Machine learning with python coursera

Результатов для запроса »machine learning python’: 632

Получаемые навыки: Data Mining, Machine Learning, Machine Learning Algorithms, Python Programming, General Statistics, Applied Machine Learning, Data Analysis, Regression, Statistical Analysis, Statistical Machine Learning, Deep Learning, Probability & Statistics, Estimation, Algorithms, Data Management, Data Structures, Entrepreneurship, Supply Chain Systems, Supply Chain and Logistics

Machine Learning

Получаемые навыки: Machine Learning, Probability & Statistics, Machine Learning Algorithms, General Statistics, Theoretical Computer Science, Algorithms, Applied Machine Learning, Artificial Neural Networks, Regression, Econometrics, Computer Programming, Deep Learning, Python Programming, Statistical Programming, Mathematics, Tensorflow, Data Management, Data Structures, Statistical Machine Learning, Reinforcement Learning, Probability Distribution, Mathematical Theory & Analysis, Data Analysis, Data Mining, Linear Algebra, Computer Vision, Calculus, Feature Engineering, Bayesian Statistics, Operations Research, Research and Design, Strategy and Operations, Computational Logic, Accounting, Communication

Applied Machine Learning in Python

Получаемые навыки: Applied Machine Learning, Data Analysis, Data Mining, Machine Learning, Machine Learning Algorithms, General Statistics, Statistical Machine Learning, Dimensionality Reduction, Feature Engineering, Python Programming, Regression, Estimation, Linear Algebra, Statistical Tests, Algorithms, Artificial Neural Networks, Computer Programming, Econometrics, Exploratory Data Analysis, Probability & Statistics, Theoretical Computer Science

Applied Data Science with Python

Получаемые навыки: Python Programming, Machine Learning, Data Analysis, Data Mining, Data Science, Machine Learning Algorithms, Computer Science, Statistical Programming, Applied Machine Learning, Graph Theory, Mathematics, General Statistics, Basic Descriptive Statistics, Statistical Machine Learning, Data Structures, Natural Language Processing, Regression, Dimensionality Reduction, Exploratory Data Analysis, Feature Engineering, Statistical Analysis, Statistical Tests, Correlation And Dependence, Estimation, Linear Algebra, Computer Programming, Data Architecture, Probability & Statistics, Statistical Visualization, Algorithms, Artificial Neural Networks, Computational Logic, Computer Graphics, Data Visualization, Econometrics, Machine Learning Software, Mathematical Theory & Analysis, Network Analysis, Plot (Graphics), Programming Principles, Theoretical Computer Science

Python for Data Science, AI & Development

Получаемые навыки: Data Analysis, Python Programming, Data Structures, Programming Principles, Algebra, Basic Descriptive Statistics, Exploratory Data Analysis, Computational Logic, Computer Programming, Mathematical Theory & Analysis, Mathematics, Statistical Programming, Theoretical Computer Science

Introduction to Machine Learning

Получаемые навыки: Applied Machine Learning, Data Analysis, Data Architecture, Data Mining, Data Science, Deep Learning, Machine Learning, Machine Learning Algorithms, Machine Learning Software, Statistical Machine Learning, Statistical Analysis, Algorithms, Artificial Neural Networks, Computer Vision, General Statistics, Natural Language Processing, Probability & Statistics, Python Programming, Regression, Theoretical Computer Science

Источник

Машинное обучение с использованием Python

Мы попросили всех учащихся предоставить отзывы о качестве преподавания наших специалистов.

Доступна финансовая помощь

347 110 уже зарегистрированы

Курс

Рекомендуется

A working knowledge of Python and Data Analysis and Visualization techniques. A minimum of high school math.

Чему вы научитесь

Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression

Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees

Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics

Получаемые навыки

Подробнее

Добавить в ваш профиль LinkedIn

Языки

Доступен на таких языках: Английский

Субтитры: Немецкий, Русский, Корейский, Португальский (бразильский), Английский, Итальянский, Французский, Испанский, Арабский, Португальский (Европа), Вьетнамский, Тайский, Индонезийский , Персидский

Курс

Рекомендуется

A working knowledge of Python and Data Analysis and Visualization techniques. A minimum of high school math.

