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.