Practical Machine Learning with Python:
Upskilling Seminar
Online Live - 8 Academic Hours
Empower Your Software Code with AI.
Warum sollten Sie an diesem Seminar teilnehmen?
Machine learning is transforming industries with its capability to solve complex problems and predict outcomes with astonishing accuracy.
Python, as a versatile programming language, stands at the forefront of this revolution, offering a suite of powerful packages and tools that simplify the development of machine learning processes.
Tailored for software developers and data scientists, this seminar offers an overview of the complexities of machine learning with Python.
You’ll become familiar with the core packages and tools that make Python essential for machine learning, from data manipulation to model creation. Engage with hands-on examples and understand how Python can be used in machine learning.
By enrolling in this seminar, you're taking a step towards enhancing your career prospects and gaining valuable skills in one of the most sought-after areas of technology.
Wer sollte teilnehmen?
This seminar is tailored for professionals who are directly involved in the development, analysis, and strategic application of machine learning technologies within their organizations.
This seminar is ideal for:
- Software-Entwickler who are keen to integrate machine learning into their applications.
- Datenwissenschaftler who specialize in extracting insights from complex data sets and want to understand machine learning models.
- Managers, particularly in technology, product development, or innovation roles, who aim to leverage machine learning to drive business strategies and improve outcomes.
Lernziele und Lernergebnisse
Am Ende des Seminars werden die Teilnehmer:
- Gain an understanding in utilizing key Python libraries for machine learning, including Numpy for numerical computations, Pandas for data manipulation, and Matplotlib/Seaborn for data visualization, along with an understanding of Scipy and Scikits for scientific computing.
- Learn the fundamental concepts and differences between various machine learning types, such as supervised, unsupervised, and reinforcement learning, and apply these concepts in real-world data analysis scenarios.
- Get familiarized with key aspects of data preparation and cleaning, learn the importance of data visualization in machine learning, and develop the ability to implement classification and regression models to solve complex problems.
Was Sie bekommen...
- 8 academic hours, 2 live online classes with a professional instructor, teaching in English.
- Cheat sheets and essentials for practical AI development tasks.
- Zugang zu den Aufzeichnungen der Sitzungen für die Dauer des Seminars, falls Sie eine Sitzung verpasst haben oder zur Vertiefung.
- Abschlusszertifikat, auch ein digitales für LinkedIn.
- Lebenslange Mitgliedschaft in der globalen Alumni-Gemeinschaft von Wawiwa, die sich aus Tech-Profis zusammensetzt, die Wawiwa's Umschulungs- und Weiterbildungsprogramme auf der ganzen Welt absolviert haben.
Professioneller Ausbilder
This unique seminar was built by a professional team made up of the leading experts in Software Development and AI, with vast knowledge and experience in training too.
Maor Mugrabi
Instructor of AI Courses for Software Developers
Maor ist ein Datenfreak mit einer großen Leidenschaft für Künstliche Intelligenz (KI) und Maschinelles Lernen (ML).
Maor is dedicated to helping businesses leverage these technologies effectively. Maor is an entrepreneur, managing his own AI/ML development and consultancy firm. Maor’s work has empowered dozens of companies to innovate and grow through custom AI/ML implementations.
Er ist auch Dozent bei John Bryce, wo er sein Wissen und seine Erfahrung im Bereich maschinelles Lernen und Python an seine Studenten weitergibt.
Maor holds a Bachelor’s degree in Applied Mathematics from Bar-Ilan University.
Was haben die Absolventen zu sagen?
Seminar Syllabus & Agenda
Session 1: Important Packages
- Numpy
- Matplotlib and seaborn
- Scipy and Scikits
- Pandas
Session 2: Machine Learning Applications, Processes, and Types of Learning
- Überwachtes Lernen
- Unüberwachtes Lernen
- Semi-Supervised Learning
- Active Learning
- Reinforcement learning
- Variables and features
- Classification and Regressions
- Types of Data
- Data Preparation and Cleaning
- Data Visualization
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