Machine Learning & AI Introduction (RXM402)

Kód kurzu: RXM402

Harness the power of data with modern machine learning tools and techniques. In this one-day course, you will gain hands-on experience with machine learning libraries and toolkits such as Pandas, NumPy, Seaborn and scikit-learn.

Key Benefits for You:

  • Live, instructor-led hands-on labs
  • Develop, tune, and test machine learning models
  • Build models with AI and machine learning

Odborní
certifikovaní lektori

Mezinárodne
uznávané certifikácie

Široká ponuka technických
a soft skills kurzov

Skvelý zákaznicky
servis

Prispôsobenie kurzov
presne na mieru

Termíny kurzov

Počiatočný dátum: Na vyžiadanie

Forma: Prezenčná/Virtuálna

Dĺžka kurzu: 1 deň

Jazyk: en/cz

Cena bez DPH: 660 EUR

Registrovať

Počiatočný
dátum
Miesto
konania
Forma Dĺžka
kurzu
Jazyk Cena bez DPH
Na vyžiadanie Prezenčná/Virtuálna 1 deň en/cz 660 EUR Registrovať
G Garantovaný kurz

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Kontakt

Popis kurzu

How will this course benefit me?

  • Harness the Power of Data
  • Gain Competitive ML Skills
  • Demonstrate Your AI Skills to Others
  • Advance Your Career

This course is lab heavy and provides hands-on experience with a variety of ML libraries and toolkits.

The Linux Foundation has partnered with the AI/ML experts at rx-m to offer this course to the community.

Cieľová skupina

Developers. Data Engineers, IT and QA Staff. Technical Managers. DevOps Engineers. Analysts. Project Managers and more!

Štruktúra kurzu

Module 1: AI/Machine Learning Overview and Data Processing
  • Lab: Setting up a Machine Learning Environment and Processing Data with NumPy and Pandas
Module 2: Data Visualization
  • Lab: Visualizing Data with Seaborn and Matplotlib
Module 3: Machine Learning Models
  • Lab: Building Models with scikit-learn
Module 4: Hyperparameter Tuning
  • Labs: Hyperparmeter Tuning with Optuna

Predpokladané znalosti

Participants should have basic programming skills (preferably in Python), a foundational understanding of mathematics and statistics, some experience with data analysis.

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pruduktová podpora