Is this course for complete beginners?

Yes! The bootcamp is designed to guide you step by step, even if you are a beginner. We cover Python from scratch, making it accessible to anyone with an interest in Machine Learning.

What happens if the batch fills up?

Each cohort has a fixed number of seats so that everyone gets attention during the live sessions. The seats remaining are shown on the page and update as students are approved. Once the batch is full, enrolment closes until the next one is announced.

Do I need a high-end computer?

Not at all. A modern computer (Windows, macOS, or Linux) with at least 4GB of RAM is sufficient to follow along.

Will I get a certificate after completion?

Absolutely. You will receive a professional, verifiable certificate that you can share on your LinkedIn profile and resume.

Premium Course

Machine Learning Bootcamp
Beginner to Advanced

Welcome to the ultimate Machine Learning journey. Whether you are a complete beginner starting your programming journey, a developer who wants to improve your skills, or an aspiring data scientist preparing for an industry career, this bootcamp is designed to guide you step by step from your current level.

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30+ students enrolled

Last updated 07/2026

Sinhala

What you'll learn

6 Core Outcomes

Master the Fundamentals: Gain a rock-solid foundation in Python programming, NumPy, Pandas, and the essential Mathematics & Statistics required for Data Science.

Build Predictive Models: Implement Supervised and Unsupervised Learning algorithms including Regression, Classification, SVM, Decision Trees, and Clustering.

Leverage Ensemble Techniques: Achieve high accuracy using advanced algorithms like Random Forest, AdaBoost, Gradient Boosting, and XGBoost.

End to End Project Lifecycle: Master the 10 stages of an AI project lifecycle, from Data Collection and Feature Engineering to Model Tuning and Evaluation.

Real World Application: Complete enterprise level projects including Healthcare Prediction and Credit Risk Modelling.

Industry Standard MLOps & Cloud: Deploy your models securely using Streamlit and FastAPI. Master experiment tracking with MLflow and learn model training on AWS SageMaker.

Requirements

•A solid understanding of Python is required.
•Prior AI/ML experience is helpful but not necessary. This course is ideal for developers, AI enthusiasts, and tech professionals specializing in automation.
•Hardware: A modern computer (Windows, macOS, or Linux) with at least 4GB of RAM

Description

This course connects theory with real world practice. Instead of only learning basic definitions from textbooks, you will experience the actual workflow of an AI/ML Engineer. You will learn the mathematics and algorithms behind Machine Learning, and you will also build, deploy, and monitor real machine learning models using industry tools like AWS, MLflow, and FastAPI.

By the end of this bootcamp, you will have the practical skills and hands on experience needed to turn your knowledge into real world expertise that employers value.

Course Content

15 sections • 174 lectures • 2 month 14 h self practice total

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1.1 What is Machine Learning?

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1.2 Classification vs Regression

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1.3 Supervised vs Unsupervised Learning

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2.1 Setup Environment

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2.2 Variables

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2.3 Numbers

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2.4 Strings

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2.5 Lists

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2.6 If Condition

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2.7 For Loop

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2.8 Functions

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2.9 Dictionary and Tuples

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2.10 Modules and Pip

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2.11 File Handling

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2.12 Classes and Objects

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2.13 Inheritance

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2.14 Exception Handling

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3.1 Introduction and Benefits

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3.2 Basic Operations

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3.3 Matrix Operations

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3.4 Slicing, Stacking

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4.1 Pandas Introduction and Installation

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4.2 Dataframe Basics

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4.3 Read, Write Excel and CSV Files

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4.4 Handle Missing Data

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4.5 Grouping Data

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4.6 Data Concatenation and Merging

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4.7 Data Visualization Using Matplotlib and Seaborn

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5.1 Descriptive vs. Inferential Statistics

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5.2 Measures of Central Tendency: Mean, Median, Mode

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5.3 Percentile

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5.4 Analysis: Shoe Sales (Using Mean, Median, Percentile)

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5.5 Measures of Dispersion: Range, IQR

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5.6 Box or Whisker Plot

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5.7 Outlier Treatment Using IQR and Box Plot

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5.8 Measures of Dispersion: Variance and Standard Deviation

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5.9 Analysis: Stock Returns Volatility (Using Variance and Std Dev)

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5.10 Correlation

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5.11 Correlation vs Causation

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5.12 Probability Basics

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5.13 Addition and Multiplication Rule

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5.14 Conditional Probability and Bayes Theorem

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5.15 What Is a Distribution?

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5.16 Skewness

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5.17 Normal Distribution

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5.18 Detect Outliers Using Normal Distribution

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5.19 Z Score

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5.20 Standard Normal Distribution (SND)

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5.21 Random Sampling & Sample Bias

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5.22 The Law of Large Numbers

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5.23 Central Limit Theorem, Sampling Distribution

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5.24 Case Study: Solar Panels

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5.25 Standard Error

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5.26 Z Score Table (Z-Table)

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5.27 Confidence Interval

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6.1 Simple Linear Regression

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6.2 Multiple Linear Regression

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6.3 Cost Function

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6.4 Derivatives and Partial Derivatives

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6.5 Chain Rule

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6.6 Gradient Descent Theory

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6.7 Gradient Descent: Python Implementation

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6.8 Why MSE (and not MAE)?

