This comprehensive course is designed for individuals interested in gaining a deep understanding of Machine Learning (ML) and Artificial Intelligence (AI). You will start with the basics of machine learning, exploring key concepts such as supervised and unsupervised learning, regression models, and clustering algorithms. Throughout the course, we’ll cover various techniques for analyzing and predicting data, including regression models like linear regression, logistic regression, and multiple linear regression.

You will also dive into clustering models such as K-means, hierarchical clustering, and DBSCAN for outlier detection, giving you the necessary tools to handle data grouping and segmentation challenges. Additionally, you'll explore dimensionality reduction methods like Principal Component Analysis (PCA) and factor analysis, which are essential for reducing the complexity of high-dimensional data and improving the performance of machine learning models.

A significant focus is placed on Generative AI, where you'll learn how to implement advanced AI models that can generate new data based on existing patterns, an exciting area that includes deep learning and neural networks. Along with Natural Language Processing (NLP), you will discover techniques for processing and analyzing textual data, including data cleaning, lemmatization, and text analysis.

By the end of this course, you will have the skills to apply machine learning algorithms to real-world datasets, understand AI’s potential applications, and build predictive models using the latest AI techniques. Whether you’re a beginner or looking to expand your knowledge, this course provides practical and in-depth content to help you become proficient in AI and machine learning.