Training Syllabus

🕘 Duration: 3 full days (08:00 AM – 04:00 PM GMT+7)

👥 Minimum Participants: 5 people

💼 Fee: Based on agreement and venue

📋 Training Overview

This Mchine Learning for Beginners training is designed for participants who wants to the world of artificial intelligence (AI) and Machine Learning, specifically deep learning. Participants will learn how Python is used to build, train, and evaluate neural network models using modern libraries like TensorFlow and Keras.
This training focuses on hands-on coding and real-world case studies, enabling participants to understand the machine learning and deep learning workflow, from data preprocessing and model architecture to training and evaluation. Download sylabus.

📚 Topics Covered

Session 1 – Introduction to Deep Learning and the Environment

  • A Brief definition of AI and Machine Learning
  • Machine Learning vs. Deep Learning Concepts.
  • Python programming concept.
  • Neural Network Architecture.
  • Installing and Setting Up TensorFlow/Keras.
  • Hands-on.

Session 2 – Data Preparation and Manipulation for Deep Learning

  • Data loading and preprocessing (normalization, one-hot encoding)
  • Data splitting: training, validation, testing
  • Using Pandas and scikit-learn for the data pipeline
  • Data distribution visualization with Matplotlib/Seaborn
  • Hands-on: MNIST dataset and initial analysis
  • Introduction to NLP.

Session 3 – Builidng and Training Neural Network

  • layer concept, neuron, activation function, loss, dan optimizer
  • Backpropagation dan gradient descent (konsep intuitif)
  • Implementasi model DNN (Fully Connected Network)
  • Evaluasi model: accuracy, loss curve
  • Hands-on: Membangun model klasifikasi digit (MNIST)

Session 4 – Convolutional Neural Network (CNN) for Image Data

  • Convolution, pooling and filter concept.
  • Popular CNN Architectures.
  • Implementing CNN with Keras.
  • Data Augmentation for improving performance.
  • Hands-on: Image Classification.

Session 5 – Recurrent Neural Network (RNN) and LSTM for Sequential Data

  • Sequence data concept and temporal dependencies.
  •  RNN architecture and LSTM.
  • RNN/LSTM implementation with Keras.
  • Case study: simple text sentiment analysis.
  • Hands-on: Text prediction.

Session 6 – Optimization and Model Evaluation

  • Overfitting vs underfitting.
  • Regularization (Dropout, Early Stopping).
  • Hyperparameter tuning.
  • Model evaluation dan confusion matrix.
  • Hands-on: Improving performance model of  CNN.
  • Introduction to Transfer learning.
  • Hand-on: Optimization.

Session 7 – Mini Project: Deep Learning Case Study

  • Choosing dataset ( image,text and sound/audio).
  • Preprocessing and data exploration.
  • Membangun model, pelatihan, dan evaluasi.
  • Interpretasi hasil (visualisasi prediksi, confusion matrix).
  • Mini project presentation.

🎯 Learning Outcomes

By the end of this training, participants will:

  • Understand the foundational concepts of Python for Machine learning Deep Learning

  • Use Python libraries for data science tasks

  • Using Python and popular libraries (NumPy, Pandas, Matplotlib) to prepare the data.

  • Apply machine learning concepts to real-world projects

📞 Contact

  • 📧 Email: wbudiharto@binus.edu

  • 📱 Mr. Prof. Widodo (WhatsApp): +62 856 9887 384

  • 📱 Ms. Dr. Emny (WhatsApp): +62 813 8741 3863

Silabus Pelatihan

Python Programming for Deep Learning

🕘 Durasi: 3 hari fullday (08:00 – 17:00 WIB)

👥 Jumlah Peserta Minimum: 5 orang

💼 Biaya: Sesuai dengan kesepakatan dan lokasi pelatihan

📋 Deskripsi Pelatihan

Pelatihan Machine learning for Beginners ini dirancang untuk peserta yang telah memahami dasar-dasar Python dan ingin melangkah ke dunia Kecerdasan Artifisial (AI), khususnya machine learning dan deep learning. Peserta akan belajar bagaimana Python digunakan untuk membangun, melatih, dan mengevaluasi model neural network menggunakan pustaka modern seperti TensorFlow dan Keras.

Pelatihan ini berfokus pada praktik langsung (hands-on coding) dan studi kasus nyata, agar peserta dapat memahami alur kerja deep learning dari preprocessing data, arsitektur model, pelatihan (training) hingga evaluasi.

📚 Materi yang Akan Dipelajari

Sesi 1 – Pengenalan Machine Learning dan  Environment

  • Definisi AI dan Machine Learning
  • Review singkat Python dan NumPy
  • Konsep Machine Learning vs Deep Learning
  • Arsitektur Neural Network
  • Instalasi dan setup TensorFlow/Keras
  • Hands-on

Sesi 2 – Persiapan dan Manipulasi Data untuk Deep Learning

  • Data loading dan preprocessing (normalisasi, one-hot encoding).
  • Split data: training, validation, test.
  • Menggunakan Pandas dan scikit-learn untuk pipeline data.
  • Visualisasi distribusi data dengan Matplotlib/Seaborn.
  • Hands-on: Dataset MNIST dan analisis awal .
  • Pengenalan NLP.

Sesi 3 – Membangun dan Melatih Neural Network

  • Konsep layer, neuron, activation function, loss, dan optimizer
  • Backpropagation dan gradient descent (konsep intuitif)
  • Implementasi model DNN (Fully Connected Network)
  • Evaluasi model: accuracy, loss curve
  • Hands-on: Membangun model klasifikasi digit (MNIST)

Sesi 4 – Convolutional Neural Network (CNN) untuk Data Gambar

  • Konsep convolution, pooling, dan filter
  • Arsitektur CNN populer
  • Implementasi CNN dengan Keras
  • Augmentasi data untuk meningkatkan performa
  • Hands-on: Klasifikasi gambar

Sesi 5 – Recurrent Neural Network (RNN) dan LSTM untuk Data Sequential

  • Konsep sequence data dan temporal dependencies
  • Arsitektur RNN dan LSTM
  • Implementasi RNN/LSTM dengan Keras
  • Studi kasus: Analisis sentimen teks sederhana
  • Hands-on: Prediksi teks

Sesi 6 – Optimisasi dan Evaluasi Model

  • Overfitting vs underfitting
  • Regularisasi (Dropout, Early Stopping)
  • Hyperparameter tuning
  • Model evaluation dan confusion matrix
  • Hands-on: Meningkatkan performa model CNN
  • Pengenalan Transfer Learning
  • Hands-on: Optimization

Sesi 7 – Mini Project: Deep Learning Case Study

  • Pemilihan dataset (gambar, teks, atau suara)
  • Preprocessing dan eksplorasi data
  • Membangun model, pelatihan, dan evaluasi
  • Interpretasi hasil (visualisasi prediksi, confusion matrix)
  • Presentasi hasil mini project

🎯 Tujuan Pelatihan

Setelah mengikuti pelatihan ini, peserta diharapkan mampu:

  1. Memahami konsep dasar machine learning, deep learning dan NLP.
  2. Menggunakan Python dan library populer (NumPy, Pandas, Matplotlib) untuk menyiapkan data.
  3. Memahami struktur dasar neural network dan proses pelatihannya.

📞 Kontak

  • 📧 Email: wbudiharto@binus.edu

  • 📱 Bpk. Prof. Widodo (WA): +62 856 9887 384

  • 📱 Ibu Dr. Emny (WA): +62 813 8741 3863