Inhalt: Discover how to use Python-and some essential machine learning concepts-to build programs that can make recommendations. In this hands-on course, Lillian Pierson, P.E. covers the different types of recommendation systems out there, and shows how to build each one. She helps you learn the concepts behind how recommendation systems work by taking you through a series of examples and exercises. Once you're familiar with the underlying concepts, Lillian explains how to apply statistical and machine learning methods to construct your own recommenders. She demonstrates how to build a popularity-based recommender using the Pandas library, how to recommend similar items based on correlation, and how to deploy various machine learning algorithms to make recommendations. At the end of the course, she shows how to evaluate which recommender performed the best. Umfang: 01:38:56.00
Inhalt: Daten, Daten, Daten? Sie haben schon Kenntnisse in Excel und Statistik, wissen aber noch nicht, wie all die Datensätze helfen sollen, bessere Entscheidungen zu treffen? Von Lillian Pierson bekommen Sie das dafür notwendige Handwerkszeug: Bauen Sie Ihre Kenntnisse in Statistik, Programmierung und Visualisierung aus. Nutzen Sie Python, R, SQL, Excel und KNIME. Zahlreiche Beispiele veranschaulichen die vorgestellten Methoden und Techniken. So können Sie die Erkenntnisse dieses Buches auf Ihre Daten übertragen und aus deren Analyse unmittelbare Schlüsse und Konsequenzen ziehen. Umfang: 338 S. graph. Darst. ISBN: 978-3-527-80675-1
Inhalt: Data science is a rapidly expanding field offering a wealth of possibilities for viewing the world around us through a more accurate lens. But for many of those whose imagination is sparked by big data-but who have already started pursuing a career in another field-the dream of becoming a data scientist can feel far-fetched. Lillian Pierson, P.E.-a leading expert in the field of big data and data science-aims to prove that notion wrong. In this course, she shares observations and tips to help you embark on a career in this exciting field, regardless of your starting point. Lillian began her career not as a data scientist, but as an environmental engineer. Here, she shares her story, discussing how she taught herself to code in Python and R, and work with data science methodologies. As a result of her own experiences, Lillian is passionate about helping those interested in data science-but who may lack a four-year degree in the discipline-get started in the field. She shares practical ways to acquire the skills and experience needed to become a data scientist, and best practices for landing a job. Lillian also dives into grappling with the challenges that occur in rapidly evolving tech workforces. Plus, she discusses the industry itself, covering recent changes in the field and areas of need, and clearing up a few common misconceptions. Umfang: 00:23:51.00
Inhalt: Python for Data Science Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts-for even more hands-on experience with Python. In this course, instructor Lillian Pierson takes you step by step through a practical data science project: a web scraper that downloads and analyzes data from the web. Along the way, she introduces techniques to clean, reformat, transform, and describe raw data; generate visualizations; remove outliers; perform simple data analysis; and generate interactive graphs using the Plotly library. You should walk away from this training with basic coding experience that you can take to your organization and quickly apply to your own custom data science projects. Umfang: 06:02:00.00
Inhalt: Python for Data Science Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts-for even more hands-on experience with Python. In this course, instructor Lillian Pierson takes you step by step through a practical data science project: building machine learning models that can generate predictions and recommendations and automate routine tasks. Along the way, she shows how to perform linear and logistic regression, use K-means and hierarchal clustering, identify relationships between variables, and use other machine learning tools such as neural networks and Bayesian models. You should walk away from this training with hands-on coding experience that you can quickly apply to your own data science projects. Umfang: 03:44:22.00
Inhalt: Ao usar o Python para obter o valor de dados brutos, você pode simplificar a jornada muitas vezes complexa que é transformar dados em informação. Neste curso prático com muita mão na massa, aprenda como usar o Python e as mais diversas bibliotecas utilizadas em ciência de dados hoje para preparar, coletar e visualizar dados e também a realizar diversos tipos de análise. Aprenda também a aplicar diversas técnicas supervisionadas e não-supervisionadas de machine learning, como capturar dados disponíveis na internet e também como fazer diversos tipos de visualizações. Umfang: 04:03:55.00
Programm Findus Internet-OPAC findus.pl V20.235/8 auf Server windhund2.findus-internet-opac.de,
letztes Datenbankupdate: 09.05.2024, 13:57 Uhr. 964 Zugriffe im Mai 2024. Insgesamt 511.194 Zugriffe seit Januar 2009
Mobil - Impressum - Datenschutz - CO2-Neutral