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Descubriendo funciones trigonométricas inversas con Python
En la matemática escolar es importante abordar varias perspectivas y técnicas para asimilar los conceptos matemáticos, realizar procedimientos con entendimiento e interpretar los resultados. Por ejemplo, las funciones matemáticas pueden ser estudiadas mediante expresiones algebraicas y trascendentes y representadas en listas, tablas y gráficas.
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Course by
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Self Paced
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3 ساعات
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الإسبانية

Graficando funciones trigonométricas con Python
En la matemática escolar es importante abordar varias perspectivas y técnicas para asimilar los conceptos matemáticos, realizar procedimientos con entendimiento e interpretar los resultados. Por ejemplo, las funciones matemáticas pueden ser estudiadas mediante expresiones algebraicas y trascendentes y representadas en listas, tablas y gráficas.
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Course by
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Self Paced
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3 ساعات
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الإسبانية

Intro to TensorFlow em Português Brasileiro
O objetivo deste curso é aproveitar a flexibilidade e a facilidade de uso do TensorFlow 2.x e do Keras para criar, treinar e implantar modelos de machine learning. Você aprenderá sobre a hierarquia da API TensorFlow 2.x e conhecerá os principais componentes do TensorFlow nos exercícios práticos. Mostraremos como trabalhar com conjuntos de dados e colunas de atributos. Você aprenderá a projetar e criar um pipeline de entrada de dados do TensorFlow 2.x.
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Course by
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Self Paced
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البرتغالي

Graficando funciones trigonométricas inversas con Python
En la matemática escolar es importante abordar varias perspectivas y técnicas para asimilar los conceptos matemáticos, realizar procedimientos con entendimiento e interpretar los resultados. Por ejemplo, las funciones matemáticas pueden ser estudiadas mediante expresiones algebraicas y trascendentes y representadas en listas, tablas y gráficas.
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Course by
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Self Paced
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2 ساعات
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الإسبانية

Aprendiendo Python con textos, números y ecuaciones
En este curso basado en un proyecto, aprenderás a crear un programa en Python para resolver ecuaciones lineales, y explorarás objetos, sentencias y funciones de Python para procesar textos y números. Al finalizar este proyecto habrás creado una aplicación que ayudará a los estudiantes y profesores a practicar con expresiones y ecuaciones lineales o de primer grado.
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Course by
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Self Paced
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3 ساعات
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الإسبانية

Tabulando funciones trigonométricas con Python
En la matemática escolar es importante abordar varias perspectivas y técnicas para asimilar los conceptos matemáticos, realizar procedimientos con entendimiento e interpretar los resultados. Por ejemplo, las funciones matemáticas pueden ser estudiadas mediante expresiones algebraicas y trascendentes y representadas en listas, tablas y gráficas.
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Course by
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Self Paced
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3 ساعات
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الإسبانية

Data Science Coding Challenge: Loan Default Prediction
In this coding challenge, you'll compete with other learners to achieve the highest prediction accuracy on a machine learning problem. You'll use Python and a Jupyter Notebook to work with a real-world dataset and build a prediction or classification model. Important Information: How to register? To participate, you’ll need to complete simple steps. First, click the “Start Project” button to register. Next, you’ll need to create a Coursera Skills Profile, which only takes a few minutes.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية

Hierarchical Clustering using Euclidean Distance
By the end of this project, you will create a Python program using a jupyter interface that analyzes a group of viruses and plot a dendrogram based on similarities among them.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية

Mathematics for Machine Learning: Linear Algebra
In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية

Introduction to Big Data with Spark and Hadoop
This self-paced IBM course will teach you all about big data! You will become familiar with the characteristics of big data and its application in big data analytics. You will also gain hands-on experience with big data processing tools like Apache Hadoop and Apache Spark. Bernard Marr defines big data as the digital trace that we are generating in this digital era. You will start the course by understanding what big data is and exploring how insights from big data can be harnessed for a variety of use cases.
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Course by
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Self Paced
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24 ساعات
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الإنجليزية

Understanding and Visualizing Data with Python
In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data.
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Course by
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Self Paced
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21 ساعات
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الإنجليزية

Inferential Statistical Analysis with Python
In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية

Introduction to Computer Vision and Image Processing
Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this beginner-friendly course, you will understand computer vision and learn about its various applications across many industries. As part of this course, you will utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection. This is a hands-on course and involves several labs and exercises.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية

Fundamentals of Scalable Data Science
Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models.\n\nIn this course we teach you the fundamentals of Apache Spark using python and pyspark.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية

Fitting Statistical Models to Data with Python
In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods.
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Course by
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Self Paced
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15 ساعات
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الإنجليزية