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Python Programming Course for Advanced Learners

Elevate your Python skills to a professional level with advanced topics, including machine learning, data processing, and complex project structures. This course empowers you to tackle real-world projects, harnessing Python's full potential for data analysis, automation, and more.

5.0 Rating Advanced Course 12 Weeks

About this Course

This advanced Python course takes you beyond fundamental programming and into the realm of data science, web scraping, and machine learning. With an emphasis on real-world applications, you’ll work with libraries like Pandas and Scikit-Learn, automate data collection, and implement predictive models. This course builds proficiency in handling large data sets, developing data pipelines, and deploying machine learning solutions, preparing you for complex and professional Python programming.

Course Objective

To equip you with advanced Python programming capabilities, enabling you to work with machine learning models, process large datasets, and implement data pipelines in Python for complex, real-world scenarios.

Skills you'll gain

Advanced Data Processing Machine Learning Foundations Automate Data Collection with APIs

Syllabus

  • Setting Up Python Environment
  • Python Basics: Syntax and Data Types
  • Variables and Basic Operations
  • Input/Output Functions
  • Introduction to Control Flow (if, elif, else)
Practice Task: Write a program to check if a number is positive, negative, or zero.

  • For and While Loops
  • Lists: Creation, Accessing Elements, and Methods
  • Introduction to Tuples and Sets
  • Basic String Manipulation
  • Dictionaries: Keys, Values, and Methods
Practice Task: Create a program that takes a list of numbers and outputs the maximum and minimum numbers.

  • Defining and Calling Functions
  • Parameters, Return Values, and Scope
  • Lambda Functions
  • Error Handling with Try-Except
  • Basic Debugging Techniques
Practice Task: Write a function to calculate the factorial of a given number.

Final Project: Complete a project applying skills from Weeks 1-3.
  • Project Work (Days 1-3)
  • Project Refinement (Day 4)
  • Project Presentation and Wrap-Up (Day 5)

  • Understanding Modules and Packages
  • Importing Built-in and Custom Modules
  • Exploring Python Standard Library
  • Creating and Organizing Packages
  • Using `pip` to Install External Libraries
Practice Task: Build a simple package with multiple modules to perform mathematical operations.

  • Reading and Writing Files
  • Working with Different File Modes
  • Exception Handling: `try`, `except`, `finally`
  • Raising and Customizing Exceptions
Practice Task: Create a program that reads data from a file, handles any potential errors, and writes processed data to a new file.

  • Advanced List Comprehensions
  • Dictionary and Set Operations
  • Introduction to Lambda Functions
  • Map, Filter, and Reduce Functions
Practice Task: Write a program to process a list of dictionaries and filter, sort, and transform the data.

Final Project: Complete a project applying skills from Weeks 1-3.
  • Project Work (Days 1-3)
  • Project Refinement (Day 4)
  • Project Presentation and Wrap-Up (Day 5)

  • Introduction to Pandas DataFrames and Series
  • Data Cleaning and Preprocessing
  • Data Aggregation and Grouping
  • Data Merging and Concatenation
  • Exploratory Data Analysis (EDA) with Pandas
Practice Task: Perform data cleaning, aggregation, and EDA on a sample dataset.

  • Introduction to Machine Learning Concepts
  • Supervised vs. Unsupervised Learning
  • Linear Regression and Classification Basics
  • Model Evaluation and Validation
  • Implementing Simple Models with Scikit-Learn
Practice Task: Build a basic linear regression model using Scikit-Learn and interpret results.

  • Introduction to APIs and REST Principles
  • Working with APIs using Requests
  • Data Extraction from APIs and JSON Parsing
  • Web Scraping Basics with BeautifulSoup
  • Handling Dynamic Content with Selenium
Practice Task: Use an API to extract data and perform a simple analysis; scrape data from a website and save it for later analysis.

Final Project: Complete a project applying skills from Weeks 9-11.
  • Project Work (Days 1-3)
  • Project Refinement (Day 4)
  • Project Presentation and Wrap-Up (Day 5)

About the Instructor

Varsha Sekar

Director & Instructor

4.95 Instructor rating

100 Students

Varsha Sekar serves as the Director and Lead Instructor at Savvy Axiss and is a Professor at Jeppiaar Engineering College. With a robust background in Machine Learning and Deep Learning, she has contributed extensively to the field through multiple research publications. An enthusiastic advocate for AI, Varsha is dedicated to fostering knowledge and inspiring the next generation of tech innovators and data scientists.

Fee ₹ 3999

₹ 2999

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Duration

12 Weeks

Class Time

1.5 Hours

Enrolled

250 students

Language

English / Tamil

Skill Level

Advanced

Schedule

Monday to Friday

Certificate

Yes

SoftSkill Trainning

Free

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