Об этом курсе
4.7
Оценки: 1,367
Рецензии: 170

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Промежуточный уровень

Data Analysis with Python

Прибл. 13 часа на выполнение

Предполагаемая нагрузка: 5-6 weeks of study, 3-6 hours per week...

Английский

Субтитры: Английский

100% онлайн

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

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Промежуточный уровень

Data Analysis with Python

Прибл. 13 часа на выполнение

Предполагаемая нагрузка: 5-6 weeks of study, 3-6 hours per week...

Английский

Субтитры: Английский

Программа курса: что вы изучите

Неделя
1
1 ч. на завершение

Introduction to Machine Learning

In this week, 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....
4 видео ((всего 24 мин.)), 1 тест
4 видео
Welcome3мин
Introduction to Machine Learning8мин
Python for Machine Learning6мин
Supervised vs Unsupervised5мин
1 практическое упражнение
Intro to Machine Learning10мин
Неделя
2
5 ч. на завершение

Regression

In this week, you will get a brief intro to regression. You learn about Linear, Non-linear, Simple and Multiple regression, and their applications. You apply all these methods on two different datasets, in the lab part. Also, you learn how to evaluate your regression model, and calculate its accuracy....
6 видео ((всего 50 мин.)), 5 тестов
6 видео
Simple Linear Regression12мин
Model Evaluation in Regression Models8мин
Evaluation Metrics in Regression Models3мин
Multiple Linear Regression13мин
Non-Linear Regression7мин
1 практическое упражнение
Regression10мин
Неделя
3
5 ч. на завершение

Classification

In this week, you will learn about classification technique. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Also, you learn about pros and cons of each method, and different classification accuracy metrics....
9 видео ((всего 81 мин.)), 5 тестов
9 видео
K-Nearest Neighbours9мин
Evaluation Metrics in Classification7мин
Introduction to Decision Trees4мин
Building Decision Trees10мин
Intro to Logistic Regression7мин
Logistic regression vs Linear regression15мин
Logistic Regression Training13мин
Support Vector Machine8мин
1 практическое упражнение
Classification10мин
Неделя
4
4 ч. на завершение

Clustering

In this section, you will learn about different clustering approaches. You learn how to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations. You understand 3 main types of clustering, including Partitioned-based Clustering, Hierarchical Clustering, and Density-based Clustering....
6 видео ((всего 41 мин.)), 1 материал для самостоятельного изучения, 4 тестов
6 видео
Intro to k-Means9мин
More on k-Means3мин
Intro to Hierarchical Clustering6мин
More on Hierarchical Clustering5мин
DBSCAN6мин
1 материал для самостоятельного изучения
IBM Digital Badge2мин
1 практическое упражнение
Clustering10мин
4.7
Рецензии: 170Chevron Right

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начал новую карьеру, пройдя эти курсы

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Лучшие рецензии

автор: RCFeb 7th 2019

The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.

автор: JJDec 6th 2018

I am happy to have this online education, I drop out my nuclear engineering degree, I am happy to learn practical things with future... I work for IBM also...but I want to become a data scientis

Преподаватели

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SAEED AGHABOZORGI

Ph.D., Sr. Data Scientist
IBM Developer Skills Network

О IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

О специализации ''Профессиональный сертификат IBM в области науки о данных'

Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning. This program consists of 9 courses providing you with latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets. It is a myth that to become a data scientist you need a Ph.D. This Professional Certificate is suitable for anyone who has some computer skills and a passion for self-learning. No prior computer science or programming knowledge is necessary. We start small, re-enforce applied learning, and build up to more complex topics. Upon successfully completing these courses you will have done several hands-on assignments and built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in Data Science. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Data Science. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....
Профессиональный сертификат IBM в области науки о данных

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