Master Python 3.14, PyTorch, YOLO26, Hugging Face, OpenCV, and MLOps across a 12-week, 110+ hour program with 24 guided labs, 6 mini-projects, and 1 capstone project.
Our AI & Machine Learning course provides comprehensive, hands-on training in artificial intelligence. You'll learn to develop, train, tune, and deploy intelligent models for predictive analytics, computer vision, natural language processing, and automated decision-making.
Progressive hands-on modules from Python data foundations to deep learning and MLOps deployment:
Python syntax, data types, control flow, functions, collections, file handling, exceptions and debugging. Python 3.14, Jupyter/Colab and Git/GitHub basics. Build: Python Data Processing Application.
OOP, decorators, generators, working with APIs/JSON, virtual environments with uv, and NumPy/Pandas for numerical and tabular data. Build: Data Handling Toolkit.
Descriptive statistics, probability, distributions, hypothesis testing, correlation, confidence intervals, linear algebra/calculus intuition, SQL and data visualization. Build: Statistical Analysis Notebook.
Exploratory data analysis, data quality checks, missing values, outliers, encoding, scaling, feature selection and scikit-learn pipelines. Build: Reusable Data Preparation Pipeline.
Linear/polynomial/regularized regression (Ridge/Lasso), Logistic Regression, KNN, Naive Bayes, SVM, Decision Trees, and evaluation metrics (MAE/RMSE/R², precision/recall/F1/ROC-AUC). Build: Price Predictor & Risk Classifier.
Random Forest, XGBoost, LightGBM, CatBoost, cross-validation, hyperparameter tuning with Optuna, feature importance, SHAP and fairness/bias checks. Build: Optimized Explainable Prediction System.
K-Means, hierarchical clustering, DBSCAN, PCA, dimensionality reduction, anomaly detection and time series forecasting basics. Build: Segmentation or Anomaly Detection Solution.
Perceptron to multi-layer networks, forward/backpropagation, activation/loss functions, optimizers, regularization, PyTorch/Keras basics and GPU concepts. Build: Deep Neural Network — From Scratch and in PyTorch.
CNNs, sequence models (RNN/LSTM/GRU), transfer learning, pretrained model fine-tuning and a Transformer/attention preview. Build: Transfer Learning Image/Sequence Model.
Image processing with OpenCV, CNN architectures (ResNet, EfficientNet, Vision Transformers), object detection with YOLO26 and image segmentation. Build: Image Classification & Object Detection Application.
Text preprocessing, embeddings, the Transformer/attention mechanism, pretrained models via Hugging Face (BERT, GPT-family, T5), and fine-tuning with LoRA/QLoRA. Build: Text Intelligence Application.
Experiment tracking (MLflow/Weights & Biases), model serving with FastAPI/Streamlit/Docker, cloud deployment, monitoring, drift and responsible AI governance. Build: Production-Ready Capstone.
Python 3.14, NumPy, Pandas, SQL, Matplotlib, Seaborn
Scikit-learn, XGBoost, LightGBM, CatBoost, Optuna, SHAP
PyTorch, Keras, OpenCV, YOLO26, Vision Transformers
Hugging Face, LoRA/QLoRA, MLflow, FastAPI, Docker
Design, build, and deploy machine learning models to production
Develop end-to-end intelligent systems, neural networks, and AI pipelines
Build image processing, object detection (YOLO26), and vision solutions
Fine-tune transformer models, implement RAG systems, and build NLP applications
Basic programming concepts (Python preferred) and high school math.
ML Engineer, AI Developer, Vision Specialist, NLP Engineer, Data Scientist