PYTHON

PYTHON

Python Programming Essentials
  • Module 1 Language Overview
    • Why Python is popular on many areas?
    • Big Data and Database Management
    • Data Science and Analysis
    • Machine Learning (ML)
    • Artificial Intelligence (AI)
    • Network Programming and System Automation
    • Philosophy and syntax of Python
    • Multi-paradigm Language
    • Procedural Programming
    • Object Oriented Programming
    • Functional Programming
  • Module 2 Standard Data Types
    • The Python Standard Library
    • Built-in Functions and Modules
    • Basic Operators and Type Casting
    • Numeric Data Types and Functions
    • String Data Type and Functions
  • Module 3 Flow Control
    • if-else
    • For loop
    • While loop
    • break and continue statements
  • Module 4 Functions
    • Function Definition
    • Scope Rules
    • Recursion
    • Random Module Functions
  • Module 5 Lists and Tuples
    • Immutable vs Mutable Types
    • List and Tuple Functions
    • Comparison
    • Conversion
    • Multi-dimensional Lists and Tuples
  • Module 6 Dictionaries
    • Key and Value Pairs
    • Dictionary Funtions
    • Sorting and Converting
  • Module 7 External Libraries
    • Important Libraries
    • How to Install and Import
    • Examples
  • Module 8 Basic File Operations
    • Open a File with r/w/a/b Modes
    • File Operations
    • File and Directory Methods
  • Module 9 Exception Handling
    • Exception Types
    • Multiple Exceptions
    • try and except block
    • Finally expression
  • Module 10 Data Formats
    • CVS
    • JSON
    • YAML
    • XML
    • Labs with JSON Files
  • Module 11 Dates and Times
    • Understanding Time
    • The time Module
    • The datetime Module
    • Working with Timezones
    • Arithmetic with Time and Dates
Python Advanced Programming
  • Module 1 Language Overview
    • Quick Review of Python Essentials
    • Flow Control, Functions, Lists, Tuples, Sets, Dictionaries, Exceptions
  • Module 2 Object Oriented Programming
    • Encapsulation
    • Information Hiding
    • Inheritance
    • Polymorphism
    • Overloading
    • Overriding
    • Constructors
    • Multiple Inheritance
  • Module 3 Advanced Functions
    • Packing and Unpacking
    • Zip Function
    • Function Parameters: *args, **kwargs
    • Iterator
    • Generator
    • Decorator
    • Magic Methods
  • Module 4 Regular Expressions
    • Regex Module
    • Search vs. Match
    • Find and Replace
    • Option Flags
    • Special Char Classes
  • Module 5 Dates and Times
    • Understanding Time
    • The time Module
    • The datetime Module
    • Working with Timezones
    • Arithmetic with Time and Dates
  • Module 6 OS Communication with other OS
    • How to send commands to OS from Python
    • os Module
    • sys Module
    • shutil Module
    • subprocess Module
  • Module 7 Networking Basics
    • How to access remote devices from Python
    • Paramiko Library
    • Netmiko Library
    • LAB: Access and Run Command on Linux using Paramiko
  • Module 8 Packaging Details
    • Virtual Environments
    • Creating your own packages and modules
    • Creating EXE files
    • CLI Debugging
    • PVM: CPython vs Cython vs Jython
  • Module 9 Data Formats
    • How to Read and Write Different Data Formats
    • File Read/Write: Text and Binary
    • CSV
    • XML
    • EXCEL
    • JSON
    • YAML
    • JSON LABS
  • Module 10 HTTP and API Access
    • HTTP Basics
    • GET, POST, PUT, PATCH, HEAD, DELETE
    • HTTP Status Codes
    • requests Module
    • How to access an API
    • API LABS
  • Module 11 Database Access
    • SQL vs. NoSQL Databases
    • SQLite3 Module
    • SQL Basics
    • CRUD Operations on SQLite3 Database
    • - CREATE
    • - SELECT
    • - INSERT
    • - UPDATE
    • - DELETE
    • SQLite3 Movie Database Project
  • Module 12 Introduction to Data Analysis
    • This module just give basic info Data Science Topics
    • Understanding the Nature of the Data
    • The Data Analysis Process
    • Problem Definition
    • Data Extraction
    • Python and Data Analysis
