Python Bootcamp 2025

Master Python and unlock power of data with NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, and PyTorch

What you'll learn

Gain a thorough understanding of Python syntax, script writing, and core concepts such as variables, data types, and string operations

Master the use of conditional statements and loops in Python to automate and optimize data processing tasks

Learn to design reusable Python functions to perform repetitive tasks efficiently, including recursion and lambda functions

Understand how to use NumPy arrays for complex mathematical computations and effectively handle large datasets with high performance

Master the use of Pandas for data manipulation and analysis; learn how to explore, clean, and transform data into a suitable format

Develop the ability to create insightful visual representations of data using Matplotlib and Seaborn libraries of Python

Gain hands-on experience with Scikit-Learn, applying supervised and unsupervised learning algorithms to solve real-world machine learning problems

Understand the fundamentals of Deep Learning and neural networks, forming the foundation to work with TensorFlow and PyTorch frameworks

Build and evaluate deep learning models in PyTorch, including projects such as Fashion MNIST classification and cancer prediction

Requirements

No prior experience in Python or data analysis is required; just basic computer skills and access to a computer with an internet connection are necessary to start this course.

Description

Are you looking to build a career in data science or elevate your data analysis skills? Do you often wonder how professionals transform raw data into meaningful insights that drive decisions? If your goal is to confidently step into the world of Python programming, machine learning, and deep learning, then this course is your complete guide.Python Bootcamp is a comprehensive bootcamp designed to take you from the fundamentals of Python all the way to advanced data science applications. Whether you are a beginner or someone with prior programming experience, this course will equip you with the knowledge and practical skills required to thrive in the data-driven world.By enrolling in this course, you will:Build a strong foundation in Python programming — from basic syntax, data types, and loops to advanced functions and file handling.Master essential data science libraries including NumPy for numerical computing, Pandas for data manipulation, and Matplotlib and Seaborn for powerful data visualizations.Gain expertise in machine learning with Scikit-Learn, exploring supervised and unsupervised learning techniques, model selection, and evaluation.Dive into deep learning fundamentals, learning how neural networks work and how to implement them using TensorFlow and PyTorch.Work on real-world projects, including classification tasks with datasets like Fashion MNIST and Melanoma Cancer Prediction, applying everything you learn in practical scenarios.Develop end-to-end data analysis workflows — from data cleaning and transformation to visualization and predictive modeling.Why this course is essential for you:In today’s data-driven landscape, the ability to analyze, visualize, and model data is one of the most in-demand skills across industries. Python stands out as the most popular and versatile language in data science, powering everything from academic research to business intelligence and AI innovation.This bootcamp doesn’t just teach you concepts; it empowers you to apply them immediately. Through hands-on coding exercises, projects, and guided assignments, you will not only understand the “how” but also the “why” behind each step.What makes this course unique?A step-by-step journey from beginner-friendly Python programming to advanced machine learning and deep learning.A practical, project-driven approach — learn by doing, not just by theory.Coverage of the entire data science ecosystem — from NumPy, Pandas, and visualization tools to Scikit-Learn, TensorFlow, and PyTorch.Real-world datasets and case studies to prepare you for professional data challenges.Don’t let data feel overwhelming anymore. Take charge and transform it into actionable insights.Enroll in Python Bootcamp today and begin your journey toward becoming a confident, skilled, and job-ready data professional.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course resources

Section 2: Getting Started with Python

Lecture 3 What is Python & Why Learn It?

Lecture 4 This is a Milestone!

