Data analyzing and Machine Learning Hands-on with KNIME

🌟 Course Title: Data Analyzing and Machine Learning Hands-On with KNIME Analytics Platform 🚀

Headline: Dive into Data Science without Coding! A Crash Course on KNIME Analytics Platform 🎓

Course Description:

Get ready to embark on a comprehensive journey through the world of data analysis and machine learning with the open source Knime Analytics Platform. In this course, led by Barbora Stetinova, MBA, you'll master the art of transforming and visualizing data frames, as well as creating robust predictive models. Whether you're a beginner or looking to expand your skillset, this hands-on experience will equip you with the knowledge and tools needed to analyze data efficiently and effectively. 📊✨

Course Structure:

1. PRE-PROCESSING DATA: TRANSFORMING AND VISUALIZING DATA FRAMES

In this section, we will delve into the core operations of data modeling, transformation, and preparation. You'll learn how to:

• Transform Data Frames: Merge data, extract table information, transpose tables, group by, pivot, and more.
• Row Operations: Filter rows based on various criteria.
• Column Operations: Split columns, handle missing values, change data types, perform basic math operations, and much more.
• Data Visualization: Create compelling visualizations such as column charts, line plots, pie charts, scatter plots, and box plots to glean insights from your data.

2. MACHINE LEARNING - REGRESSION AND CLASSIFICATION:

Moving on to the realm of machine learning, you'll follow a standard process to create predictive models:

• Data Collection: Learn how to import data frames into KNIME using reading nodes. (Data sets provided in the course!)
• Pre-processing Data: Transform your data to ensure it's ready for prediction.
• Data Visualization: Use KNIME visual nodes to create basic plots and charts for a clear understanding of your data.
• Understanding Machine Learning: Gain insights into what machine learning entails and why it's crucial.
• Creating Predictive Models: Dive into the world of machine learning algorithms, including:
  • Regression Techniques: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression.
  • Classification Algorithms: Decision Tree Classification, Decision Tree Regression, Random Forest Regression, Random Forest Classification, Naive Bayes, Support Vector Machine (SVM), and Gradient Booster.
• Evaluating Models: Learn how to evaluate the performance of your models using various metrics.

What You'll Learn:

• The Knime Analytics Platform environment, including installation guidance.
• Where to find help and hints for a smooth learning experience.
• A dedicated lecture on Metanodes and Components to deepen your understanding of KNIME's capabilities.

Join us in this hands-on crash course and transform the way you approach data analysis and machine learning with KNIME Analytics Platform! 🤖💡

Enroll now and start your journey towards becoming a data science expert with Barbora Stetinova, MBA. Let's unlock the potential of your data together! 🎉🛠️