Machine Learning.

What You Should Already Know:

Before you continue, you should have a basic understanding of the following:

• HTML
• CSS
• JavaScript

What is Machine Learning?

• Machine Learning is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed.
• It involves algorithms that allow computers to automatically learn and make predictions or decisions based on data.

What Can Machine Learning Do?

Machine Learning can perform various tasks, including:

• Classification: Assigning labels to data based on patterns and characteristics.
• Regression: Predicting continuous values based on input features.
• Clustering: Grouping data points based on similarities.
• Dimensionality Reduction: Reducing the number of input features while retaining important information.
• Reinforcement Learning: Teaching agents to make decisions by trial and error to maximize rewards.

Why Machine Learning?

• Machine Learning enables systems to learn from data and make predictions or decisions without explicit programming.
• It has applications in various domains, including healthcare, finance, marketing, and robotics.
• Machine Learning algorithms can uncover insights and patterns in large datasets that are not apparent to humans.
• It is a rapidly evolving field with numerous opportunities for innovation and problem-solving.

Machine Learning Fundamentals

• Introduction to machine learning concepts and applications.
• Types of machine learning algorithms: supervised, unsupervised, and reinforcement learning.
• Understanding the machine learning workflow: data collection, preprocessing, model training, evaluation, and deployment.
• Introduction to popular machine learning frameworks and libraries.

Data Preprocessing and Exploration

• Exploratory Data Analysis (EDA): understanding the structure and characteristics of datasets.
• Data cleaning techniques: handling missing values, outliers, and inconsistencies.
• Feature engineering: transforming and selecting relevant features for model training.
• Data scaling, normalization, and encoding for machine learning algorithms.