    • The NumPy Library
    • The Pandas Standard Library
    • Data Visualization with matplotlib
Data Science with Python
  • Module 1 Python Background
    • Quick Review of Python Essentials
    • Functional Programming Functions;
    • List Comprehensions
    • Lambda Functions
    • map, filter, reduce
    • Iterator
    • Generator
    • Practice: Fibonacci Series with Generator
    • LAB: Random Walk with Matplotlib Visualization
  • Module 2 Math Background
    • Probability Essentials
    • Monty Hall and other Problems
    • Statistics Basics
    • Normal Distribution
    • Mean, Variance, Standard Deviation
    • Bayes Theorem
    • LAB: COVID-19 Test Reliability
  • Module 3 Regular Expressions
    • Regex Module
    • Search vs. Match
    • Find and Replace
    • Option Flags
    • Special Char Classes
    • LAB: Email Extraction with REGEX
  • Module 4 Database Access
    • SQL vs. NoSQL Databases
    • SQLite3 Module
    • SQL Basics
    • CRUD Operations on SQLite3 Database
    • LAB: SQLite3 Movie Database Project
  • Module 5 Data Analysis Basics
    • Scipy Ecosystem
    • Numpy
    • Pandas
    • Sympy
    • Matplotlib
    • Seaborn
  • Module 6 NUMPY> Ndarray: A multidimensional array
    • dtype and shape to define type and geometry
    • Element-wise computation
    • Creating Arrays
    • Using Arrays and Scalars
    • Indexing Arrays
    • Array Transpositionv
    • Universal Array Function
    • Array Processing
    • Array input and output
    • LAB: Matrice Operations
  • Module 7 PANDAS
    • Series
    • DataFrame
    • Index objects
    • Drop Entry
    • Selecting Entries
    • Data Alignment
    • Rank and Sort
    • Summary Statistics
    • Missing Data
    • Reading and Writing Text Files
    • JSON with Python
    • HTML with Python
    • Merge
    • Merge on Index
    • Concatenate
    • Combining DataFrames
    • Reshaping
    • Mapping
    • Replace
    • Permutation
    • GroupBy on DataFrames
    • GroupBy on Dict and Series
    • Aggregation
    • Splitting and Combining
    • Pandas LABS
  • Module 8 MATPLOTLIB
    • Data Visualization with Matplotlib
    • Matplotlib is written in Python and makes use of NumPy
    • Matplotlib has a procedural interface named the Pylab
    • Pylab is designed to resemble MATLAB
    • Pyplot API
    • Simple Plot
    • PyLab module
    • Figure Class
    • Axes Class
    • Multiplots
    • Subplots Function
    • Grids
    • Formatting Axes
    • Bar Plot
    • Histogram
    • Pie Chart
    • Scatter Plot
    • Contour Plot
    • Three-dimensional Plotting
    • Matplotlib LABS
  • Module 9 SEABORN
    • Seaborn Introduction
    • Line Plot
    • Scatter Plot
    • Bar Plot
    • Hisplot
    • Kdeplot
    • Displot
    • Jointplot
    • Pairplot
    • Rugplot
    • Countplot
    • Boxplot
    • Violinplot
    • Stripplot
    • Heatmap
    • Matrix Plots
    • Grids
    • Regression Plots
    • Styles, Colors, and Palettes
    • Seaborn LABS
Machine Learning with Python
  • Module 1 Introduction to Machine Learning
  • Module 2 Classification vs. Regression
  • Module 3 Linear Regression
  • Module 4 Bias-Variance Trade-off
  • Module 5 Dimensionality Reduction
  • Module 6 Cross Validation
  • Module 7 Training Models
  • Module 8 Logistic Regression
  • Module 9 K-Nearest Neighbors
  • Module 10 Support Vector Machines
  • Module 11 Decision Trees
  • Module 12 Random Forest
  • Module 13 Unsupervised Learning
  • Module 14 K-means Clustering
Artificial Intelligence with Python
  • Module 1 Deep Learning Concepts
  • Module 2 History Behind Neural Networks
  • Module 3 Perceptron and Working Mechanism
  • Module 4 Architecture of Artificial Neural Networks
  • Module 5 Types of Activation Functions
  • Module 6 Softmax Function, Loss Function
  • Module 7 Back Propagation and Gradient descent
  • Module 8 Tensorflow 2.0 vs. Keras Framework
  • Module 9 Feedforward Neural Networks (FNN)
  • Module 10 Convolutional Neural Networks (CNN)
  • Module 11 Recurrent Neural Networks (RNN)
  • Module 12 Natural Language Processing (NLP)