Machine Learning Bootcamp
A beginner-friendly, 7-week project-based bootcamp designed to take you from Python basics to deploying your first Machine Learning model. Through hands-on practice, you will master essential data manipulation, build predictive algorithms, and develop an end-to-end, industry-ready application to kickstart your career in data science.
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About This Course
A beginner-friendly, 7-week project-based bootcamp designed to take you from Python basics to deploying your first Machine Learning model. Through hands-on practice, you will master essential data manipulation, build predictive algorithms, and develop an end-to-end, industry-ready application to kickstart your career in data science.
Silabus Course
01 Python Fundamentals for Data Science
Python Fundamentals for Data Science
- Introduction to the Python ecosystem and Jupyter Notebooks.
- Basic data types, variables, and operators.
- Control flow (if/else statements, loops) and functions.
- Mini-Project: Python Logic Builder – Creating a simple text-based calculator and interactive data dictionary.
02 Data Wrangling & Manipulation
Data Wrangling & Manipulation
- Introduction to NumPy arrays and mathematical operations.
- Data manipulation with Pandas (Series and DataFrames).
- Filtering, sorting, and grouping data.
- Handling missing values and data cleaning techniques.
- Mini-Project: Messy Data Cleaner – Transforming a raw, unstructured CSV file into a clean, analytical-ready dataset.
03 Exploratory Data Analysis (EDA) & Visualization
Exploratory Data Analysis (EDA) & Visualization
- Principles of effective data storytelling.
- Creating basic plots (line, bar, scatter) with Matplotlib.
- Advanced statistical visualizations with Seaborn.
- Identifying correlations, distributions, and outliers.
- Mini-Project: Insight Dashboard – Designing a static visual report that answers three key business questions from a provided dataset.
04 Introduction to Machine Learning & Regression
Introduction to Machine Learning & Regression
- Supervised vs. Unsupervised Learning concepts.
- Understanding Linear Regression and its assumptions.
- Feature engineering: Encoding categorical variables and feature scaling.
- Evaluating regression models (MAE, MSE, RMSE, R-Squared).
- Mini-Project: Real Estate Predictor – Building a model to estimate house prices based on features like location, size, and age.
05 Foundations of Classification
Foundations of Classification
- Understanding Classification problems and use cases.
- Implementing Logistic Regression.
- Introduction to Decision Trees and how they split data.
- Evaluating classification models: Accuracy, Confusion Matrix.
- Mini-Project: Health Diagnosis App – Classifying patient risk levels (e.g., high/low risk of diabetes) using basic medical data.
06 Advanced Classification & Model Tuning
Advanced Classification & Model Tuning
- Ensemble learning techniques: Random Forest Classifier.
- Advanced evaluation metrics: Precision, Recall, and F1-Score.
- Handling imbalanced datasets.Hyperparameter tuning using Grid Search and Cross-Validation.
- Mini-Project: Spam Detector – Training an optimized Random Forest model to classify emails or SMS messages as spam or legitimate.
07 Unsupervised Learning & Clustering
Unsupervised Learning & Clustering
- Finding hidden patterns without labeled data.
- Implementing K-Means Clustering.Determining the optimal number of clusters (Elbow Method).
- Evaluating clusters using the Silhouette Score.
- Mini-Project: Customer Segmentation – Grouping mall shoppers into distinct marketing personas based on purchasing behavior.
08 Model Deployment & End-to-End Pipeline
Model Deployment & End-to-End Pipeline
- Saving and loading trained models using Pickle/Joblib.
- Introduction to building web interfaces with Streamlit.
- Best practices for UI/UX in data applications.
- Connecting the machine learning pipeline to the web app.
- Final Delivery: Deployment of the Capstone Project.
Capstone Project
End-to-End Student Success Predictor
You will act as a Data Scientist for an e-learning platform. Your objective is to analyze historical student data and build a predictive web application that identifies students who are at risk of dropping out or failing a course. You will handle the entire machine learning lifecycle—from cleaning raw engagement logs and training an optimized classification model, to deploying an interactive dashboard that instructors can use to input student metrics and receive real-time risk assessments.
Why Choose Corporate Training?
Training programs tailored to your team and organization's needs
Team Discounts
Get special pricing for group registrations. The more participants, the bigger the discount.
Custom Curriculum
Training materials can be tailored to your team's specific needs and company projects.
Flexible Schedule
Choose training times that suit your team: weekday, weekend, or special sessions at your office.
Official Certificate
All participants receive a professional certificate upon completion.
Post-Training Support
Get free consultation access for 30 days after training to ensure successful implementation.
Real Projects
Participants will work on real-world projects that can be immediately applied in their work environment.
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Frequently Asked Questions
Find answers to common questions about our training programs
Yes, we provide online (remote), offline (at your office, for corporate only), or hybrid training options based on your team's needs. All formats receive the same materials and certificates.
For corporate training, the minimum is 3 participants. However, we also accept individual registrations.
Absolutely. We offer custom curriculum services where materials can be tailored to your technology stack, active projects, and your team's specific needs.
Yes, we offer special group discounts: 10% for 5-9 participants, 15% for 10-14 participants, and 20-30% for 15+ participants from the same company.
Training duration varies depending on the material. For corporate training, schedules can be customized to your team's needs - weekday, weekend, or custom schedules.
Yes, all participants who complete the training will receive an official certificate from Rumah Coding. Digital certificates can be verified online.
Of course. We provide free consultation support for 30 days after training to help with implementation. Participants also get access to our exclusive community and training recording materials.
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For Companies?
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- Up to 30% discount
- Custom curriculum
- Flexible schedule