Introduction to Machine Learning

Machine learning is a subset of artificial intelligence that allows computers to learn and make predictions based on data without being explicitly programmed. It entails creating algorithms that can quickly know patterns and forecast outcomes based on data inputs. Problems in a variety of industries, including banking, healthcare, marketing, and more, may be resolved using machine learning. Image identification, natural language processing, and predictive analytics are some popular uses for machine learning. Machine learning has emerged as a critical tool for organisations to get insights and influence business results as big data becomes more widely available.

Skills you will learn

1. Data Preprocessing: Cleaning, transforming and normalizing data to prepare it for use in machine learning models.
2. Model selection: Understanding different types of machine learning algorithms, such as supervised, unsupervised, and reinforcement learning, and selecting the appropriate algorithm for a given problem.
3. Model training and evaluation: Building, training and evaluating machine learning models using techniques like cross-validation and feature selection.
4. Predictive modeling: Building predictive models that can accurately make predictions based on historical data.
5. Feature engineering: Understanding and creating new features to improve the performance of machine learning models.
6. Model interpretation: Understanding the reasoning behind the decisions made by machine learning models, and using this information to make informed decisions.

Syllabus

1. Introduction to Machine Learning:
   • Definition and overview of machine learning
   • Types of machine learning
   • Real-world applications of machine learning
2. Data Preprocessing:
   • Importing and cleaning data
   • Handling missing data
   • Normalizing and scaling data
3. Model Selection:
   • Overview of different machine learning algorithms
   • Supervised learning algorithms (e.g. linear regression, decision trees)
   • Unsupervised learning algorithms (e.g. clustering, association rule mining)
   • Reinforcement learning algorithms
4. Model Training and Evaluation:
   • Splitting data into training and testing sets
   • Evaluating model performance using metrics such as accuracy, precision and recall
   • Cross-validation techniques
   • Feature selection
5. Predictive Modeling:
   • Building predictive models using supervised learning algorithms
   • Making predictions using machine learning models
6. Feature Engineering:
   • Understanding and creating new features to improve the performance of machine learning models
7. Model Interpretation:
   • Understanding the reasoning behind the decisions made by machine learning models
   • Interpreting model performance and making informed decisions
8. Deployment:
   • Deploying machine learning models in real-world environments
   • Integrating machine learning models with other systems

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