What does the Data Preprocessing for Machine Learning course cover?
Data Preprocessing for Machine Learning is covered here in 5 modules: Introduction to Data Preprocessing: Overview of data preprocessing techniques, Data Cleaning: Handling missing values, Data transformation techniques, Feature Scaling and Engineering: Feature engineering techniques and 2 more. The outline lists 17 specific topics, opening with what is data preprocessing?
How do you approach Data Preprocessing for Machine Learning step by step?
The work is sequenced in 5 stages. It starts with Introduction to Data Preprocessing: Overview of data preprocessing techniques, moves through Data Cleaning: Handling missing values, Data transformation techniques and Feature Scaling and Engineering: Feature engineering techniques, and ends at Hands-on Projects and Case Studies: Project 1: Data preprocessing for a regression problem.
What is in Module 1 of the Data Preprocessing for Machine Learning course?
Module 1 is Introduction to Data Preprocessing: Overview of data preprocessing techniques. It works through what is data preprocessing?, why is data preprocessing important in machine learning? and overview of data preprocessing techniques. It sets the vocabulary the remaining 4 modules build on.
How is the Data Preprocessing for Machine Learning course delivered?
The Data Preprocessing for Machine Learning course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.
How much does the Data Preprocessing for Machine Learning course cost?
The Data Preprocessing for Machine Learning course is $199 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.
Closely related courses: Data Preprocessing in Machine Learning for Business, Machine Learning Explained, Data Wrangling and Preprocessing, Unlocking Social Media Insights.
More answers: what you get with every course, refund policy, all help answers.
Data Preprocessing for Machine Learning: A Beginner's Guide
Course Overview
Welcome to our comprehensive course on Data Preprocessing for Machine Learning! In this beginner-friendly course, you'll learn the fundamentals of data preprocessing and how to apply them in real-world machine learning scenarios. Our expert instructors will guide you through interactive and engaging lessons, ensuring you gain practical skills and actionable insights.Course Objectives
- Understand the importance of data preprocessing in machine learning
- Learn various data preprocessing techniques, including data cleaning, feature scaling, and feature engineering
- Apply data preprocessing techniques to real-world datasets
- Develop skills in using popular data preprocessing tools and libraries
- Gain hands-on experience with machine learning projects
Course Curriculum
Module 1. Introduction to Data Preprocessing: Overview of data preprocessing techniques
- What is data preprocessing?
- Why is data preprocessing important in machine learning?
- Overview of data preprocessing techniques
Module 2. Data Cleaning: Handling missing values, Data transformation techniques
- Handling missing values
- Data normalization and standardization
- Removing duplicates and outliers
- Data transformation techniques
Module 3. Feature Scaling and Engineering: Feature engineering techniques
- Introduction to feature scaling
- Standardization and normalization techniques
- Feature engineering techniques
- Dimensionality reduction techniques
Module 4: Data Preprocessing for Machine Learning Algorithms
- Data preprocessing for supervised learning algorithms
- Data preprocessing for unsupervised learning algorithms
- Data preprocessing for deep learning algorithms
Module 5. Hands-on Projects and Case Studies: Project 1: Data preprocessing for a regression problem
- Project 1: Data preprocessing for a regression problem
- Project 2: Data preprocessing for a classification problem
- Case study: Data preprocessing for a real-world machine learning project
Course Features
- Interactive and Engaging: Learn through interactive lessons, quizzes, and hands-on projects
- Comprehensive: Cover all aspects of data preprocessing, from basics to advanced techniques
- Personalized: Get personalized feedback and support from our expert instructors
- Up-to-date: Stay updated with the latest tools, techniques, and best practices in data preprocessing
- Practical: Apply data preprocessing techniques to real-world datasets and projects
- Real-world Applications: Learn how to apply data preprocessing techniques in real-world machine learning scenarios
- High-quality Content: Learn from high-quality video lessons, quizzes, and hands-on projects
- Expert Instructors: Learn from experienced instructors with expertise in data preprocessing and machine learning
- Certification: Receive a certificate upon completion of the course
- Flexible Learning: Learn at your own pace, anytime, anywhere
- User-friendly: Learn through a user-friendly interface, optimized for mobile and desktop devices
- Community-driven: Join a community of learners and instructors, and get support and feedback
- Actionable Insights: Gain actionable insights and practical skills, applicable in real-world scenarios
- Hands-on Projects: Work on hands-on projects, and apply data preprocessing techniques to real-world datasets
- Bite-sized Lessons: Learn through bite-sized lessons, optimized for busy schedules
- Lifetime Access: Get lifetime access to the course materials, and learn at your own pace
- Gamification: Learn through gamified lessons, and earn rewards and badges
- Progress Tracking: Track your progress, and get personalized feedback and support