Course Outline

Machine Learning Mastery

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Data Preprocessing and Feature Engineering for Machine Learning

Rating

9/10

Duration

2 Days

Course Overview

This course focuses on the critical steps of data preprocessing and feature engineering, essential for building effective machine learning models. Participants will learn techniques for cleaning data, handling missing values, scaling, encoding, and creating meaningful features to enhance model performance. Through hands-on labs and real-world examples, attendees will develop the skills to prepare high-quality datasets for machine learning workflows.

Format of Training

Instructor-led sessions
Hands-on lab activities with Python tools
Practical demonstrations of preprocessing workflows
Group discussions and real-world case studies

Course Objectives

Understand the importance of data preprocessing and feature engineering in machine learning.
Learn techniques for handling missing data and outliers.
Explore scaling, normalization, and encoding methods for numerical and categorical data.
Gain hands-on experience with feature selection and creation.
Apply best practices for preparing datasets for machine learning models.
Develop workflows for automating data preprocessing tasks.
Build confidence in preparing datasets for improved model accuracy and efficiency.

Prerequisites

Course Outline

Day 1: Data Cleaning and Preprocessing

Session 1: Introduction to Data Preprocessing

Importance of clean data for machine learning
Overview of common data preprocessing techniques

Session 2: Handling Missing and Inconsistent Data

Techniques for identifying and handling missing values
Hands-on lab: Imputing missing data with Python libraries

Session 3: Managing Outliers and Data Transformation

Detecting and treating outliers in datasets
Practical demonstration: Transforming skewed data for better model performance

Day 2: Feature Engineering and Optimization

Session 1: Scaling and Encoding Data

Methods for scaling and normalizing numerical features
Encoding techniques for categorical variables (One-hot, Label Encoding)
Hands-on lab: Scaling and encoding data using Scikit-learn

Session 2: Feature Selection and Creation

Techniques for selecting relevant features (PCA, Feature Importance)
Creating new features for improved model performance
Hands-on lab: Feature selection and engineering for a predictive model

Session 3: Automating Preprocessing Workflows

Tools and libraries for automating preprocessing tasks
Practical demonstration: Building a preprocessing pipeline with Scikit-learn

Bespoke Option

We are open to customizing this program to align with your specific learning objectives. If your team has particular goals or areas they wish to focus on, we would be happy to tailor the course outline to meet those needs and ensure the program supports the achievement of your desired outcomes.

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Course Outline

Machine Learning Mastery

Data Preprocessing and Feature Engineering for Machine Learning

Rating

Duration

Course Overview

Format of Training

Course Objectives

Prerequisites

Course Outline

Day 1: Data Cleaning and Preprocessing

Day 2: Feature Engineering and Optimization

Bespoke Option

Further Learning Opportunities

Introduction to Machine Learning Training Course

Machines to Machine (M2M) Training Course

Machine Learning Fundamentals: From Concepts to Applications

Python for Machine Learning: A Hands-On Introduction

Data Preprocessing and Feature Engineering for Machine Learning

Introduction to Neural Networks and Deep Learning Basics Training Course

Building and Evaluating Machine Learning Models: Best Practices Training Course

Ethics and Bias in Machine Learning: Building Responsible AI Systems Training Course

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Data Preprocessing and Feature Engineering for Machine Learning

Course Name: Data Preprocessing and Feature Engineering for Machine Learning

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