Machine Learning Bootcamp: Build Ml Models Using Genai

Machine Learning for non-coders | Understand Machine Learning concepts & use GenAI to write code for building ML models

What you'll learn

Build a strong foundation in Python, statistics, and machine learning—covering regression, classification, and model evaluation

Work with real datasets to clean, preprocess, and visualize data using NumPy, Pandas, and Seaborn for ML readiness

Implement core ML algorithms in Python with ChatGPT-assisted coding for faster, cleaner, and more efficient development

Master advanced ML techniques like ensemble methods, grid search, and SVMs to create high-performing predictive models

Handle missing values, outliers, categorical variables, and feature scaling to improve model quality and accuracy

Leverage ChatGPT to explain complex ML concepts, debug Python code, and generate efficient solutions in real-time

Compare multiple models side-by-side to select the best fit for predictive accuracy and business requirements

Requirements

No prior programming or machine learning experience required—A computer with internet access, basic familiarity with browsing tools, and a willingness to explore AI-assisted learning methods.

Description

If you’re an aspiring data scientist, analyst, or AI enthusiast looking to break into one of the most in-demand fields of the decade, imagine having a hands-on guide that teaches you not only the theory—but also how to code, implement, and fine-tune models—without getting lost in complexity. What if you could accelerate your learning curve by having an AI partner (ChatGPT) that helps you write cleaner code, debug faster, and understand concepts more intuitively?In this immersive, practical bootcamp, you’ll gain the technical skills, problem-solving mindset, and project experience needed to work confidently with real-world machine learning applications. Whether you’re building predictive models, classifying data, or tuning advanced algorithms, this course equips you to move from “learning about ML” to “building with ML” in record time.In this hands-on course, you will:Master the full ML workflow – from data import, exploration, and preprocessing to model building, evaluation, and optimization.Understand the math and logic behind key algorithms like Linear & Logistic Regression, Decision Trees, Random Forests, KNN, SVM, Boosting methods, and more.Learn with ChatGPT-assisted coding – using AI to generate, optimize, and debug Python code for faster, more accurate implementation.Work with Python’s top ML libraries like NumPy, Pandas, Seaborn, Scikit-learn, and XGBoost.Build both regression and classification models and understand when to apply each.Gain experience in advanced topics like model tuning with Grid Search, feature engineering, ensemble methods, and kernel-based SVMs.Throughout the course, you’ll:Use ChatGPT to write and refine Python code for ML tasks.Explore side-by-side the theory of an algorithm and its real Python implementation.Work with real-world datasets, handling missing values, outliers, and categorical variables.Compare and evaluate models to select the best approach for a given problem.Build a portfolio-ready set of projects that showcase both coding ability and ML understanding.Machine Learning is more than just knowing algorithms—it’s about applying them effectively to real data. By the end of this bootcamp, you’ll be able to confidently approach ML problems, build and optimize models, and leverage AI tools like ChatGPT to boost your productivity and accuracy.Whether you’re preparing for a career in data science, adding ML to your skill set as a developer, or simply exploring the potential of AI-powered problem solving, you’ll walk away with the skills, confidence, and workflow to succeed.Enroll today to build the future—one model at a time—powered by Python, guided by AI, and driven by data.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Resources

Section 2: Setting up Python and Jupyter Notebook

Lecture 3 Installing Python and Anaconda

Lecture 4 Opening Jupyter Notebook

Lecture 5 Introduction to Jupyter

Lecture 6 Arithmetic operators in Python: Python Basics

Lecture 7 Strings in Python: Python Basics

Lecture 8 Lists, Tuples and Directories: Python Basics

Lecture 9 Working with Numpy Library of Python

Lecture 10 Working with Pandas Library of Python

Lecture 11 Working with Seaborn Library of Python

Section 3: Basics of Statistics

Lecture 12 Types of Data

Lecture 13 Types of Statistics

Lecture 14 Describing data Graphically

Lecture 15 Measures of Centers

Lecture 16 Measures of Dispersion

Section 4: Introduction to Machine Learning

Lecture 17 Introduction to Machine Learning

Lecture 18 Building a Machine Learning Model

Section 5: Data Preparation

Lecture 19 Gathering Business Knowledge

Lecture 20 Data Exploration

Lecture 21 The Dataset and the Data Dictionary

Lecture 22 Importing Data in Python(ChatGPT Assisted)

Lecture 23 Univariate analysis and EDD

Lecture 24 Univariate Analysis on Data in Python(ChatGPT Assisted)

Lecture 25 Outlier Treatment

Lecture 26 Outlier Treatment in Python(ChatGPT Assisted)

Lecture 27 Missing Value Imputation

Lecture 28 Missing Value Imputation in Python(ChatGPT Assisted)

Lecture 29 Seasonality in Data

Lecture 30 Bi-variate analysis and Variable transformation

Lecture 31 Variable Transformation in Python(ChatGPT Assisted)

Lecture 32 Non-usable variables

Lecture 33 Dummy variable creation: Handling qualitative data

Lecture 34 Dummy Variables in Python(ChatGPT Assisted)

Lecture 35 Correlation Analysis

Lecture 36 Correlation Matrix in Python(ChatGPT Assisted)

Section 6: Linear Regression

Lecture 37 Linear Regression – Introduction

Lecture 38 Basic Equations and Ordinary Least Squares (OLS) method

Lecture 39 Assessing accuracy of predicted coefficients

Lecture 40 Assessing Model Accuracy: RSE and R squared

Lecture 41 Simple Linear Regression using Python(ChatGPT Assisted)

Lecture 42 Multiple Linear Regression

Lecture 43 The F - statistic

Lecture 44 Interpreting results of Categorical variables

Lecture 45 Multiple Linear Regression in Python(ChatGPT Assisted)

Lecture 46 Test-train split

Lecture 47 Bias Variance trade-off

Lecture 48 Train-Test Split in Python(ChatGPT Assisted)

Lecture 49 Shrinkage methods: Ridge and Lasso

Lecture 50 Ridge & Lasso Regression in Python(ChatGPT Assisted)

Lecture 51 Heteroscedasticity

Section 7: Introduction to the classification Models

Lecture 52 Three classification models and Data set

Lecture 53 Load Classification Data in Python(ChatGPT Assisted)

Lecture 54 The problem statements

Lecture 55 Why can't we use Linear Regression?

