Installation and Setting up Path
Download Python from the official website, install it, and configure the system path so you can run Python from any directory.

Features
Python is simple, readable, versatile, and supports libraries for web, data science, AI, automation, and more.

Python Variables
Variables are used to store data. In Python, you don’t need to declare the type—just assign a value (e.g. x = 10).

Input & Output and Import
Use input() to take user input, print() to show output, and import to use external libraries or modules.

Why Learn Python
Easy to learn, beginner-friendly, in-demand across industries, and widely used in web development, AI, ML, data science, and automation.

Who Uses Python
Used by developers, data scientists, AI/ML engineers, web developers, automation testers, and major companies like Google, Netflix, and Instagram.

Set up and Use PyCharm for Python Development
Install PyCharm IDE, create a new project, and write/run Python code easily with built-in tools like debugging and code suggestions.

Understand Scripting Keywords and Their Usage
Keywords are reserved words in Python (like if, for, while, def) that have special meanings and cannot be used as variable names.

Learn About Valid Identifiers in Python
Identifiers are names for variables, functions, or classes. They must start with a letter or underscore, cannot use spaces or symbols, and are case-sensitive.

Use Arithmetic, Logical, Comparison, and Assignment Operators

• Arithmetic (+, -, *, /) → Perform math operations.

• Logical (and, or, not) → Combine conditions.

• Comparison (==, !=, <, >) → Compare values.

• Assignment (=, +=, -=) → Store or update values in variables.

Apply Proper Indentation to Define Code Blocks
Python uses indentation (spaces or tabs) instead of braces {} to define code blocks like loops and functions.

Write Clean and Readable Python Scripts
Use meaningful variable names, comments, consistent formatting, and keep code simple for better readability and maintenance.

Strings & Slicing
Strings store text. Use slicing (string[start:end]) to extract parts of a string and perform operations like concatenation, repetition, and formatting.

Datetime Operations
Work with dates and times using the datetime module. You can get current date/time, calculate differences, and format dates.

Math Functions
Use the math module for operations like square root, power, rounding, and trigonometry.

Random Data
Generate random numbers or select random items using the random module, useful for simulations and testing.

Statistics Basics
Perform basic statistical operations like mean, median, mode, and standard deviation using the statistics module.

PDF Extraction
Extract text or data from PDF files using libraries like PyPDF2 or pdfplumber.

CSV Handling
Read, write, and manipulate CSV files using the csv module or pandas for structured data processing.

Introduction to Tuples
Tuples are ordered, immutable collections in Python, used to store multiple items in a single variable.

Creating and Accessing Tuples
Create tuples using parentheses ( ), e.g., my_tuple = (1, 2, 3). Access items using indexing, e.g., my_tuple[0] for the first item.

Tuple Operations
You can perform operations like concatenation (+), repetition (*), and membership checking (in).

Tuple Functions and Methods
Common tuple functions include len(), min(), max(), and sum(). Tuples have limited methods since they are immutable, but count() and index() are available.

Introduction to Sets
Sets are unordered collections of unique items in Python, used to store data without duplicates.

Creating and Using Sets
Create sets using curly braces { } or the set() function, e.g., my_set = {1, 2, 3}. Access elements by iteration since sets are unordered.

Set Operations
Perform operations like union (|), intersection (&), difference (-), and symmetric difference (^) to combine or compare sets.

Set Functions and Methods
Common set methods include:

• add(), remove(), discard(), pop() → Modify sets

• union(), intersection(), difference(), symmetric_difference() → Combine or compare sets

• clear(), copy() → Other useful operations

Types of Functions
Python supports built-in functions (like print()), user-defined functions created with def, and anonymous functions using lambda.

Function Arguments
Functions can accept positional, keyword, default, or variable-length arguments to pass data for processing.

Recursion
A function that calls itself to solve smaller instances of a problem, often used in algorithms like factorial or Fibonacci.

Anonymous Functions (lambda)
Small, single-line functions defined without a name using the lambda keyword, e.g., lambda x: x+2.

