Overview What You Will Learn Curriculum Instructor

Course Overview

Scikit-learn is one of the most widely used machine learning libraries in Python, offering simple and efficient tools for data mining and data analysis. It provides built-in support for numerous algorithms and utilities, making it an essential toolkit for beginners and professionals alike. Learning Scikit-learn allows aspiring data scientists and analysts to build, train, and evaluate models with ease, offering a practical gateway into the world of machine learning and predictive analytics.

This course begins with a gentle introduction to the library, followed by key preprocessing techniques necessary for preparing data for modeling. Learners will then explore core supervised learning concepts through dedicated modules on regression and classification, before diving into scenarios that blend both approaches. Clustering techniques are also introduced to tackle unsupervised learning challenges. The course wraps up with an in-depth look at model selection and evaluation strategies to help optimize performance and avoid common pitfalls.

By the end of the course, you'll be confident in using Scikit-learn to build, test, and evaluate a wide range of machine learning models.

What You Will Learn

  • This course is a one stop shop for an introduction to sklearn, the most commonly used Python package for statistical modelling
  • This course will cover all aspects of the modelling workflow
  • We will look at Preprocessing, Regressions, Classifications, Neural Networks and Clustering algorithms
  • We will also cover Evaluation methodology for building highly successful models

Program Curriculum

  • Preprocessing
  • $3.5 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • Regression
  • Chapter 2 Quiz

  • Classification
  • Chapter 3 Quiz

  • Regression and Classification
  • Chapter 4 Quiz

  • Clustering
  • Chapter 5 Quiz

  • Model Selection and Evaluation
  • Chapter 6 Quiz

  • Final Thoughts
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Instructor

Dhruva Krishna

Dhruva is a Data Scientist with experience in the Trading, IoT and Sports Analytics sectors. He spent considerable time understanding Machine Learning models, both in Python and on a theoretical level and building complex visualizations in BI tools such as Tableau. He has been responsible for building high-speed trading algorithms in the foreign exchange and commodities markets, both from a technical and fundamental analysis perspective. He also has built statistical modelling tools in Sports Analysis, for both the Betting markets and Behavioral Analysis sector.

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