Узнайте, как сотрудники ведущих компаний осваивают востребованные навыки

Получайте профильные знания по нужным предметам

• Узнавайте о новых концепциях у отраслевых экспертов
• Получите базовые знания о предмете или инструменте
• Развивайте профессиональные навыки с помощью практических проектов
• Получите профессиональный сертификат, ссылкой на который можно поделиться, от IBM

В этом курсе 6 модулей

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.

This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.

Introduction to Machine Learning

In this module, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You’ll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.

Что включено

Show info about module content

• Course Introduction • 4 мин. • Предварительный просмотр модуля
• Welcome • 3 мин.
• Introduction to Machine Learning • 8 мин.
• Python for Machine Learning • 6 мин.
• Supervised vs Unsupervised • 6 мин.

• Graded Quiz: Intro to Machine Learning • 15 мин.
• Practice Quiz: Intro to Machine Learning • 10 мин.

Источник

Результатов для запроса »machine learning python’: 632

Получаемые навыки: Data Mining, Machine Learning, Machine Learning Algorithms, Python Programming, General Statistics, Applied Machine Learning, Data Analysis, Regression, Statistical Analysis, Statistical Machine Learning, Deep Learning, Probability & Statistics, Estimation, Algorithms, Data Management, Data Structures, Entrepreneurship, Supply Chain Systems, Supply Chain and Logistics

Machine Learning

Получаемые навыки: Machine Learning, Probability & Statistics, Machine Learning Algorithms, General Statistics, Theoretical Computer Science, Algorithms, Applied Machine Learning, Artificial Neural Networks, Regression, Econometrics, Computer Programming, Deep Learning, Python Programming, Statistical Programming, Mathematics, Tensorflow, Data Management, Data Structures, Statistical Machine Learning, Reinforcement Learning, Probability Distribution, Mathematical Theory & Analysis, Data Analysis, Data Mining, Linear Algebra, Computer Vision, Calculus, Feature Engineering, Bayesian Statistics, Operations Research, Research and Design, Strategy and Operations, Computational Logic, Accounting, Communication

Applied Machine Learning in Python

Получаемые навыки: Applied Machine Learning, Data Analysis, Data Mining, Machine Learning, Machine Learning Algorithms, General Statistics, Statistical Machine Learning, Dimensionality Reduction, Feature Engineering, Python Programming, Regression, Estimation, Linear Algebra, Statistical Tests, Algorithms, Artificial Neural Networks, Computer Programming, Econometrics, Exploratory Data Analysis, Probability & Statistics, Theoretical Computer Science

Applied Data Science with Python

Получаемые навыки: Python Programming, Machine Learning, Data Analysis, Data Mining, Data Science, Machine Learning Algorithms, Computer Science, Statistical Programming, Applied Machine Learning, Graph Theory, Mathematics, General Statistics, Basic Descriptive Statistics, Statistical Machine Learning, Data Structures, Natural Language Processing, Regression, Dimensionality Reduction, Exploratory Data Analysis, Feature Engineering, Statistical Analysis, Statistical Tests, Correlation And Dependence, Estimation, Linear Algebra, Computer Programming, Data Architecture, Probability & Statistics, Statistical Visualization, Algorithms, Artificial Neural Networks, Computational Logic, Computer Graphics, Data Visualization, Econometrics, Machine Learning Software, Mathematical Theory & Analysis, Network Analysis, Plot (Graphics), Programming Principles, Theoretical Computer Science

Python for Data Science, AI & Development

Получаемые навыки: Data Analysis, Python Programming, Data Structures, Programming Principles, Algebra, Basic Descriptive Statistics, Exploratory Data Analysis, Computational Logic, Computer Programming, Mathematical Theory & Analysis, Mathematics, Statistical Programming, Theoretical Computer Science

Introduction to Machine Learning

Получаемые навыки: Applied Machine Learning, Data Analysis, Data Architecture, Data Mining, Data Science, Deep Learning, Machine Learning, Machine Learning Algorithms, Machine Learning Software, Statistical Machine Learning, Statistical Analysis, Algorithms, Artificial Neural Networks, Computer Vision, General Statistics, Natural Language Processing, Probability & Statistics, Python Programming, Regression, Theoretical Computer Science

Источник