Global, Local, and Nonlocal Variables

• Local → Defined inside a function, accessible only there

• Global → Defined outside a function, accessible anywhere

• Nonlocal → Used inside nested functions to modify a variable in the outer function

Modules and Packages

• Modules → Python files containing functions, classes, or variables that can be imported

• Packages → Collections of modules organized in directories for better structure and reuse

try, except, and finally
Use try to execute code that might raise an error. If an error occurs, except handles it. The finally block runs code regardless of whether an error occurred, ensuring tasks like closing files or releasing resources are done.

try with else
The else block runs only if no exceptions occur in the try block. It’s useful for code that should execute when everything works correctly.

Custom Exceptions
You can define your own exceptions by creating a class that inherits from Exception. This allows tailored error handling specific to your program’s needs.

Error vs. Exception

• Error → A serious problem, such as syntax or system-level issues, that stops the program from running.

• Exception → An unexpected event during program execution that can be caught and handled to prevent the program from crashing.

Using the os Module
The os module provides functions to interact with the operating system, like managing files, directories, and environment variables.

Environment Variables
Store system-level settings and configuration values. Access or modify them in Python using os.environ.

Working with Paths, Directories, and Filenames
Use os.path and os functions to join paths, get file names, check if a file or directory exists, and navigate the file system.

File System Operations
Perform tasks like creating, deleting, renaming, or moving files and directories using os.mkdir(), os.remove(), os.rename(), and os.rmdir().

Handling Dates and Times
Use the datetime module to work with dates and times: get current date/time, format them, calculate differences, and perform time-based operations.

Starting a New Thread
Begin a separate flow of execution in a program to perform tasks concurrently without blocking the main program.

Creating Threads Using the threading Module
Use Python’s threading module to create threads by defining a function or subclassing Thread. Example: thread = threading.Thread(target=my_function).

Synchronizing Threads
Coordinate threads to prevent conflicts when accessing shared resources using locks, semaphores, or events.

Implementing Multithreaded Programs
Design programs where multiple threads run simultaneously to improve performance, handle tasks like I/O, or perform background processing.

Using Priority Queues with Threads
Manage tasks with different priorities using queue.PriorityQueue(), allowing high-priority tasks to execute before lower-priority ones in multithreaded programs.

Pattern Matching and Searching
Use regular expressions (regex) to find specific patterns in text, like email addresses, phone numbers, or keywords.

Real-Time Data Parsing (Network/System Logs)
Extract and analyze information from live data sources such as log files or network data using regex for monitoring or reporting.

Input Validation Using Regex
Ensure user input follows specific formats, like validating emails, passwords, or phone numbers, to prevent errors or security issues.

Regex Concepts and Syntax (match, search, findall, etc.)

• match() → Checks for a pattern at the beginning of a string

• search() → Searches for a pattern anywhere in the string

• findall() → Returns all occurrences of a pattern

• Supports special symbols, character sets, and quantifiers for flexible pattern definitions

Introduction to NumPy
NumPy is a powerful Python library for numerical computing, offering fast array operations and mathematical functions.

Creating NumPy Arrays
Create arrays using np.array(), np.zeros(), np.ones(), or np.arange() for efficient data storage and computation.

Data Types in NumPy
NumPy arrays support types like int, float, complex, and more, allowing precise control over memory and calculations.

Array Indexing and Slicing
Access or modify elements using indices or slices, similar to Python lists, for selective data operations.

Array Shape and Reshaping
Check the array’s shape with .shape and change it using .reshape() without altering data.

Iterating Through Arrays
Loop through elements using for loops or vectorized operations for faster computation.

Joining and Splitting Arrays
Combine arrays using np.concatenate() or split them with np.split() for organized data manipulation.

Searching and Sorting Arrays
Find elements using np.where() and sort arrays with np.sort() for analysis and processing.

Filtering Arrays
Use conditions to filter arrays, e.g., arr[arr > 5] to get elements greater than 5.

Introduction to Pandas
Pandas is a powerful Python library for data manipulation and analysis, providing easy-to-use structures for handling structured data.

Pandas Series
A Series is a one-dimensional labeled array that can hold any data type. You can access elements using labels or indices.

Pandas DataFrames
A DataFrame is a two-dimensional, table-like data structure with labeled rows and columns, ideal for storing and analyzing datasets.

Reading CSV Files with Pandas
Use pd.read_csv() to load CSV files into a DataFrame for easy analysis and manipulation.

