: Explore the skills, roles, and responsibilities of a data scientist. Data Science Process Overview Parts 1 & 2: Gain insights into the end-to-end data science process. Introduction to Python for Data Science: Learn Python programming for data science tasks and analysis. Python Libraries for Data Science: Master key Python libraries like Numpy, Pandas, and Matplotlib. Introduction to R for Data Science: Get acquainted with R programming for statistical analysis. Data Structures and Functions in Python & R: Handle and manipulate data efficiently using Python and R. Introduction to Data Collection Methods: Understand various data collection techniques, including experimental methods. Data Preprocessing (Parts 1 & 2): Clean and transform raw data to prepare it for analysis. Exploratory Data Analysis (EDA): Detect outliers and anomalies to understand your data better. Data Visualization Techniques: Choose the right visualization methods to represent data insights. Tableau and Data Visualization: Utilize Tableau for advanced data visualization. Inferential Statistics for Hypothesis Testing: Apply inferential statistics to test hypotheses and determine confidence intervals. Introduction to Machine Learning: Learn the fundamentals of machine learning and its applications. Unsupervised Learning (Clustering, DBSCAN, Dimensionality Reduction): Discover patterns and clusters in unlabeled datasets. Supervised Learning (Regression, Classification, Decision Trees): Build and evaluate predictive models using labeled data. Evaluation Metrics for Regression & Classification: Use various metrics to assess machine learning model performance. Model Evaluation and Validation Techniques: Improve model robustness through bias-variance tradeoffs and validation techniques. Ethical Challenges in Data Science: Address ethical concerns in data collection and model deployment. Requirements Anyone can learn this class it is very simple. Description This comprehensive Data Science Mastery Program is designed to equip learners with essential skills and knowledge across the entire data science lifecycle. The course covers key concepts, tools, and techniques in data science, from basic data collection and processing to advanced machine learning models. Here's what learners will explore:Core Data Science Fundamentals:Data Science Sessions Part 1 & 2 - Foundation of data science methodologies and approaches.Data Science vs Traditional Analysis - Comparing modern data science techniques to traditional statistical methods.Data Scientist Journey Parts 1 & 2 - Roles, skills, and responsibilities of a data scientist.Data Science Process Overview Parts 1 & 2 - An introduction to the step-by-step process in data science projects.Programming Essentials:Introduction to Python for Data Science - Python programming fundamentals tailored for data science tasks.Python Libraries for Data Science - In-depth exploration of key Python libraries like Numpy, Pandas, Matplotlib, and Seaborn.Introduction to R for Data Science - Learning the R programming language basics for statistical analysis.Data Structures and Functions in Python & R - Efficient data handling and manipulation techniques in both Python and R.Data Collection & Preprocessing:Introduction to Data Collection Methods - Understanding various data collection techniques, including experimental studies.Data Preprocessing - Cleaning, transforming, and preparing data for analysis (Parts 1 & 2).Exploratory Data Analysis (EDA) - Detecting outliers, anomalies, and understanding the underlying structure of data.Data Wrangling - Merging, transforming, and cleaning datasets for analysis.Handling Missing Data and Outliers - Techniques to manage incomplete or incorrect data.Visualization & Analysis:Data Visualization Techniques - Best practices for choosing the right visualization method to represent data.Tableau and Data Visualization - Leveraging advanced data visualization software.Inferential Statistics for Hypothesis Testing & Confidence Intervals - Key statistical concepts to test hypotheses.Machine Learning Mastery:Introduction to Machine Learning - Core concepts, types of learning, and their applications.Unsupervised Learning (Clustering, DBSCAN, Dimensionality Reduction) - Discovering patterns in unlabeled data.Supervised Learning (Regression, Classification, Decision Trees) - Building predictive models from labeled data.Evaluation Metrics for Regression & Classification - Techniques to evaluate model performance (e.g., accuracy, precision, recall).Model Evaluation and Validation Techniques - Methods for improving model robustness, including bias-variance tradeoffs.Advanced Topics in Data Science:Dimensionality Reduction (t-SNE) - Reducing complexity in high-dimensional datasets.Feature Engineering and Selection - Selecting the best features for machine learning models.SQL for Data Science - Writing SQL queries for data extraction and advanced querying techniques.Ethical Challenges in Data Science - Understanding the ethical implications in data collection, curation, and model deployment.Hands-on Applications & Case Studies:Data Science in Practice Case Study (Parts 1 & 2) - Real-world data science projects, combining theory with practical implementation.End-to-End Python & R for Data Science - Practical coding exercises to master Python and R in real data analysis scenarios.Working with Data Science Applications - Applying data science techniques in real-world situations.By the end of this program, learners will be equipped to handle end-to-end data science projects, including data collection, cleaning, visualization, statistical analysis, and building robust machine learning models. 