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NewMachine Learning · Intermediate

Machine Learning Bootcamp

A 10-week hands-on Machine Learning Bootcamp designed to take you from data fundamentals to building, training, and deploying real-world ML models using Python and industry-standard tools.

The Machine Learning Bootcamp is a structured, project-driven program designed for learners who want to master core machine learning concepts and apply them to real-world problems. Starting from data preprocessing and exploratory data analysis, the course gradually moves into supervised and unsupervised learning, model evaluation, feature engineering, and advanced algorithms like ensemble methods and gradient boosting. By the end of the program, students will not only understand how machine learning algorithms work but will also be able to build end-to-end ML pipelines and deploy models for production use cases such as prediction systems, classification engines, and recommendation systems. This bootcamp emphasizes practical implementation over theory, ensuring students gain industry-ready skills through multiple real-world projects and case studies.

10 weeks4.8 rating15+ enrolledCertificate included
PythonNumPyPandasMatplotlibSeabornScikit-learnXGBoostLightGBMJupyter NotebookGoogle ColabFlask / FastAPI (intro level)Pickle / Joblib for model savingGit & GitHub
NPR 30,000
One-time payment · Lifetime access
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10 weeks
3 modules
Real-world projects
Completion certificate

What You'll Learn

Understand core machine learning concepts and workflow
Perform data cleaning and preprocessing effectively
Conduct exploratory data analysis (EDA) using Python
Apply feature engineering techniques for better model performance
Build supervised learning models (Regression & Classification)
Implement unsupervised learning techniques (Clustering & Dimensionality Reduction)
Use advanced algorithms like Random Forest, XGBoost, and SVM
Evaluate and optimize ML models using proper metrics
Perform hyperparameter tuning for better accuracy
Build complete ML pipelines
Deploy basic machine learning models using APIs
Work on real-world datasets and industry-level problems
Prepare for ML interviews and technical assessments

Prerequisites

  • Basic understanding of Python programming
  • Familiarity with basic mathematics (algebra, probability is helpful)
  • Basic knowledge of programming logic (if-else, loops, functions)
  • No prior machine learning experience required

Who Is This For

  • Beginners who want to start a career in Machine Learning
  • Python developers transitioning into AI/ML
  • Data analysts who want to upgrade to Data Scientist roles
  • Students preparing for AI/ML internships or jobs
  • Engineers and graduates targeting tech companies or research roles
  • Anyone interested in building intelligent systems using data

Course Timeline

  • The ML landscape: Supervised vs. unsupervised vs. reinforcement; the end-to-end workflow; setting up Python, Jupyter, Git; the bias–variance idea informally.
  • Loading & inspecting data: Pandas I/O, dtypes, .info()/.describe(), sanity checks, identifying target and feature columns.
  • Missing data & outliers: Mechanisms of missingness; imputation strategies; detecting outliers (IQR, z-score) and deciding what to do about them.
  • Scaling & encoding: Standardisation vs. normalisation; one-hot, ordinal, and target encoding; why scaling matters for distance/gradient methods.
  • Train/test discipline: The cardinal rule of no leakage; train_test_split, stratification, and fitting transformers on train only.
  • Practical Lab 1 — Cleaning a Messy Dataset: Take a deliberately dirty version of the Ames Housing dataset (mixed types, missing values, inconsistent categories). Produce a cleaned, documented dataset with an accompanying notebook that records every decision. Deliver a preprocessing.py that reproduces the cleaning deterministically.
  • Assignment 1: Given a raw CSV, deliver a reproducible cleaning script plus a one-page memo defending each imputation and encoding choice. Grading emphasis: no leakage, deterministic output, clear justification.

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Enroll today and start your first module within 24 hours.

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