Introduction-to-Machine-Learning

1. Introduction to Machine Learning Machine learning is a powerful field of artificial intelligence that enables computers to learn and improve from experience without being explicitly programmed. It has transformed industries and is driving innovation across various sectors.

2. Fundamentals of Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Algorithms that learn from labeled data, making predictions on new inputs. Algorithms that find hidden patterns and insights in unlabeled data. Algorithms that learn by trial-and-error, maximizing rewards through interactive experiences.

3. Supervised Learning Algorithms 1 2 Linear Regression Logistic Regression Predicts continuous target variables based on one or more input features. Predicts binary or categorical target variables based on input features. 3 4 Decision Trees Support Vector Machines Constructs tree-like models of decisions and their possible consequences. Finds the optimal hyperplane to separate data into distinct classes.

4. Data Preprocessing and Feature Engineering 1 Data Cleaning Handling missing values, removing outliers, and ensuring data quality. 2 Feature Selection Identifying the most relevant features that contribute to the target variable. 3 Feature Transformation Scaling, normalizing, or encoding features to improve model performance.

5. Real-World Applications of Machine Learning Computer Vision Natural Language Processing Recommendation Systems Predictive Analytics Object detection, image classification, and facial recognition. Text analysis, sentiment analysis, and language translation. Personalized product recommendations and content suggestions. Forecasting, anomaly detection, and risk assessment.