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Free Download Excel Data Science with Power BI & R Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 6h 49m | Size: 3.29 GB Power BI and R What you'll learn Learn to combine Power BI's visualization with R's advanced statistical analysis for powerful data insights and decision-making in any business environment. Master Power BI's dashboards and R's statistical techniques to analyze trends, forecast outcomes, and drive data-driven decisions for business success. Discover how to integrate Power BI and R for dynamic visualizations, advanced analytics, and actionable insights to enhance your data analysis skills. Elevate your data skills by learning how to merge Power BI's visual tools with R's statistical power to create impactful insights for business decisions. Requirements Basic Knowledge of Power BI: Familiarity with Power BI's interface and basic data visualization techniques is recommended. Understanding of R or Programming Basics: Prior experience with R or a general understanding of programming and data manipulation is helpful, but not mandatory. Foundational Data Analysis Skills: Basic knowledge of data analysis concepts, such as working with datasets, interpreting data, and creating simple reports, is ideal. Access to Power BI and R: Ensure you have access to both Power BI and R installed on your system to follow along with the exercises. No prior advanced experience in Power BI or R is required, as the course will guide you through their integration step by step. Description Excel TV is excited to offer our premium course, Data Science with Power BI & R, led by renowned data science expert, Ryan Wade. This course is designed to equip professionals with the skills to harness the full potential of Power BI's advanced visualization features and the robust statistical computing power of R.In today's data-driven world, businesses are increasingly relying on insights gleaned from vast datasets. Power BI, with its intuitive and dynamic dashboards, allows users to visualize and interpret data with ease. However, to gain deeper, more complex insights, R's statistical computing capabilities are indispensable. This course brings together the best of both worlds, teaching you how to seamlessly integrate these two powerful tools for comprehensive data analysis.Under Ryan Wade's expert guidance, you'll not only learn how to create compelling visual reports using Power BI, but also how to implement advanced statistical techniques with R to analyze trends, forecast outcomes, and derive actionable insights. Whether you're a business analyst, data scientist, or aspiring professional, this course will empower you to take your data analysis skills to the next level.By the end of the course, you'll have mastered the ability to generate impactful, data-driven insights that can drive strategic decision-making within any organization. Don't miss this opportunity to elevate your data science expertise with Excel TV's Data Science with Power BI & R course! Who this course is for Business Analysts: Professionals looking to enhance their data analysis skills by integrating Power BI's visualization with R's advanced statistical capabilities. Data Scientists: Individuals who want to leverage Power BI for interactive dashboards while applying R for deeper statistical analysis and forecasting. Aspiring Data Professionals: Those seeking to enter the data science field, looking to gain practical knowledge in using both Power BI and R for comprehensive data insights. Decision Makers and Strategists: Executives and managers who want to better understand how to drive data-driven decisions using advanced analytics and visualization. Power BI Users: Current Power BI users interested in expanding their skills by incorporating R's robust data manipulation and analysis techniques. Homepage https://www.udemy.com/course/excel-data-science-with-power-bi-r/ Screenshot Rapidgator https://rg.to/file/067a3300e38be570829751f25f7f2b5a/tcjia.Excel.Data.Science.with.Power.BI..R.part4.rar.html https://rg.to/file/6b9530764100166d584881dcf7e71ed7/tcjia.Excel.Data.Science.with.Power.BI..R.part1.rar.html https://rg.to/file/8a148e0e5c0bdf68411decbb31579055/tcjia.Excel.Data.Science.with.Power.BI..R.part2.rar.html https://rg.to/file/eb29c755b50d2599b8b868c3309b5c38/tcjia.Excel.Data.Science.with.Power.BI..R.part3.rar.html Fikper Free Download https://fikper.com/ThZYHJzT6b/tcjia.Excel.Data.Science.with.Power.BI..R.part3.rar.html https://fikper.com/VcodWMJWzP/tcjia.Excel.Data.Science.with.Power.BI..R.part1.rar.html https://fikper.com/Xzrj9V8NFp/tcjia.Excel.Data.Science.with.Power.BI..R.part2.rar.html https://fikper.com/mQ4ka9fx1o/tcjia.Excel.Data.Science.with.Power.BI..R.part4.rar.html No Password - Links are Interchangeable

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