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6.9 Model Evaluation: Train, Test Split

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6.10 Model Evaluation: Metrics

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6.11 Data Preprocessing: One Hot Encoding

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6.12 Polynomial Regression

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6.13 Overfitting and Underfitting

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6.14 Reasons and Remedies For Overfitting / Underfitting

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6.15 L1 and L2 Regularization

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6.16 Bias Variance Trade Off

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7.1 Introduction to Classification

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7.2 Logistic Regression: Binary Classification

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7.3 Model Evaluation: Accuracy, Precision and Recall

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7.4 Model Evaluation: F1 Score, Confusion Matrix

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7.5 Logistic Regression: Multiclass Classification

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7.6 Cost Function: Log Loss

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7.7 Support Vector Machine (SVM)

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7.8 Data Pre-processing: Scaling

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7.9 Sklearn Pipeline

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7.10 Naive Bayes: Theory

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7.11 Naive Bayes: SMS Spam Classification

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7.12 Decision Tree: Theory

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7.13 Decision Tree: Salary Classification

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7.14 Handle Class Imbalance: Theory

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7.15 Handle Class Imbalance Using imblearn: Churn Prediction

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8.1 What is Ensemble Learning?

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8.2 Majority Voting, Average and Weighted Average

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8.3 Bagging

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8.4 Bagging: Random Forest

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8.5 Random Forest: Raisin Classification

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8.6 Boosting: AdaBoost

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8.7 Gradient Boosting: Regression Walk Through

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8.8 Gradient Boosting: Regression Math

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8.9 Gradient Boosting: Revenue Prediction

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8.10 Gradient Boosting: Classification

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8.11 XGBoost: Walk Through

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8.12 XGBoost: California Housing Prediction

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8.13 XGBoost: Synthetic Data Classification

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8.14 XGBoost: Benefits

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9.1 Introduction

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9.2 Model Evaluation: ROC Curve & AUC

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9.3 Cost Benefit Analysis Using ROC in Sklearn

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9.4 K Fold Cross Validation

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9.5 Stratified K Fold Cross Validation

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9.6 Hyperparameter Tuning: GridsearchCV

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9.7 Hyperparameter Tuning: RandomizedSearchCV

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9.8 Model Selection Guide

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9.9 Selecting the Right Evaluation Metric

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10.1 Stages of AI Project Life Cycle

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10.2 Requirements and Scope of Work (SOW)

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10.3 Data Collection

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10.4 Data Cleaning & Exploratory Data Analysis

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10.5 Feature Engineering

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10.6 Model Selection & Training

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10.7 Model Fine Tuning

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10.8 Model Deployment

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10.9 Monitoring and Feedback Using ML Ops

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11.1 three Ways of Doing Feature Engineering

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11.2 Feature Selection Using Correlation

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11.3 Feature Selection Using Variance Inflation Factor (VIF)

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11.4 VIF: Practical Implementation (Salary Prediction)

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12.1 Introduction

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12.2 K Means Clustering: Theory

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12.3 K Means Clustering: Customer Segmentation

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12.4 Hierarchical Clustering: Theory

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12.5 Hierarchical Clustering: Customer Segmentation

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12.6 DBSCAN: Theory

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12.7 DBSCAN: Practical Implementation

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13.1 Project Charter Meeting

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13.2 Scope of Work, Task Planning in JIRA

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13.3 Data Collection

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13.4 Data Cleaning & EDA

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13.5 Feature Engineering

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13.6 Model Training, Fine Tunning

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13.7 98% Model Accuracy, Really?

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13.8 Error Analysis

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13.9 Model Segmentation

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13.10 Request More Data

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13.11 Model Retraining

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13.12 Deployment

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14.1 Domain Understanding

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14.2 Scope of Work & Tech Architecture

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14.3 Data Collection

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14.4 Quick Intro to Data Leakage

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14.5 Data Cleaning

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14.6 Exploratory Data Analysis (EDA)

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14.7 Feature Engineering

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14.8 Weight of Evidence (WOE), Information Value (IV)

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14.9 Model Training & Evaluation

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14.10 Introduction to Optuna

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14.11 Model Fine Tuning Using Optuna

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14.12 Intro To Rank Ordering & KS Statistic

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14.13 Model Evaluation Using KS Statistic & Gini Coefficient

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14.14 Business Presentation

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14.15 Deployment

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15.1 What is ML Ops?

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15.2 Importance of ML Ops in Your Career

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15.3 ML Flow: Purpose and Overview

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15.4 ML Flow: Experiment Tracking

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15.5 ML Flow: Model Registry

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15.6 ML Flow: Centralized Server Using Dagshub

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15.7 What is API?

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15.8 FastAPI Basics

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15.9 Build FastAPI Server Project

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15.10 Git Version Control System

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15.11 Introduction to ML Cloud Platforms

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15.12 AWS Sagemaker: Account Setup

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15.13 AWS Sagemaker: Sagemaker Studio

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15.14 AWS Sagemaker: 4 Ways to Train Model

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15.15 AWS Sagemaker: Built In Algorithms

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15.16 AWS Sagemaker: Script Mode

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15.17 Data Drift Detection Using PSI & CSI

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15.18 PSI & CSI: Practical Implementation

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Frequently Asked Questions

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Machine Learning BootcampBeginner to Advanced

What you'll learn

Requirements

Description

Course Content

Start Learning for Free!

Frequently Asked Questions

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Machine Learning Bootcamp
Beginner to Advanced