Introduction to Machine Learning

Learn machine learning fundamentals through this comprehensive 3-hour video course that covers essential concepts from basic principles to advanced neural network architectures. Start with foundational machine learning concepts and build your first model before progressing through multi-layer perceptrons, convolutional neural networks, and autoencoders. Explore natural language processing applications including sentiment analysis, text generation using LSTMs and GRUs, and text classification with CNNs. Master recurrent neural networks (RNNs) and their variants, then advance to hybrid LSTM architectures and sequence-to-sequence models for complex tasks. Gain hands-on experience with practical implementations while learning how to leverage Weights & Biases as an AI developer platform for MLOps and LLMOps workflows throughout your machine learning projects.

Syllabus

Intro to ML: Course Overview
0. What is machine learning?
1. Build Your First Machine Learning Model
2. Multi-Layer Perceptrons
3. Convolutional Neural Networks
4. Autoencoders
5. Sentiment Analysis
6. Recurrent Neural Networks [RNNs]
7. Text Generation using LSTMs and GRUs
8. Text Classification Using Convolutional Neural Networks
9. Hybrid LSTMs [Long Short-Term Memory]
10. Seq2Seq Models
Weights & Biases: The AI Developer Platform for MLOps and LLMOps