Program Overview
Transform your career with industry’s most comprehensive LLMOps specialization
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
What you’ll learn
- Understand Core ML Concepts — Learn supervised and unsupervised learning, classification, regression, and clustering
- Implement ML Algorithms — Apply key algorithms like linear regression, decision trees, and neural networks using Python
- Evaluate Model Performance — Use metrics such as accuracy, precision, recall, and F1-score to assess models
- Work with Real-World Data — Preprocess, clean, and analyze datasets for effective machine learning applications
Skills you’ll learn
- Generative AI
- Model Deployment
- Cloud Platforms
- Vector Databases
- MLOps
Tools you’ll learn
- Python 3
- PyTorch
- NumPy
- Jupyter Notebook
- scikit-learn
- Google Colab
Comprehensive Curriculum
6 sections, 69 lessons, 16 weeks of foundational machine learning
Key Topics
- Why Machine Learning is Exciting
- What Is Machine Learning?
- Logistic Regression and Interpretation
- Multilayer Perceptron Concepts and Math Model
- Deep Learning and Transfer Learning
- Model Selection and Early History of Neural Networks
- Hierarchical Structure of Images
- Convolution Filters and Convolutional Neural Networks
- CNN Math Model and How the Model Learns
- Applications in Use and Practice
- Introduction to PyTorch
Hands-on Projects
- Implement logistic regression with PyTorch
- Build multilayer perceptron from scratch
- Create CNN for image classification
- Transfer learning with pre-trained models
