This complete supervised and unsupervised machine learning course is designed for beginners who want to understand machine learning concepts, algorithms, and practical AI applications using Python. The course explains the differences between supervised and unsupervised learning while providing hands-on examples and real-world machine learning workflows.
Students will learn important machine learning algorithms including Linear Regression, Logistic Regression, Decision Trees, Random Forest, Naive Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), K-Means Clustering, Principal Component Analysis (PCA), and Regularization techniques.
The tutorial covers the basics of machine learning before moving into advanced concepts such as classification models, clustering algorithms, predictive analytics, and dimensionality reduction. Learners also explore how machine learning models are trained, evaluated, and optimized for better performance.
Additional topics include Python machine learning tools and frameworks such as NumPy, Pandas, SciPy, Flask, Keras, OpenCV, and Matplotlib. The course also introduces practical AI and data science workflows used in real-world machine learning projects.
By the end of the course, learners will have a solid understanding of supervised and unsupervised learning techniques, machine learning algorithms, and AI development using Python.
