Description

Course Overview:
This course dives deeper into machine learning, covering essential algorithms and techniques. Designed for those with a basic understanding of programming and AI, you'll learn how to implement machine learning algorithms, handle datasets, and perform model evaluation.

Course Curriculum:

• Introduction to Machine Learning: Types of machine learning—supervised, unsupervised, and reinforcement learning.
• Data Preprocessing: Data cleaning, feature selection, feature engineering, and handling missing data.
• Core ML Algorithms: Linear regression, logistic regression, decision trees, support vector machines (SVM), k-nearest neighbors (KNN).
• Model Evaluation: Cross-validation, accuracy, precision, recall, F1 score, confusion matrix.
• Advanced ML Techniques: Ensemble methods (bagging, boosting), Random Forest, and XGBoost.
• Hands-on Projects: Building predictive models using Python libraries like Scikit-learn and TensorFlow.

Skills Gained:
Building and evaluating machine learning models, understanding key algorithms, working with real datasets.