1.2 Real-World Applications - Kington Institute

Course Content

Introduction to Machine Learning and Data Preprocessing

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1.1 Understanding Machine Learning

Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning.

1.2 Real-World Applications

1.3 Python and Java for Machine Learning

Setting up the development environment for both languages.

1.4 Data Preprocessing Techniques

Handling missing data: Techniques in Python (Pandas, NumPy) and Java (Weka).

Data encoding: Label encoding and one-hot encoding.

Feature scaling: Standardization and normalization methods.

Splitting data into training and testing sets.

Module 2: Core Machine Learning Algorithms

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Linear Regression

Logistic Regression

Unsupervised Learning Algorithms : Clustering

Principal Component Analysis (PCA)

Anomaly Detection

Machine Learning A-Z: Hands-On Python and java

About Lesson

Machine learning is revolutionizing multiple industries by enabling smarter, data-driven decisions and automating complex processes.

· Finance

In finance, machine learning enhances fraud detection, algorithmic trading, and personalized financial services. It helps in analyzing market trends and predicting risks, leading to more informed investment strategies.

· Healthcare

Machine learning in healthcare improves diagnostic accuracy, personalizes treatment plans, and streamlines administrative processes. It powers advanced medical imaging, predictive analytics for patient outcomes, and drug discovery.

· Technology

The technology sector uses machine learning for natural language processing, recommendation systems, and autonomous systems. It drives innovations in AI-powered assistants, smart devices, and cybersecurity.