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Prerrequisitos de Aprendizaje Profundo: Regresión Lineal en Python

120,00  10,00 

Prerrequisitos de Aprendizaje Profundo: Regresión Lineal en Python. ciencia de datos: Obtener información de regresión lineal a partir de cero y construir su propio programa de trabajo en Python para el análisis de datos.

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Descripción

No te pierdas este fabuloso curso online llamado Prerrequisitos de Aprendizaje Profundo: Regresión Lineal en Python. Es 100% online y comenzarás justo en el momento de matricularte. Tú serás el que marques tu propio ritmo de aprendizaje.

Breve descripción del curso llamado Prerrequisitos de Aprendizaje Profundo: Regresión Lineal en Python

ciencia de datos: Obtener información de regresión lineal a partir de cero y construir su propio programa de trabajo en Python para el análisis de datos.

El profesor de este fabuloso curso 100% online es Lazy Programmer Inc., un auténtico experto en la materia, y con el que aprenderás todo lo necesario para ser más competitivo. El curso se ofrece en Inglés.

Descripción completa del curso llamado Prerrequisitos de Aprendizaje Profundo: Regresión Lineal en Python

Course Description This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python. Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you’ll be returning to it for years to come. That’s why it’s a great introductory course if you’re interested in taking your first steps in the fields of: deep learningmachine learningdata sciencestatistics In the first section, I will show you how to use 1-D linear regression to prove that Moore’s Law is true. What’s that you say? Moore’s Law is not linear? You are correct! I will show you how linear regression can still be applied. In the next section, we will extend 1-D linear regression to any-dimensional linear regression – in other words, how to create a machine learning model that can learn from multiple inputs. We will apply multi-dimensional linear regression to predicting a patient’s systolic blood pressure given their age and weight. Finally, we will discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, overfitting, train-test splits, and so on. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for FREE. If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want to know how to apply your skills as a software engineer or “hacker”, this course may be useful. This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. NOTES: All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples In the directory: linear_regression_class Make sure you always “git pull” so you have the latest version! HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE: calculuslinear algebraprobabilityPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV file TIPS (for getting through the course): Watch it at 2x.Ask lots of questions on the discussion board. The more the better! Realize that most exercises will take you days or weeks to complete. USEFUL COURSE ORDERING: (The Numpy Stack in Python)Linear Regression in PythonLogistic Regression in Python(Supervised Machine Learning in Python)(Bayesian Machine Learning in Python: A/B Testing)Deep Learning in PythonPractical Deep Learning in Theano and TensorFlow(Supervised Machine Learning in Python 2: Ensemble Methods)Convolutional Neural Networks in Python(Easy NLP)(Cluster Analysis and Unsupervised Machine Learning)Unsupervised Deep Learning(Hidden Markov Models)Recurrent Neural Networks in PythonNatural Language Processing with Deep Learning in Python

Información adicional

Profesor

Lazy Programmer Inc.

Lecciones

29

Duración

3

Nivel

Todos

Idioma

Inglés

Incluye

Acceso de por vida <br/> Devolución a los 30 días garantizada <br/> Disponible en iOS y Android <br/> Certificado de finalización

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