Algorithms Fundamentals

Algorithms

Definition: A set of instructions to solve a specific problem or perform a particular task
Types:
- Recursive algorithms: solve problems by breaking them down into smaller instances of the same problem
- Dynamic programming algorithms: solve problems by breaking them down into smaller subproblems and solving each only once
- Greedy algorithms: solve problems by making the locally optimal choice at each step
Analysis:
- Time complexity: measures the computational complexity of an algorithm, usually expressed as a function of the input size (e.g., O(n), O(n^2), etc.)
- Space complexity: measures the memory used by an algorithm, usually expressed as a function of the input size

Object Oriented Programming (OOP)

Key concepts:
- Classes: blueprint for creating objects, defines properties and behavior
- Objects: instances of classes, have their own set of attributes (data) and methods (functions)
- Inheritance: a child class inherits properties and behavior from a parent class
- Polymorphism: objects of different classes can be treated as objects of a common superclass
- Encapsulation: hiding internal implementation details from the outside world
Principles:
- Abstraction: showing only essential features, hiding non-essential details
- Composition: objects are composed of other objects or collections of objects
- Decoupling: reducing dependencies between objects

Data Structures

Arrays:
- Definition: a collection of elements of the same data type stored in contiguous memory locations
- Operations: indexing, slicing, concatenation, iteration
Linked Lists:
- Definition: a dynamic collection of elements, each pointing to the next element
- Operations: insertion, deletion, traversal
Stacks and Queues:
- Stack: a Last-In-First-Out (LIFO) data structure, with operations push, pop, and peek
- Queue: a First-In-First-Out (FIFO) data structure, with operations enqueue, dequeue, and peek
Trees and Graphs:
- Trees: a hierarchical data structure, with nodes and edges
- Graphs: a non-hierarchical data structure, with nodes and edges

Web Development

Front-end:
- HTML: structure and content of web pages
- CSS: styling and layout of web pages
- JavaScript: dynamic behavior and interactivity of web pages
Back-end:
- Server-side programming: server-side languages (e.g., Node.js, Ruby, Python) handle requests and generate responses
- Databases: storing and retrieving data (e.g., relational databases, NoSQL databases)
Full-stack development: combining front-end and back-end development

Machine Learning

Types:
- Supervised learning: training on labeled data to make predictions
- Unsupervised learning: training on unlabeled data to discover patterns
- Reinforcement learning: training on feedback to make decisions
Algorithms:
- Linear regression: predicting continuous values
- Decision trees: predicting categorical values
- Neural networks: predicting complex relationships
Evaluation metrics:
- Accuracy: proportion of correct predictions
- Precision: proportion of true positives among predicted positives
- Recall: proportion of true positives among actual positives

Algorithms

Definition: A set of instructions to solve a specific problem or perform a particular task.
Types:
- Recursive algorithms: break down problems into smaller instances of the same problem, solving each recursively until a base case is reached.
- Dynamic programming algorithms: break down problems into smaller subproblems, solving each only once and storing the solutions to subproblems to avoid redundant computation.
- Greedy algorithms: make the locally optimal choice at each step, hoping to find a global optimum.
Analysis:
- Time complexity: measures the computational complexity of an algorithm, usually expressed as a function of the input size (e.g., O(n), O(n^2), etc.), helping to estimate the running time.
- Space complexity: measures the memory used by an algorithm, usually expressed as a function of the input size, helping to estimate the memory requirements.

Object-Oriented Programming (OOP)

Key concepts:
- Classes: blueprints for creating objects, defining properties and behavior.
- Objects: instances of classes, having their own set of attributes (data) and methods (functions).
- Inheritance: a child class inherits properties and behavior from a parent class, allowing for code reuse and a more hierarchical organization.
- Polymorphism: objects of different classes can be treated as objects of a common superclass, allowing for more flexibility and generic code.
- Encapsulation: hiding internal implementation details from the outside world, improving code modularity and reducing dependencies.
Principles:
- Abstraction: showing only essential features, hiding non-essential details to reduce complexity.
- Composition: objects are composed of other objects or collections of objects, allowing for more modular and flexible code.
- Decoupling: reducing dependencies between objects, improving code modularity and maintainability.

Data Structures

Arrays:
- Definition: a collection of elements of the same data type stored in contiguous memory locations.
- Operations: indexing, slicing, concatenation, and iteration, allowing for efficient data manipulation and access.
Linked Lists:
- Definition: a dynamic collection of elements, each pointing to the next element.
- Operations: insertion, deletion, and traversal, allowing for efficient data manipulation and access.
Stacks and Queues:
- Stack: a Last-In-First-Out (LIFO) data structure, with operations push, pop, and peek, allowing for efficient data manipulation and access.
- Queue: a First-In-First-Out (FIFO) data structure, with operations enqueue, dequeue, and peek, allowing for efficient data manipulation and access.
Trees and Graphs:
- Trees: a hierarchical data structure, with nodes and edges, allowing for efficient data organization and access.
- Graphs: a non-hierarchical data structure, with nodes and edges, allowing for efficient data organization and access.

Web Development

Front-end:
- HTML: structure and content of web pages, providing a standard way to define web page content.
- CSS: styling and layout of web pages, providing a standard way to define web page appearance.
- JavaScript: dynamic behavior and interactivity of web pages, allowing for client-side scripting and dynamic content.
Back-end:
- Server-side programming: server-side languages (e.g., Node.js, Ruby, Python) handle requests and generate responses, allowing for dynamic content generation and data storage.
- Databases: storing and retrieving data (e.g., relational databases, NoSQL databases), allowing for efficient data storage and retrieval.
Full-stack development: combining front-end and back-end development, allowing for complete web application development.

Machine Learning

Types:
- Supervised learning: training on labeled data to make predictions, where the model learns from labeled examples.
- Unsupervised learning: training on unlabeled data to discover patterns, where the model learns from unlabeled examples.
- Reinforcement learning: training on feedback to make decisions, where the model learns from rewards or penalties.
Algorithms:
- Linear regression: predicting continuous values, using a linear equation to model the relationship between inputs and outputs.
- Decision trees: predicting categorical values, using a tree-like model to classify data.
- Neural networks: predicting complex relationships, using a layered, interconnected model to learn from data.
Evaluation metrics:
- Accuracy: proportion of correct predictions, measuring the model's overall performance.
- Precision: proportion of true positives among predicted positives, measuring the model's accuracy for positive predictions.
- Recall: proportion of true positives among actual positives, measuring the model's accuracy for positive instances.