Reading JSON Files with Pandas
Use pd.read_json() to load JSON data into a DataFrame, allowing structured data handling and processing.

Analyzing Data with Pandas
Use Pandas to explore and summarize data with functions like .head(), .describe(), .info(), and statistical operations such as mean, sum, and count.

Data Cleaning Techniques in Pandas
Handle missing values with .fillna() or .dropna(), remove duplicates with .drop_duplicates(), and correct data types using .astype() to prepare datasets for accurate analysis.

Introduction to Matplotlib
Matplotlib is a Python library used for creating static, interactive, and animated visualizations like charts and graphs.

Pyplot and Basic Plotting
Use matplotlib.pyplot (plt) to create simple line plots with functions like plt.plot() and display them using plt.show().

Using Markers and Line Styles
Customize plots with markers ('o', '^') and line styles ('-', '--') to make data visually distinct.

Adding Labels and Grid
Add titles, x-axis and y-axis labels using plt.title(), plt.xlabel(), plt.ylabel(), and enable grid lines with plt.grid().

Creating Subplots
Use plt.subplot() to place multiple plots in a single figure for side-by-side comparisons.

Scatter Plot
Plot individual data points with plt.scatter() to visualize relationships between two variables.

Bar Chart
Represent categorical data using plt.bar() for vertical bars or plt.barh() for horizontal bars.

Histogram
Show the distribution of numeric data using plt.hist() by dividing data into bins.

Pie Chart
Visualize proportions of a whole dataset using plt.pie() with labels and percentage display.

Introduction to Data Science
Data Science is the field of collecting, analyzing, and interpreting large volumes of data to make informed decisions, uncover patterns, and solve real-world problems using tools like Python, SQL, and machine learning.

What is Data?
Data is raw information in the form of numbers, text, images, or other formats that can be processed to extract meaningful insights.

Understanding Databases and Tables
A database is a structured collection of data, while a table is a set of rows and columns within a database that organizes and stores related information for easy access and analysis.

Bias-Variance Tradeoff
This explains the balance between two types of errors in a model:

• Bias → Happens when the model is too simple and cannot capture the true patterns in the data.

• Variance → Happens when the model is too complex and reacts too much to small fluctuations in the training data.
  The goal is to create a model that learns patterns well but also generalizes to new, unseen data.

Underfitting and Overfitting

• Underfitting → Model is too simple and fails to learn important patterns, performing poorly on both training and test data.

• Overfitting → Model is too complex, fits the training data too closely, and performs poorly on new data.

Distance Metrics Overview
Distance metrics measure how similar or different two data points are, which is useful in clustering, classification, and other machine learning tasks.

Euclidean Distance
Calculates the straight-line distance between two points in space using the Pythagorean theorem.

Manhattan Distance
Calculates distance by summing the absolute differences across each dimension, like moving along a grid of streets.

What is an Outlier?
An outlier is a data point that is significantly different from other observations in the dataset, potentially affecting analysis or model performance.

Interquartile Range (IQR)
IQR measures the spread of the middle 50% of data and is calculated as Q3 - Q1. It is used to detect outliers.

Box & Whisker Plot
A visual tool to display data distribution, showing the median, quartiles, and potential outliers.

Upper and Lower Whiskers
Lines in a box plot that extend from the quartiles to the maximum and minimum values within 1.5 × IQR, helping identify outliers.

Scatter Plot
A plot of individual data points on two axes used to visualize relationships and detect anomalies.

Cook’s Distance
A measure to identify influential data points in regression analysis that can disproportionately affect model results.

Handling Missing Values (NA)
Techniques to manage missing data in datasets to ensure accurate analysis and modeling.

Central Imputation
Replace missing values with the mean, median, or mode of the feature.

KNN Imputation
Use the values of the nearest neighbors to estimate and fill in missing data points.

Dummification (Categorical Encoding)
Convert categorical variables into numerical form using one-hot encoding so machine learning models can process them.

Descriptive Statistics
Summarizes and describes the main features of a dataset using measures like mean, median, mode, standard deviation, and range.

Grouping Data
Organize data into categories or groups to perform aggregated analysis, e.g., using groupby() in Python’s Pandas.

Handling Missing Values in Datasets
Manage missing or incomplete data using techniques like deletion, central imputation (mean/median/mode), or advanced methods like KNN imputation.