Section 8: Logistic Regression & Classification Basics

Lecture 56 Logistic Regression

Lecture 57 Simple Logistic Regression in Python(ChatGPT Assisted)

Lecture 58 Result of Simple Logistic Regression

Lecture 59 Logistic with multiple predictors

Lecture 60 Multiple Logistic Regression in Python(ChatGPT Assisted)

Lecture 61 Confusion Matrix

Lecture 62 Predictions and Confusion Matrix in Python(ChatGPT Assisted)

Lecture 63 Evaluating performance of model

Lecture 64 Evaluating Model Performance in Python(ChatGPT Assisted)

Section 9: Linear Discriminant Analysis (LDA)

Lecture 65 Linear Discriminant Analysis

Lecture 66 Linear Discriminant Analysis (LDA) in Python(ChatGPT Assisted)

Section 10: K-Nearest Neighbors (KNN)

Lecture 67 Splitting Data in Python(ChatGPT Assisted)

Lecture 68 K-Nearest Neighbors classifier

Lecture 69 KNN in Python – Part (ChatGPT Assisted)

Lecture 70 KNN in Python – Part 2(ChatGPT Assisted)

Section 11: Comparing results from 3 models

Lecture 71 Understanding the results of classification models

Lecture 72 Summary of the three models

Section 12: Decision Trees

Lecture 73 Introduction to Decision trees

Lecture 74 Basics of Decision Trees

Lecture 75 Understanding a Regression Tree

Lecture 76 The stopping criteria for controlling tree growth

Lecture 77 Load Data for Decision Tree Regressor in Python

Lecture 78 Imputing Missing Values for Decision Tree Regressor in (ChatGPT Assisted)

Lecture 79 Dummy Variables for Decision Tree Regressor in Python(ChatGPT Assisted)

Lecture 80 Splitting Data for Decision Tree Regressor in Python(ChatGPT Assisted)

Lecture 81 Training & Evaluating Decision Tree Model in Python(ChatGPT Assisted)

Lecture 82 Plotting Decision Tree in Python(ChatGPT Assisted)

Lecture 83 Pruning a tree

Lecture 84 Pruning Decision Tree in Python(ChatGPT Assisted)

Section 13: Classification Trees

Lecture 85 Classification tree

Lecture 86 The Data set for Classification problem

Lecture 87 Preprocessing Classification Tree in Python(ChatGPT Assisted)

Lecture 88 Training Classification Tree in Python(ChatGPT Assisted)

Lecture 89 Advantages and Disadvantages of Decision Trees

Section 14: Ensemble technique 1 - Bagging

Lecture 90 Bagging

Lecture 91 Bagging in Python(ChatGPT Assisted)

Section 15: Ensemble technique 2 - Random Forests

Lecture 92 Random Forests

Lecture 93 Random Forests in Python(ChatGPT Assisted)

Lecture 94 Grid Search for Random Forest in Python(ChatGPT Assisted)

Section 16: Ensemble technique 3 - Boosting

Lecture 95 Boosting

Lecture 96 Ensemble technique 3a - Boosting in Python(ChatGPT Assisted)

Lecture 97 Ensemble technique 3b - AdaBoost in Python(ChatGPT Assisted)

Lecture 98 Ensemble technique 3c - XGBoost in (ChatGPT Assisted)

Section 17: Support Vector Machines

Lecture 99 Introduction to SVM's

Lecture 100 The Concept of a Hyperplane

Lecture 101 Maximum Margin Classifier

Lecture 102 Limitations of Maximum Margin Classifier

Section 18: Support Vector Classifier

Lecture 103 Support Vector classifiers

Lecture 104 Limitations of Support Vector Classifiers

Section 19: Support Vector Machines

Lecture 105 Kernel Based Support Vector Machines

Section 20: Creating Support Vector Machine Model in Python

Lecture 106 SVM in Python

Lecture 107 SVM Regressor – Preprocessing in Python(ChatGPT Assisted)

Lecture 108 SVM Regressor – Scaling in Python(ChatGPT Assisted)

Lecture 109 Support Vector Regression (SVR) in Python(ChatGPT Assisted)

Lecture 110 Preprocessing SVC in Python(ChatGPT Assisted)

Lecture 111 Support Vector Classification (SVC) in Python(ChatGPT Assisted)

Lecture 112 Grid Search for SVM in Python(ChatGPT Assisted)

Lecture 113 Polynomial Kernel SVM in Python(ChatGPT Assisted)

Lecture 114 Radial Kernel SVM in Python(ChatGPT Assisted)

Section 21: Conclusion

Lecture 115 About your certificate

Lecture 116 Bonus Lecture

Aspiring data scientists and analysts who want a structured, hands-on introduction to machine learning using Python.,Students and professionals from non-technical backgrounds eager to break into the ML/AI field with the help of ChatGPT-assisted coding.,Software developers, engineers, and IT professionals looking to expand their skill set into machine learning and predictive analytics.,Business analysts, managers, and domain experts who want to use data-driven models to solve real-world problems.,Anyone curious about how to apply machine learning—from regression to advanced ensemble methods—without needing prior ML experience.