Data Preprocessing For Machine Learning And Data Analysis

Data Preprocessing For Machine Learning And Data Analysis
Published 3/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

A Comprehensive Guide for AI & Machine Learning Developers and Data Scientists

What you’ll learn

Understand the importance of high-quality data in AI & machine learning.

Apply data cleaning techniques to handle missing and poor-quality data.

Perform feature selection, scaling, and transformation for better model performance.

Work with categorical, numerical, text-based, and image features effectively.

Identify correlations and use visualization techniques to gain insights.

Implement Principal Component Analysis (PCA) for dimensionality reduction.

Properly split datasets for training, testing, and cross-validation.

Build automated data preprocessing pipelines using custom transformers.

Visualize data using weighted scatter plots and shapefiles.

Understand and process image and geographic datasets for AI & machine learning applications.

Gain experience with traditional structured datasets, image datasets, and geographic datasets, providing a broader perspective on data used in AI & ML projects.

Enhance your resume with in-demand data science skills, including statistical analysis, Python with NumPy, pandas, Matplotlib and advanced statistical analysis.

Learn and apply useful data preprocessing techniques using Scikit-learn, pandas, NumPy, and Matplotlib.

Requirements

There are no special requirements for this course. If you have beginner to intermediate-level Python experience, that is enough to follow along and understand the concepts. This course follows a classic classroom-style approach, where we first cover the theoretical foundations before moving on to hands-on coding sessions. This structured format makes the course easy to understand for learners at all levels.

Description

Overview

Section 1: Course Overview, Introduction and Handling Poor-Quality Data

Lecture 1 Course Overview

Lecture 2 Introduction to Data Preprocessing for Machine Learning and Data Analysis

Lecture 3 Ensuring Sufficient Quantity of Training Data

Lecture 4 Addressing Non-representative Training Data

Section 2: Feature Selection and Engineering

Lecture 9 Data Cleaning

Lecture 10 Eliminating Irrelevant Features and Feature Engineering

Lecture 16 Feature Enhancement

Lecture 20 Creating Feature Combinations

Section 3: Data Exploration and Dimensionality Reduction

Lecture 21 Identifying Correlations

Lecture 22 Visualizing the Data to Gain Insights

Section 4: Model Readiness and Automation

Lecture 26 Training and Test Data Splitting

Lecture 27 Building Data Pipelines

Lecture 28 Creating Custom Transformers

Aspiring AI & Machine Learning Developers who want to master data preprocessing.,Data Scientists & Analysts looking to improve model accuracy and efficiency.,AI & ML Engineers working with real-world datasets, including geographic and image data.,Students & Researchers interested in learning advanced data preparation techniques.