12/06/2025
Learn Machine Learning and AI at IT Training Nepal!
Step into the future of technology with our 60-hour immersive training on Machine Learning and AI. This all-in-one course equips you with the mathematical foundations, machine learning algorithms, deep learning expertise, and hands-on project experience needed to thrive in data-driven roles.
What You’ll Learn:
Mathematics for Machine Learning & Data Science
- Linear Algebra: Vectors, matrices, matrix operations, eigenvalues, eigenvectors
- Calculus: Differentiation, integration, gradients, partial derivatives
- Probability: Distributions, Bayes theorem, random variables
- Statistics: Hypothesis testing, sampling, confidence intervals, ANOVA
- Optimization: Gradient Descent, Stochastic Gradient Descent (SGD), Convex Optimization
Programming Foundations
- Python for Data Science
- Jupyter Notebooks, NumPy, Pandas, Matplotlib, Seaborn
- File handling, OOP in Python, list comprehensions
- Introduction to R and Julia (optional track)
Core Machine Learning
- Introduction to ML: Supervised, Unsupervised, Reinforcement learning
- Regression: Linear, Polynomial, Ridge, Lasso
- Classification: Logistic Regression, SVM, Decision Trees, Random Forest
- Clustering: K-Means, DBSCAN, Agglomerative
- Model Evaluation: Accuracy, Precision, Recall, F1, AUC-ROC, confusion matrix
- Dimensionality Reduction: PCA, t-SNE, LDA
- Feature Engineering & Feature Selection
- Cross-Validation and Bias-Variance Tradeoff
Advanced Machine Learning Algorithms
- Ensemble Methods: Bagging, Boosting, Stacking
- XGBoost, LightGBM, CatBoost
- Gradient Boosting Machines (GBM)
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes Classifier
- Anomaly Detection and Outlier Detection
Deep Learning & Neural Networks
- Feedforward Neural Networks (FNN)
- Backpropagation and optimization
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN), LSTMs, GRUs
- Transformers and Attention Mechanisms
- Autoencoders and Variational Autoencoders (VAE)
- Generative Adversarial Networks (GANs)
- Transfer Learning: Using pre-trained models like ResNet, VGG, Inception
Large Language Models (LLMs)
- Transformers: BERT, GPT, T5, LLaMA, Mistral, Falcon
- Prompt Engineering Techniques
- Fine-tuning LLMs for specific use-cases
- Text summarization, classification, translation, question answering
- Building Chatbots using LLMs
- Retrieval-Augmented Generation (RAG)
Natural Language Processing (NLP)
- Text preprocessing (tokenization, stemming, lemmatization)
- Word embeddings: Word2Vec, GloVe, FastText
- Named Entity Recognition (NER), POS tagging, sentiment analysis
- Topic modeling: LDA, NMF
- Sequence-to-sequence models and translation
- Hugging Face Transformers Library
Generative AI and Creative Applications
- Stable Diffusion: Text-to-image generation
- DALL·E, Midjourney, RunwayML
- AI-generated music and voice (Jukebox, ElevenLabs)
- AI in design: Canva AI, Adobe Firefly
- Generating synthetic data and use in model training
Bioinformatics with Python
- BioPython library for biological data
- Working with DNA/RNA sequences
- Parsing FASTA, GenBank files
- Sequence alignment algorithms
- Phylogenetic analysis
- Genomic data visualization
Computer Vision
- Image preprocessing and augmentation
- Object detection: YOLO, SSD, Faster R-CNN
- Face recognition and tracking
- Optical Character Recognition (OCR)
- Image segmentation with U-Net, Mask R-CNN
Data Engineering & Big Data
- Data pipelines with Python
- Web scraping with BeautifulSoup, Selenium
- Working with large datasets using Dask
- Big Data tools: Hadoop, Spark (PySpark)
- ETL processes and data warehousing basics
Model Deployment & MLOps
- Saving/loading models with Pickle, Joblib, ONNX
- Building REST APIs with Flask and FastAPI
- Model Deployment using Streamlit and Gradio
- Docker for containerization
- CI/CD pipelines for ML (GitHub Actions, MLflow)
- Monitoring models in production
Cloud Computing for AI
- Introduction to AWS, GCP, Azure
- Cloud Notebooks: Google Colab, Azure ML, SageMaker
- Storing and retrieving models/data from cloud
- Deploying models on cloud platforms (Heroku, AWS EC2, Lambda)
Projects and Capstone Work
- House price prediction (regression)
- Customer churn prediction (classification)
- Face mask detection (CNN + OpenCV)
- Movie recommendation system (collaborative filtering)
- Text summarization using Transformers
- Sentiment analysis from Twitter data
- Stock market prediction using LSTMs
- Image generation with Stable Diffusion
- Genomic data analysis with BioPython
- Real-time chatbot with GPT API
Who Can Join?
- Students and fresh graduates
- Working professionals in IT, analytics, engineering
- Tech enthusiasts and hobbyists
- Entrepreneurs looking to leverage AI for startups
Tools & Technologies Covered:
- Python, Scikit-learn, TensorFlow, PyTorch
- Pandas, NumPy, Matplotlib, Seaborn
- Jupyter, VS Code, Git & GitHub
- Hugging Face, OpenAI, Keras, Gradio, Streamlit
- BioPython, OpenCV, Dask, Spark, AWS, Docker
Duration: 120 hours
Location: IT Training Nepal, Putalisadak
Format: In-person training with practical projects
Seats: Limited
Register Now and take the first step toward becoming an AI/ML expert.
Contact: 01-5340005, 9801169144 for more details.
https://www.ittrainingnepal.com/machine-learning-with-python/
Shape your future with Machine Learning at IT Training Nepal!