By the end of this course, students will:Gain proficiency in Python programming, specifically for data science and machine learning applications.Learn the fundamentals of data analysis and data visualization using Python, SQL, and Excel.Develop expertise in essential libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn.Understand data preprocessing techniques and feature engineering.Implement regression, classification, clustering, and ensemble learning models.Perform exploratory data analysis (EDA) and feature selection.Master techniques for dimensionality reduction and unsupervised learning.Gain hands-on experience with natural language processing (NLP) and sentiment analysis.Learn how to integrate machine learning models into web applications using Flask.Work on real-world projects, enhancing their ability to solve business problems through data-driven decision-making.