Preparing Data for Machine Learning Training Course
Course Overview
This course equips participants with the essential skills to prepare data for machine learning models. It covers feature engineering, normalization, encoding, and techniques for handling both categorical and numerical data. With a focus on practical applications, participants will learn how to preprocess and transform datasets to improve the performance of machine learning models.
Format of Training
- Instructor-led sessions with step-by-step demonstrations
- Hands-on lab exercises to practice data preparation techniques
- Real-world datasets for applied learning
- Group discussions and collaborative problem-solving
Course Objectives
- Understand the importance of data preparation in machine learning.
- Perform feature engineering to create meaningful variables.
- Apply normalization and standardization techniques.
- Encode categorical data using techniques like one-hot encoding.
- Handle missing data and outliers in datasets.
- Prepare datasets for various machine learning algorithms.
- Develop reusable workflows for efficient data preprocessing.
Prerequisites
- Basic understanding of machine learning concepts
- Familiarity with Python programming basics
- Experience with handling datasets in tools like Pandas
- Interest in learning data preprocessing techniques
Course Outline
Day 1
Session 1: Introduction to Data Preparation for Machine Learning
- Overview of the data preparation process
- Importance of clean and well-prepared data
- Key challenges in data preprocessing
Session 2: Feature Engineering Essentials
- Creating new features from existing data
- Transformations and polynomial features
- Hands-on lab: Engineering features from a dataset
Session 3: Handling Missing and Inconsistent Data
- Techniques for imputing missing values
- Addressing outliers and inconsistent entries
- Hands-on lab: Cleaning and imputing data
Day 2
Session 1: Encoding Categorical Data
- One-hot encoding, label encoding, and target encoding
- Choosing the right encoding technique for the task
- Hands-on lab: Encoding categorical variables
Session 2: Scaling and Normalization
- Importance of scaling in machine learning
- Techniques for standardization and normalization
- Hands-on lab: Applying scaling techniques
Session 3: Integrating Data Preparation into ML Workflows
- Preparing data for specific machine learning algorithms
- Automating the data preparation process
- Case study: Preparing a dataset for a machine learning project
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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