AI / ML Training in Nepal
Master machine learning and deep learning, from the maths to deployed models.
- 3 Months
- Mode: Online, Hybrid & Physical Live Classes
- Beginner to Intermediate
Learn directly from Rohan Aryal
AI IntegratedTotal fee
Rs. 12,000
Start with
Rs. 6,000
Course overview
This Artificial Intelligence and Machine Learning program gives you a solid, practical grounding in how machines learn from data. You will learn the essential maths intuitively, prepare real datasets, and train classical machine learning and deep learning models.
The course covers supervised and unsupervised learning, neural networks, computer vision and natural language processing, then shows you how to package models as APIs and integrate them with modern generative AI tools. You will finish with an end-to-end ML project you can show employers.
What you will learn
- Apply linear algebra, probability and statistics to ML problems
- Clean, explore and engineer features from real datasets
- Train and evaluate regression, classification and clustering models
- Build neural networks, CNNs and RNNs with TensorFlow and PyTorch
- Solve computer vision and NLP problems
- Deploy models as APIs and interactive apps
Tools covered
Some of the major industry-relevant tools you will work with in this course:
- Python
- NumPy
- Pandas
- Matplotlib
- Scikit-learn
- TensorFlow
- Keras
- PyTorch
- OpenCV
- Jupyter
- FastAPI
- Streamlit
Curriculum
The complete syllabus for AI / ML: every lesson, section and topic you will cover. Download it as a PDF to read offline or share it.
9 lessons · 28 sections · 100 topics
- Variables, data types and operators
- Control flow and functions
- Lists, dictionaries and comprehensions
- Working in Jupyter Notebook and Google Colab
- Creating and indexing arrays
- Vectorised maths and broadcasting
- Random numbers and simulations
- Linear algebra with NumPy
- Loading CSV and Excel data
- Selecting, filtering and grouping data
- Handling missing values
- Charts with Matplotlib and Seaborn
- Vectors, matrices and their operations
- Dot products and distances
- Matrix multiplication in ML models
- Eigenvectors intuition for PCA
- Mean, median, variance and standard deviation
- Probability rules and conditional probability
- Normal and binomial distributions
- Sampling, confidence and hypothesis testing
- Derivatives and slopes intuitively
- Partial derivatives and gradients
- Gradient descent step by step
- Learning rate and convergence
- Detecting and handling missing values
- Outlier detection and treatment
- Removing duplicates and fixing types
- Encoding categorical variables
- Scaling and normalisation
- Creating new features from dates and text
- Feature selection techniques
- Univariate and bivariate analysis
- Correlation and heatmaps
- Finding patterns and telling the story
- Case study: real estate price data
- Linear regression and assumptions
- Polynomial regression
- Ridge and Lasso regularisation
- Regression metrics: MAE, RMSE and R²
- Logistic regression
- K-nearest neighbours
- Support vector machines
- Naive Bayes
- Classification metrics: accuracy, precision, recall and F1
- Decision trees
- Random forests
- Gradient boosting and XGBoost
- Feature importance
- Train, validation and test splits
- Cross-validation
- Hyperparameter tuning with grid and random search
- Handling imbalanced data
- K-means clustering and choosing K
- Hierarchical clustering and dendrograms
- DBSCAN for density-based clustering
- Customer segmentation project
- Principal component analysis
- Visualising high-dimensional data
- t-SNE intuition
- Anomaly detection
- Association rules and market basket analysis
- Recommendation system basics
- Perceptrons and multi-layer networks
- Activation functions
- Backpropagation intuitively
- Optimisers: SGD, Adam and learning rates
- Building models with TensorFlow and Keras
- Building models with PyTorch
- Saving, loading and reusing models
- Convolutional neural networks for images
- Pooling, padding and feature maps
- RNNs and LSTMs for sequences
- Dropout, batch normalisation and transfer learning
- Image processing with OpenCV
- Image classification with CNNs
- Object detection with YOLO
- Face detection project
- Text cleaning and tokenisation
- Bag of words, TF-IDF and word embeddings
- Sentiment analysis
- Text classification project
- Attention and transformers intuition
- Using pre-trained models from Hugging Face
- Adding LLM features to ML projects
- Saving models with joblib and pickle
- Building prediction APIs with FastAPI
- Input validation and error handling
- Interactive apps with Streamlit
- Containerising with Docker
- Deploying to a cloud platform
- Experiment tracking basics
- Monitoring model performance and drift
- Retraining strategies
- Problem definition and data collection
- Success metrics and baseline model
- Model development and experiments
- Deployment with an API or app
- Documentation and code review
- Demo day
- Internship readiness evaluation
What you get
- Live instructor-led classes
- Recorded video of every session
- Hands-on projects and assignments
- 1:1 mentor support and doubt sessions
- Internship pathway after evaluation
- Verifiable completion certificate
- Learner community on Discord
- Career guidance and portfolio review
Fees & payment plan
3 Months program. Choose the class type that suits you. The curriculum, mentors, recordings and certificate are the same in every mode.
Online
Live, interactive classes over video, from anywhere in Nepal or abroad.
Rs. 12,000
Enroll online| Payment plan | Online | Hybrid | Physical |
|---|---|---|---|
First installment (50%) Payable at enrollment | Rs. 6,000 | Rs. 6,000 | Rs. 6,000 |
Second installment (50%) Payable when you are promoted to the internship | Rs. 6,000 | Rs. 6,000 | Rs. 6,000 |
| Total | Rs. 12,000 | Rs. 12,000 | Rs. 12,000 |
Your mentor
Rohan Aryal
Mentor
Rohan mentors Leafclutch Academy's AI and data programs, guiding learners from their first lines of Python to building agents, generative AI applications, machine learning models and analytics dashboards, with a strong focus on hands-on projects.
Who can join
- Basic programming knowledge (Python preferred)
- High-school level mathematics
- A laptop with at least 8 GB RAM
Career opportunities
- Machine Learning Engineer
- AI Engineer
- Junior Data Scientist
- Computer Vision Engineer
Earn a high-value industry certificate
Complete AI / ML and receive a certificate from Leafclutch Academy. Add it to your LinkedIn profile, resume or CV to stand out to recruiters. Every certificate carries a unique code that employers can check online.
Already earned a certificate? Verify it here
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Frequently asked questions
How much maths do I need?
School-level maths is enough. We teach the linear algebra, statistics and calculus you need with visual, practical examples.
AI/ML or Data Science: which should I choose?
Choose AI/ML if you want to build models and AI products. Choose Data Science if you are more interested in analysing data and answering business questions with it.
Which deep learning framework is taught?
Both TensorFlow/Keras and PyTorch, so you can work with either in a job.
Start AI / ML this month
Talk to our counsellors for free. We will help you choose the right course, mode and batch.