Taught by Working Engineers
Learn from the same engineers building production software at APX Creation—not career instructors.
APX Academy · Kathmandu
IT courses that prepare you to build
Hands-on training in programming languages, machine learning, and industry ML frameworks—taught by working engineers at APX Creation. Small batches, real projects, direct mentorship.
11
Courses
4
Learning Tracks
100%
Hands-on
Practical training, not theory-only lectures
APX Academy is the training arm of APX Creation. Our courses cover foundational programming in C, C++, and Python, a structured machine learning path from preparatory math through deep learning, and hands-on work with the frameworks teams actually use in production.
Real Projects, Real Code
Every course includes hands-on labs and a capstone project you can showcase in your portfolio.
Small Batches, Direct Mentorship
Maximum 15 students per batch. You get direct access to instructors, not recorded videos alone.
Industry-Relevant Curriculum
Course content shaped by what hiring teams and technical leads actually look for in candidates.
Programming Languages
Core language courses for students, career-changers, and engineers who need strong fundamentals.
Foundation
C Programming
Memory management, pointers, structs, file I/O, and systems thinking—the foundation for embedded, OS, and performance-critical software.
- Syntax, control flow & functions
- Pointers, arrays & memory allocation
- Structs, unions & modular C
- Debugging with GDB & build tools
Foundation
C++ Programming
Object-oriented design, STL, modern C++ (C++11/14/17), and patterns used in game engines, finance, and high-performance applications.
- OOP: classes, inheritance & polymorphism
- STL containers, iterators & algorithms
- RAII, smart pointers & move semantics
- Templates & real-world project build
Foundation
Python Programming
From scripts to structured applications: Python fundamentals through APIs, automation, and the skills needed before data science or ML work.
- Python syntax, data structures & OOP
- File handling, modules & virtual environments
- APIs, web scraping & automation scripts
- Testing, packaging & capstone project
Machine Learning Path
Preparatory courses plus core ML—designed as a progressive track from prerequisites to deployable models.
Preparatory
ML Preparatory: Math & Statistics
Linear algebra, probability, and statistics explained for ML practitioners—not abstract math for its own sake.
- Vectors, matrices & linear transformations
- Probability, distributions & Bayes’ rule
- Descriptive & inferential statistics
- Gradient intuition for optimization
Preparatory
ML Preparatory: Python for Data Science
NumPy, Pandas, Matplotlib, and Jupyter workflows—the toolkit you need before any ML or deep learning course.
- NumPy arrays & vectorized operations
- Pandas DataFrames & data cleaning
- Visualization with Matplotlib & Seaborn
- Exploratory data analysis project
Core ML
Machine Learning Fundamentals
Supervised and unsupervised learning, model evaluation, feature engineering, and end-to-end ML pipelines on real datasets.
- Regression, classification & clustering
- Train/validation/test splits & cross-validation
- Feature engineering & model selection
- Deployment-ready ML project
Core ML
Deep Learning
Neural networks, CNNs, RNNs, transfer learning, and practical training workflows for vision and sequence problems.
- Neural network architecture & backprop
- CNNs for image classification
- RNNs, LSTMs & sequence models
- Transfer learning & fine-tuning
ML Frameworks & Tools
Framework-specific courses for engineers ready to build and ship models with industry-standard libraries.
Data Tools
NumPy & Pandas for ML
Focused deep-dive on numerical computing and tabular data manipulation for ML pipelines.
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Framework
scikit-learn
Classical ML algorithms, pipelines, hyperparameter tuning, and model persistence with scikit-learn.
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Framework
TensorFlow & Keras
Build, train, and export neural networks with Keras on TensorFlow—including saved models and serving basics.
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Framework
PyTorch
Dynamic computation graphs, custom training loops, and research-to-production workflows with PyTorch.
ApplySuggested Learning Path
Follow our recommended progression or jump in at the level that matches your background.
1
Pick a Language
C, C++, or Python
2
ML Preparatory
Math + Python for Data Science
3
Core ML
ML Fundamentals & Deep Learning
4
Frameworks
scikit-learn, TensorFlow, PyTorch
Not sure where to start? Contact us for a free skill assessment.
Who Should Attend?
Aspiring Engineers
University students and career-changers looking for high-impact, industry-relevant skills.
Corporate Teams
Engineering departments seeking to upskill in emerging technologies like AI, Cloud, and Security.
IT Leaders
CTOs and Lead Developers who need to understand the strategic implementation of new tech stacks.
Educational Institutions
Colleges and universities looking to modernize their curricula with industry-standard labs.
Enrollment Process
01
Apply Online
Click Apply on any course—you’ll land on our contact page with the course pre-selected. Tell us your background and preferred batch.
02
Interview / Assessment
A brief technical discussion to ensure the program matches your skill level and goals.
03
Onboarding
Receive your access to our learning management system and project repositories.
04
Training Begins
Engage in live sessions, hands-on labs, and real-world project development.
What Our Students Say
“The ML Fundamentals course gave me the practical foundation I needed. The capstone project became my portfolio piece for job interviews.”
“Best Python course I've taken. The instructor's industry experience shows in how they teach debugging and project structure, not just syntax.”
“Our team took the TensorFlow course as a group. The hands-on labs and direct access to instructors made a huge difference vs. self-paced options.”