Apa itu machine learning?

Machine learning adalah cabang kecerdasan buatan yang membuat komputer belajar pola dari data, bukan dari aturan yang ditulis satu per satu. Jenis utamanya supervised learning (data berlabel), unsupervised learning (tanpa label), dan reinforcement learning (belajar dari reward).

Apakah kursus machine learning ini cocok untuk pemula?

Ya. Materi dimulai dari pengenalan, dasar Python, NumPy, dan pandas, lalu matematika yang dijelaskan secara intuitif sebelum masuk ke algoritma.

Apakah harus jago matematika untuk belajar machine learning?

Tidak. Konsep seperti vektor, statistik, peluang, turunan, dan gradient descent dijelaskan secara visual dan langsung dibuktikan dengan kode Python.

Berapa lama belajar machine learning sampai bisa membuat model sendiri?

Dengan paket Basic (10 sesi) Anda sudah bisa membersihkan data dan melatih model klasifikasi maupun regresi. Ensemble, tuning, deep learning, dan deploy dipelajari di paket Intermediate dan Advanced.

Apakah ada video rekaman dan modul tertulis?

Ya. Peserta mendapat 18 video tutorial lengkap dengan PDF panduan langkah demi langkah dan source code, ditambah rekaman sesi live.

Spesifikasi laptop yang dibutuhkan?

Windows, macOS, atau Linux dengan RAM minimal 8 GB. GPU tidak wajib; semua contoh, termasuk CNN PyTorch, bisa dijalankan di CPU.

Apakah ada sertifikat?

Ya, sertifikat diberikan setelah menyelesaikan paket kursus.

Online Machine Learning Python Course - Learn from Zero to Deploy Models

What You'll Learn in the Machine Learning Course?

The curriculum follows the data scientist workflow in the real world: data processing, algorithm selection, evaluation, tuning, and then deploying the model in an application.

Python for Data

NumPy, pandas, dan matplotlib: mengolah tabel, operasi vektor, groupby, dan visualisasi data.

Preprocessing & EDA

Empty data, outliers, duplicates, encoding, scaling, and scikit-learn pipeline without data leakage.

Supervised Learning

Regression, Logistic Regression, KNN, Naive Bayes, Decision Tree, Random Forest, SVM, XGBoost, LightGBM.

Evaluation & Tuning

Confusion matrix, precision, recall, F1, ROC-AUC, cross-validation, Ridge/Lasso, GridSearch, Optuna.

Unsupervised Learning

K-Means, DBSCAN, hierarchical clustering, PCA, t-SNE, UMAP, Isolation Forest, and Apriori.

Deep Learning & Deploy

Neural network and CNN with PyTorch, Q-learning, and deploy model into Streamlit web application.

Complete Curriculum: 18 Modules of Machine Learning Python

Each module is available as a 10–15 minute video tutorial complete with a PDF guide. Module 1 can be watched for free.

Foundations

Supervised Learning

Model Evaluation & Optimization

Unsupervised Learning

Deep Learning & Reinforcement Learning

Final Project

18Final Project: Deploy Model ke Web (Streamlit)

8 Real-World Projects You'll Build

Iris classification & K-Means

First model with 100% accuracy on test data, then clustering without labels.

Sales prediction from advertising costs

Multiple regression with R² 0,94 and coefficient analysis for business decisions.

Breast cancer detection & spam filtering

Logistic Regression ±99% accuracy and Naive Bayes for messages.

Retail customer segmentation

Five customer segments from income and shopping score, complete with profile for each segment.

Detect suspicious transactions

Isolation Forest & LOF capture anomalies without labeled fraud examples.

Handwritten digit recognition (CNN)

Self-made PyTorch convolutional network with ±98% accuracy.

AI Agent escapes from a maze

Q-learning from scratch: agent finds the shortest path only from reward.

Web app for house price prediction

Pipeline + Gradient Boosting tuned, saved with joblib, and used via Streamlit.

Who is Suitable to Learn Machine Learning?

Beginners wanting to enter the AI world

Start from Python basics and concepts, without needing to be good at math first.

Students & researchers (thesis/dissertation)

Master the most commonly used methods in research: classification, regression, clustering, and model evaluation.

Data analysts & professionals

Level up from dashboards to predictive models that can be used for business decisions.

Programmers & developers

Add machine learning skills and deploy models to web apps or APIs.

Format Belajar & Paket Kursus

Basic · 10 sessions

Python untuk data, praproses & EDA, regresi, dan klasifikasi dasar. Proyek: model prediksi pertama.

Intermediate · 20 sessions

Plus Random Forest, SVM, XGBoost, evaluation, tuning, feature engineering, clustering, and PCA.

Advanced · 30 sessions

Plus anomaly detection, deep learning PyTorch, reinforcement learning, and deploy web applications.

Every live online session is 1.5 hours with an instructor, including recording, exercise files, discussion group, and certificate.

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Lihat semua kelas machine learning

Butuh Model yang Langsung Dikerjakan?

We also offer machine learning services: predictive models, customer segmentation, anomaly detection, deep learning, up to AI web applications.

Lihat Jasa Machine Learning

Common Questions About Machine Learning Courses

What is machine learning?+

Machine learning is a branch of artificial intelligence that enables computers to learn patterns from data, not from rules written one by one. Its main types are supervised learning (labeled data), unsupervised learning (no labels), and reinforcement learning (learning from rewards).

Is this machine learning course suitable for beginners?+

Yes. The material starts with an introduction, basics of Python, NumPy, and pandas, then moves to mathematics explained intuitively before diving into algorithms.

Do I need to be good at math to learn machine learning?+

No. Concepts such as vectors, statistics, probability, derivatives, and gradient descent are explained visually and directly proven with Python code.

Which libraries are studied?+

NumPy, pandas, matplotlib, scikit-learn, XGBoost, LightGBM, Optuna, UMAP, PyTorch, mlxtend, joblib, and Streamlit.

How long does it take to learn machine learning until I can create my own model?+

With the Basic package (10 sessions), you can already clean data and train classification and regression models. Ensemble, tuning, deep learning, and deployment are covered in the Intermediate and Advanced packages.

Are there video recordings and written modules?+

Yes. Participants receive 18 video tutorials complete with PDF step-by-step guides and source code, plus recordings of live sessions.

What are the laptop specifications required?+

Windows, macOS, or Linux with at least 8 GB of RAM. GPU is not required; all examples, including PyTorch CNN, can run on CPU.

Is there a certificate?+

Yes, a certificate is provided after completing the course package.

Online Machine Learning Python Course - Learn from Zero to Deploy Models