ANOVA (Analysis of Variance)
A statistical method to compare means across multiple groups and determine if there are significant differences between them.

Correlation
Measures the strength and direction of the relationship between two variables, often represented using correlation coefficients or a correlation matrix.

Hierarchical Clustering
A clustering method that builds a tree-like structure (dendrogram) to group similar data points based on distance, either by merging (agglomerative) or splitting (divisive) clusters.

K-Means Clustering
A partition-based clustering algorithm that divides data into K clusters by minimizing the distance between points and their cluster centroids.

K-Means++ Initialization
An improved version of K-Means that carefully selects initial cluster centroids to speed up convergence and improve clustering accuracy.

Passive Reinforcement Learning
The agent observes and evaluates a fixed policy without changing it, learning the value of states based on received rewards.

Active Reinforcement Learning
The agent explores and updates its policy by interacting with the environment, aiming to maximize cumulative rewards.

Policy Search
A method in reinforcement learning that directly searches for the best policy (set of actions) instead of evaluating each state individually, often used in complex or continuous environments.

Introduction to CSS
CSS (Cascading Style Sheets) is used to style and layout HTML elements, controlling colors, fonts, spacing, and overall appearance.

Types of CSS (Inline, Internal, External)

• Inline → Styles applied directly to an HTML element using the style attribute

• Internal → Styles defined within a <style> tag in the HTML <head>

• External → Styles written in a separate .css file and linked using <link>

CSS Selectors
Used to target HTML elements for styling, e.g., element selectors, class selectors (.class), ID selectors (#id), and attribute selectors.

CSS Commands/Syntax
CSS rules follow the structure: selector { property: value; }, where properties define styles for the selected elements.

Background Properties
Control background color, image, repeat, position, and size using properties like background-color and background-image.

Text Properties
Style text using properties like color, text-align, text-decoration, letter-spacing, and line-height.

Font Properties
Customize fonts with font-family, font-size, font-weight, font-style, and font-variant.

Link Styling
Style hyperlinks using pseudo-classes like :link, :visited, :hover, and :active to control appearance in different states.

List Styles
Control bullet points or numbering with list-style-type, list-style-image, and list-style-position.

Image as List Item Type
Use images instead of standard bullets in lists with list-style-image: url('image.png');.

Border Styles
Customize element borders using border-width, border-style, border-color, and shorthand border property.

Table Styling
Style tables with border-collapse, border-spacing, background-color, padding, and text-align for a neat presentation.

Margin
Adds space outside an element to separate it from other elements using margin-top, margin-right, margin-bottom, margin-left.

Padding
Adds space inside an element between its content and border using padding-top, padding-right, padding-bottom, and padding-left.

Introduction to Flask
Flask is a lightweight Python web framework used to build web applications quickly and easily with minimal setup.

What is Flask?
A micro web framework that provides essential tools for web development without extra complexity, ideal for small to medium projects.

How Flask Works
Flask handles web requests, processes them in Python functions (routes), and returns responses such as HTML pages or JSON data to the user.

Template Files in Flask
HTML files stored in the templates folder, which can include dynamic content using Flask’s template engine, Jinja2.

Rendering HTML Pages with Flask
Use the render_template() function to display HTML pages with dynamic data passed from Python code to the template.

Introduction to AI
Artificial Intelligence (AI) is the field of computer science focused on creating machines that can perform tasks requiring human intelligence, such as learning, reasoning, problem-solving, and decision-making.

Perceptron
A basic unit of a neural network that takes input features, applies weights, and outputs a binary decision based on a threshold.

Multi-Layer Perceptron (MLP)
A type of neural network with multiple layers (input, hidden, output) capable of solving complex, non-linear problems.

Markov Decision Process (MDP)
A framework for modeling decision-making where outcomes are partly random and partly under the control of an agent, used in reinforcement learning.

Logical Agent & First Order Logic

• Logical Agent → An AI system that makes decisions based on knowledge and logical reasoning.

• First Order Logic → A formal language used to represent knowledge and reason about objects, their properties, and relationships.

AI Applications
Real-world uses of AI include virtual assistants, recommendation systems, autonomous vehicles, healthcare diagnostics, fraud detection, and natural language processing.