Transform raw data into intelligent predictions that drive Industry 4.0 innovation.
From regression to neural networks — master AI, ML, and real-world implementation with Python.
Upskill your analytics, automate insights, and transition from analyst to AI practitioner.
Course Overview
As industries move into the Fourth Industrial Revolution, data is no longer just an asset — it’s the foundation for intelligent automation and predictive decisions. This 5-day instructor-led program is designed for professionals ready to apply AI, data science, and machine learning techniques using Python.
You’ll begin with the foundations of ML, AI, and deep learning, and then progress through statistics, regression, time series, NLP, clustering, SVMs, and neural networks. With a focus on business use cases, the course bridges theoretical concepts with hands-on labs, industry datasets, and model deployment practices.
Whether you’re a developer, analyst, or architect, you’ll walk away with real tools to transform data into decisions using Python — and a deeper understanding of how these technologies integrate into Industry 4.0.
Learning Objectives
Supervised & unsupervised ML algorithms
Python for AI: NumPy, Pandas, SciPy, Matplotlib, Seaborn
Data preprocessing, transformation, and normalization
Linear/logistic regression, SVMs, clustering, time series
Model evaluation (cross-validation, confusion matrix, ROC)
Ensemble learning: Random Forest, XGBoost, AdaBoost
Natural Language Processing (NLP) techniques
Deep learning with TensorFlow and ANN
End-to-end model deployment strategies
Who Should Attend
Python developers entering data science or ML
Business & data analysts seeking deeper AI integration
Architects and analytics leads looking to automate decisions
Data professionals transitioning into AI-centric roles
Graduates or upskilling professionals pursuing AI careers
Prerequisites
Basic Python programming knowledge
No prior data science or AI experience required
Ideal for intermediate-level learners exploring AI and ML
Course Modules
Module 1: Introduction to AI and ML
Understand AI concepts, supervised vs unsupervised learning, and set up your Python environment.
Module 2–5: Python & Data Handling
Use NumPy and Pandas to import, clean, and manipulate datasets; explore statistical foundations.
Module 6–7: Data Preprocessing & Visualisation
Transform, normalize, and visualize data using matplotlib, seaborn, and EDA techniques.
Module 8–13: Supervised Learning Techniques
Build linear/logistic regression, decision trees, ensemble models (Random Forest, XGBoost), and evaluate them.
Module 14–16: Advanced Modelling
Explore SVMs, Ridge, PCA, spectral clustering, and hyperparameter tuning techniques.
Module 17–18: Clustering & NLP
Use k-means, hierarchical clustering, and NLP tools to segment and analyze unstructured data.
Module 19: Deep Learning with ANN
Design multi-layered neural networks with TensorFlow and validate ANN models.
Module 20: End-to-End ML Deployment
Apply models to real-world datasets and simulate use-case implementations from training to deployment.
Professional Outcomes
Completing this course opens doors to roles like Machine Learning Engineer, Data Scientist, AI Analyst, or Python AI Developer — enabling you to tackle predictive modeling, customer segmentation, and AI-driven automation across industries.
Certification Details
No specific exam for this course
Frequently Asked Questions
Is this a beginner-level course?
No. This is an intermediate-level course that requires a basic understanding of Python.
Do I get hands-on experience with real datasets?
Yes. Each module includes practical exercises using industry-aligned data.
Are tools like TensorFlow, Pandas, and Scikit-learn included?
Yes. The course includes instruction and exercises with all major Python AI libraries.
Will I learn both supervised and unsupervised techniques?
Yes. The course covers a wide range of machine learning algorithms across both categories.
Is deep learning part of the course?
Yes. You will build, evaluate, and optimize neural networks using TensorFlow.
Can I apply these skills to business problems?
Yes. The course emphasizes practical applications like churn prediction, forecasting, and segmentation.
Is this course aligned to any certification?
No. This course is skills-focused and not tied to a specific certification body.
Is this course HRDC claimable?
Yes. It is fully claimable under HRDC for eligible employers in Malaysia.
Can this course be conducted for my internal team?
Yes. GemRain offers both on-site and virtual private training sessions.
Will I receive a certificate of completion?
Yes. You will receive an official GemRain certificate upon successfully completing the course.