Exploratory Data Analysis & Core ML Algorithms

Overview

In this course, you'll dive deep into Exploratory Data Analysis (EDA) techniques and core machine learning algorithms. You'll learn how to analyze, visualize, and preprocess data, which are essential steps for building effective machine learning models. By the end of the course, you will have a solid understanding of key algorithms like linear regression, logistic regression, Naive Bayes, and decision trees, along with the skills to implement and optimize them. The course begins with EDA, where you will explore various techniques to understand your data and prepare it for machine learning tasks. You'll learn to visualize data, detect patterns, and handle missing or outlier values, all crucial for creating high-quality datasets. Once you've mastered data exploration, you will move on to linear regression, focusing on how to use this algorithm for predictive modeling and forecasting. Next, the course introduces logistic regression, where you'll learn to classify data and optimize your models using techniques like the AUC-ROC curve. You'll apply this knowledge to real-world case studies, gaining practical insights into classification problems. You’ll also dive into the Naive Bayes classifier, learning how to implement it for specific applications such as employee attrition prediction. Finally, you'll explore decision tree classifiers, understand key concepts like entropy and the Gini index, and optimize your decision tree models using hyperparameter tuning. By the end, you’ll have the skills to implement these fundamental machine learning algorithms and apply them to solve real-world problems. This course is best suited for learners with basic Python and ML knowledge who want to advance their data modeling and analysis skills.

Syllabus

Exploratory Data Analysis

In this module, we will explore the importance of exploratory data analysis (EDA) in the data science process. You will learn various tools and processes to uncover patterns, detect anomalies, and summarize key features of your data. The module includes several hands-on projects, allowing you to apply EDA techniques to real-world datasets.

Linear Regression

In this module, we will dive deep into linear regression, a core machine learning technique. You will gain a comprehensive understanding of its underlying concepts, including cost functions and gradient descent. Through hands-on projects, you'll build and optimize models using real-world data, focusing on both theoretical foundations and practical applications.

Logistic Regression

In this module, we will introduce you to logistic regression, an essential algorithm for binary classification problems. You will explore how to prepare data, build models, and assess their performance. Additionally, you will learn how to optimize logistic regression models using techniques such as AUC-ROC and feature engineering.

Naive Bayes Classification Algorithm

In this module, we will cover the Naive Bayes classification algorithm, focusing on its probabilistic nature and applications in classification tasks. Through real-world case studies, such as employee attrition prediction, you will learn how to build and optimize Naive Bayes models effectively.

Decision Tree Classifier

In this module, we will introduce decision tree classifiers, focusing on how they work and their advantages in classification tasks. You will explore key concepts such as the Gini Index, Entropy, and pruning. By the end of this module, you will be able to apply decision trees to real-world datasets and optimize them for improved model performance.