Lecture 5 Understanding Variables in Python

Lecture 6 Python Data Types

Lecture 7 Working with Strings in Python

Lecture 8 Useful String Methods

Section 3: Data Structures in Python

Lecture 9 Lists in Python

Lecture 10 Understanding Tuples

Lecture 11 Working with Dictionaries

Lecture 12 Sets in Python

Section 4: Conditional Statements in Python

Lecture 13 Introduction to Conditional Statements

Lecture 14 Operators and Advanced Conditions

Section 5: Loops in Python

Lecture 15 For Loops in Python

Lecture 16 While Loops in Python

Section 6: Functions in Python

Lecture 17 Defining and Using Functions

Lecture 18 Understanding Recursion

Lecture 19 Lambda Functions in Python

Section 7: File Handling in Python

Lecture 20 Reading and Writing Files in Python

Section 8: Machine Learning Basic

Lecture 21 Introduction to Machine Learning

Section 9: Numpy Library

Lecture 22 Overview of NumPy and Its Core Concepts

Lecture 23 Indexing and Selecting Data in NumPy Arrays

Lecture 24 Understanding Array Data Types, Shapes, and Stacking

Lecture 25 Techniques for Creating Arrays in NumPy

Lecture 26 Performing Mathematical and Statistical Operations with Arrays

Section 10: Pandas Library

Lecture 27 Introduction to Pandas DataFrames

Lecture 28 Working with Series and DataFrames

Lecture 29 Core Methods for Data Analysis in Pandas

Lecture 30 Handling Missing and Null Data

Lecture 31 DataFrame Transformation and Manipulation

Section 11: Matplotlib Library

Lecture 32 Getting Started with Matplotlib Library

Lecture 33 Plotting Fundamentals: Creating and Customizing Visuals

Lecture 34 Subplots and Scatter Plots: Comparative and Relational Analysis

Lecture 35 Bar Charts, Histograms, and Pie Charts: Distribution and Composition Insights

Section 12: Seaborn Library

Lecture 36 Introduction to the Seaborn Library

Lecture 37 Visualizing Distributions: Univariate and Bivariate Analysis

Lecture 38 Advanced Plots in Seaborn: Pairplots and Barplot Customization

Lecture 39 Complex Visualizations: Countplots and Heatmaps

Section 13: Scikit-Learn (sklearn) Library

Lecture 40 Introduction to Scikit-Learn and Environment Setup

Lecture 41 Data Loading Utilities in Scikit-Learn

Lecture 42 Supervised Learning with Scikit-Learn

Lecture 43 Unsupervised Learning with Scikit-Learn

Lecture 44 Data Transformation Techniques in Scikit-Learn

Lecture 45 Model Selection and Evaluation in Scikit-Learn

Lecture 46 Visualization Tools in Scikit-Learn

Lecture 47 Saving and Reusing Models in Scikit-Learn

Section 14: Deep Learning Basic

Lecture 48 Introduction to Deep Learning

Section 15: Tensorflow Framework

Lecture 49 Introduction to TensorFlow

Lecture 50 Working with Tensors and TensorFlow Operations

Lecture 51 Key Components of TensorFlow

Lecture 52 Building Models with Keras in TensorFlow

Lecture 53 Understanding the Variety of Layers in Neural Networks

Lecture 54 Project – Fashion MNIST Classification with TensorFlow

Section 16: PyTorch Framework

Lecture 55 Introduction to PyTorch

Lecture 56 Tensor Operations in PyTorch

Lecture 57 Building Neural Networks with PyTorch

Lecture 58 Project – Melanoma Cancer Prediction with PyTorch

Lecture 59 Project Extension – Data Augmentation for Cancer Prediction

Lecture 60 Project Extension – Defining a Custom Neural Network

Lecture 61 Evaluating Models with Confusion Matrix in PyTorch

Lecture 62 The final milestone!

Section 17: Conclusion

Lecture 63 About your certificate

Lecture 64 Bonus Lecture

Complete beginners who want to learn Python programming step by step, starting from the basics and moving towards advanced applications.,Aspiring data scientists and analysts who want a structured, hands-on pathway to mastering Python libraries like NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn.,Software developers, engineers, and IT professionals looking to expand their skill set into data analysis, machine learning, and deep learning.,Students and academic researchers who want to apply Python programming to analyze datasets, visualize results, and gain actionable insights for projects and publications.,Professionals working with business data, marketing analytics, or finance who want to automate data processing and generate meaningful insights efficiently.,Enthusiasts interested in deep learning, and neural networks who want practical exposure to frameworks like TensorFlow and PyTorch through real-world projects.