Data Preprocessing For Machine Learning And Data Analysis

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

This course includes 29 downloadable files, including one PDF file containing the entire course summary (91 pages) and 28 Python code files attached to their corresponding lectures.If we understand a concept well theoretically, only then can we apply it effectively for our purposes. Therefore, this course is structured in a classic "classroom-style" approach. First, we dedicate sufficient time to explaining the theoretical foundations of each topic, including why we use a particular technique, where it is applicable, and its advantages.After establishing a solid theoretical understanding, we move on to the coding session, where we explain the example code line by line. This course includes numerous Python-based coding examples, and for some topics, we provide multiple examples to reinforce understanding. These examples are adaptable, meaning you can modify them slightly to fit your specific projects.Data preprocessing is a crucial step in AI and machine learning, directly affecting model performance, accuracy, and efficiency. Since raw data is often messy and unstructured, preprocessing ensures clean, optimized datasets for better predictions.This hands-on course covers essential techniques, including handling missing values, scaling, encoding categorical data, feature engineering, and dimensionality reduction (PCA). We will also explore data visualization with geographic information, weighted scatter plots, and shapefiles, particularly useful for geospatial AI applications.Beyond traditional structured datasets, this course includes image and geographic datasets, giving learners a broader perspective on real-world AI projects.By the end, you’ll be able to build automated data preprocessing pipelines and prepare datasets efficiently for machine learning and deep learning applications.Ideal for ML engineers, data scientists, AI developers, and researchers, this course equips you with practical skills and best practices for high-quality, well-processed datasets that enhance model performance. You can download the entire course summary PDF from the final lecture (Lecture 28)

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

Lecture 5 Handling Poor-Quality Data, Part-1: Identifying Outliers with Extreme Values

Lecture 6 Handling Poor-Quality Data, Part-2: Identifying Outliers with Visual Identificat

Lecture 7 Handling Poor-Quality Data, Part-3: Identifying Outliers with Z-Score

Lecture 8 Handling Poor-Quality Data, Part-4, Identifying Outliers with IQR

Section 2: Feature Selection and Engineering

Lecture 9 Data Cleaning

Lecture 10 Eliminating Irrelevant Features and Feature Engineering

Lecture 11 Feature Scaling,Part-1: Why Feature Scaling is Important & Useful Pandas Methods

Lecture 12 Feature Scaling, Part-2: Scaled Datasets and Bunch Object Data Structure

Lecture 13 Feature Scaling, Part-3: Scaling with Log Transformation

Lecture 14 Feature Scaling, Part-4: Min-Max Scaling

Lecture 15 Feature Scaling, Part-5: Standardization

Lecture 16 Feature Enhancement

Lecture 17 Handling Text and Categorical Features, Part-1: One-Hot Encoding

Lecture 18 Handling Text and Categorical Features, Part-2: LabelEncoder

Lecture 19 Handling Text and Categorical Features, Part-3: LabelBinarizer

Lecture 20 Creating Feature Combinations

Section 3: Data Exploration and Dimensionality Reduction

Lecture 21 Identifying Correlations

Lecture 22 Visualizing the Data to Gain Insights

Lecture 23 Principal Component Analysis (PCA), Part-1: What is PCA Mathematically?

Lecture 24 Principal Component Analysis (PCA), Part-2: Python Coding Example-1

Lecture 25 Principal Component Analysis (PCA), Part-3: Python Coding Example-2

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.