Python & ML Learning Path : 0-5 Years Experience (Beginners)

This comprehensive beginner’s course establishes a solid foundation in Python programming and introduces essential machine learning concepts. Designed for complete beginners with 0-5 years of experience, this program will equip you with the practical skills needed to start your journey in data science and machine learning.

Training Mode & Duration

Ready to start your journey in Python and Machine Learning? Enroll now and take the first step toward becoming a data professional!

Learning Resources

Comprehensive course materials

Coding exercises with solutions

Guided projects with step-by-step instructions

Reference guides and cheat sheets

Online forum access for doubt resolution

Course Delivery

On-demand video lectures

Interactive coding environments

Weekly live doubt-clearing sessions

Hands-on assignments and quizzes

Project-based assessments

Course Delivery

Expert-led video tutorials

Live coding sessions

Architecture review workshops

Group code review exercises

1-on-1 mentoring for project development

Module 1: Python Programming Basics (8 hours)

• Introduction to Python
• Setting up Python environment
• Understanding Python syntax
• Variables and data types
• Basic operations and expressions
• Control Structures
• Conditional statements (if, elif, else)
• Loops (for, while)
• Loop control (break, continue)
• Functions and Modules
• Creating and using functions
• Parameters and return values
• Lambda functions
• Importing and creating modules
• Data Structures
• Lists, tuples, and dictionaries
• Sets and their operations
• List comprehensions
• File Handling
• Reading and writing text files
• Working with CSV files
• Exception handling

Module 2: Libraries for Data Science (10 hours)

• NumPy Fundamentals
• Arrays and array operations
• Broadcasting
• Mathematical operations
• Random number generation
• Data Manipulation with Pandas
• Series and DataFrames
• Data importing and cleaning
• Data filtering and transformation
• Grouping and aggregation
• Data Visualization
• Plotting basics with Matplotlib
• Creating advanced visualizations with Seaborn
• Customizing plots
• Interactive visualization introduction

Module 3: Fundamentals of Machine Learning (9 hours)

• Introduction to Machine Learning
• What is ML and its applications
• Types of ML: supervised, unsupervised, reinforcement
• ML workflow and methodology
• Supervised Learning
• Linear regression implementation
• Classification with k-Nearest Neighbors
• Model evaluation techniques
• Cross-validation
• Unsupervised Learning
• Clustering methods
• Dimensionality reduction basics

Module 4: Practical Implementation (8 hours)

• End-to-End Projects
• Exploratory data analysis project
• Building a predictive model
• House price prediction system
• Customer segmentation analysis
• Best Practices
• Code organization and documentation
• Debugging techniques
• Performance optimization basics

Basic computer skills

No prior programming experience required

Fundamental understanding of high school mathematics