Machine Learning course - decision trees and random forests - ON-LINE version

Index: KRW-18459 EAN: 5903351245944
BOTLAND B. DERKACZ SPÓŁKA JAWNA Opinie z ekomi-pl.com
The online course Machine Learning - Decision Trees and Random Forests addresses the use of artificial intelligence in solving real-world problems. It covers decision trees, i.e. operating on logical systems for making decisions, and practical skills for modelling data sets.
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Product description: Machine Learning - decision trees and random forests course

The online course Machine Learning - Decision Trees and Random Forests addresses the use of artificial intelligence in solving real-world problems. It covers decision trees, i.e. operating on logical systems used to make decisions, and practical skills in modeling data sets. Intermediate level course.

Kurs Machine Learning - drzewa decyzyjne i lasy losowe
Kurs Machine Learning - drzewa decyzyjne i lasy losowe

The future of the world is machine learning

Those who have encountered machine learning need no convincing about this field. Artificial intelligence research, or rather the results of this research already available, includes a number of models that work well for certain specific families of problems. Decision trees and derived methods perform well in classification, regression, and anomaly detection, making them among the most versatile. The course shows the algorithms from both the theoretical and practical sides.

Developing your knowledge of ML and Python

Suppose you can already program in Python and are looking for a new challenge, You know the basics of machine learning but decision trees are alien to you. The course will make you gain an understanding of algorithms and transfer them to your own fascinating projects. The level rises gradually - first problems solved with the help of one tree, and later on in the course more trees appear. The examples will be simple and close to everyday life - such as a decision to buy a car or a comparison of apartment prices.

Kurs Machine Learning - drzewa decyzyjne i lasy losowe
Kurs Machine Learning - drzewa decyzyjne i lasy losowe

Overview of the course content

  • Advanced data importing
  • Combining data from multiple sources
  • Creating relationships between tables
  • Organizing Big Data
  • Exporting Data to a Pivot Table
  • Refreshing Changed Data Sets
  • Automatic Relation Detection
  • Analyzing and processing data using DAX language

What is Power Pivot?

Power P ivot is a feature of Microsoft Excel. It is available as an add-in in Excel 2010, 2013 in separate downloads and as an add-in included in Excel 2016. Power Pivot extends a local instance of Microsoft Analysis Services Tabular that is embedded directly into an Excel workbook. It remains an invaluable tool for modeling and analyzing data.

Kurs Machine Learning - drzewa decyzyjne i lasy losowe
Kurs Machine Learning - drzewa decyzyjne i lasy losowe

For database enthusiasts and those who want to learn it

Prior to the release of Power Pivot, Microsoft relied heavily on SQL Server Analysis Services as the engine for its Business Intelligence suite. Power Pivot complements the core BI components of SQL Server within the vision of the Business Intelligence Semantic Model (BISM). It aims to integrate multidimensional analytics on disk formerly known as the Unified Dimensional Model (UDM) with a more flexible in-memory tabular model. As a self-service product, Power Pivot allows users to create data models and calculations without analytics skills and share them directly or through Microsoft SharePoint document libraries.

Complementing learning

Machine learning and deep learning (DL, or deep learning) are emerging fields. They remain closely related to programming skills. Also explore books for programmers and the textbook Deep Learning. Working with the Python language and the Keras library by François Chollet.

Kurs Machine Learning - drzewa decyzyjne i lasy losowe
Kurs Machine Learning - drzewa decyzyjne i lasy losowe

Course highlights

  • Unlimited access, including 24/7 mobile access
  • 6.5 hours of training
  • Tests and assignments
  • 40 lectures
  • 20 test questions
  • Certificate of completion

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Table of contents

Module 1: Introduction

  • Lesson 1: Introduction
  • Lesson 2: How to use the material
  • Lesson 3: Recall of data structures: binary trees
  • Lesson 4: General flowchart of decision trees
  • Lesson 5: Common applications of decision trees

Module 2: Decision trees in classification

  • Lesson 1: Tree learning scheme
  • Lesson 2: Risk of overfitting
  • Lesson 3: Measuring the quality of partitioning
  • Lesson 4: Split quality: Gini impurity
  • Lesson 5: Split quality: entropy
  • Lesson 6: Dataset: transaction prediction
  • Lesson 7: Predicting purchases using decision trees
  • Lesson 8: Other learning parameters - see free lesson
  • Lesson 9: Searching for the best tree configuration
  • Lesson 10: Visualizing the created classifier

Module 3: Regression with decision trees

  • Lesson 1: Tree performance for numerical data
  • Lesson 2: Measuring split quality for regression
  • Lesson 3: Split Quality: MSE (Mean Squared Error)
  • Lesson 4: Split Quality: MAE (Mean Absolute Error) - see free lesson
  • Lesson 5: Dataset: house prices
  • Lesson 6: Building a regression tree for estimating house prices
  • Lesson 7: Trimming the tree to avoid overfitting
  • Lesson 8: Searching for the best regression tree configuration
  • Lesson 9: Interpretation of tree performance

Module 4: Extensions of decision trees

  • Lesson 1: Creating a forest from decision trees: bagging
  • Lesson 2: Extra trees vs Random Forest
  • Lesson 3: Effectiveness of new methods for predicting purchases
  • Lesson 4: Comparison of bagging for price regression
  • Lesson 5: Boosting: a general workflow - see free lesson
  • Lesson 6: AdaBoost vs XGBoost
  • Lesson 7: Boosting for classification and regression
  • Lesson 8: Combining the power of several algorithms via stacking
  • Lesson 9: Testing stacking capabilities

Module 5: Anomaly detection

  • Lesson 1: Data set: seismic shocks
  • Lesson 2: What is an anomaly? Introduction to Isolation Forest
  • Lesson 3: Using decision trees for anomaly detection
  • Lesson 4: Extended Isolation Forest

Module 6: Decision tree capabilities

  • Lesson 1: Explainability of algorithms
  • Lesson 2: Non-obvious examples of the use of decision trees
  • Lesson 3: Conclusion

Same-day shipping

  • Crediting the payment for the order on our account on a business day before 10:00 AM means sending the goods on the same day.
  • All packages are being delivered by GLS Group courier and covered with insurance up to 1200 EUR.
  • You will receive a link to track your package and monitor the progress of delivery as soon as it is shipped.

Terms of delivery 

GLS - zone 1

Austria, Belgium, Croatia, The Czech Republic, Denmark, Germany, Netherlands, Poland, Slovakia 

  • Delivery time: 2-4 days
  • Delivery cost: from 4.18 EUR
  • Insurance: 1200 EUR
GLS - zone 2

Bulgaria, Estonia, France, Hungary, Italy, Latvia, Lithuania, Luxembourg, Portugal, Romania, San Marino, Slovenia

  • Delivery time: 2-5 days
  • Delivery cost: from 5.35 EUR
  • Insurance: 1200 EUR
GLS - zone 3

Finland, Greece, Ireland, Lichtenstein, Spain, Sweden 

  • Delivery time: 2-7 days
  • Delivery cost: from 7.67 EUR
  • Insurance: 1200 EUR

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