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
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)
