Machine Learning & Deep Learning with Python | Hands-On AI

Build real-world Machine Learning & Deep Learning models with Python—through hands-on projects, practical datasets, and clear step-by-step guidance.This intensive course is designed to help you become a confident Machine Learning practitioner by solving real-world problems relevant to industry and research.Whether you are a student, engineer, or professional, you will gain the skills to apply ML techniques in your projects and develop job-ready expertise in AI and data science.Created by an experienced professor and refined through classroom teaching and real project implementation, this course is practical, structured, and up-to-date.This exemplary, engaging, enlightening and enjoyable course is organized as seven interesting modules, with abundant worked examples in the form of code executed on Jupyter Notebook. By the end of this course, you will be able to build, evaluate, and apply Machine Learning models to real-world problems with confidence.It is important that data is visualized before attempting to carryout machine learning and hence we start the course with a module on data visualization. This is followed by a full blown and enjoyable exposure to Regression covering simple linear regression, polynomial regression, multiple linear regression.  Regression is followed by extensive discussions on another important supervised learning algorithms on Classification. We carry out modeling using classification strategies such as logistic regression, Naive Bayes classifier, support vector machine, K nearest neighbor, Decision trees, ensemble learning, classification and regression trees, random forest and boosting - ada boost, gradient boosting. From supervised learning we move on to discuss about unsupervised learning - clustering for unlabelled data. We study the hierarchical, k means, k medoids and Agglomerative Clustering. It is not enough to know the algorithms, but also strategies such as bias variance