Our Speech Recognition with Python course explores the technology that powers modern voice-activated systems and AI tools like virtual assistants, automated transcription devices, and home devices. We break down the theory behind speech recognition, covering Python audio processing and machine learning aspects in an easy-to-understand format. Along the way, we demonstrate the use of the librosa library, showing you how to perform essential audio processing tasks that are key to preparing sound data for analysis. You’ll gain hands-on experience as you implement speech-to-text tools using cutting-edge AI models like OpenAI’s Whisper and Google’s Web Speech API. Additionally, you'll explore the appropriate use of popular speech recognition toolkits like Assembly AI, Meta's Wav2Letter, Mozilla DeepSpeech, and cloud-based solutions, such as Amazon Transcribe and Azure Speech, considering accessibility and costs. This speech recognition course unravels the behind-the-scenes processes that drive speech recognition. We explain how various methodologies operate—from audio feature extraction and noise cleaning to deep learning and transformers. We also cover essential audio concepts, including sound wave properties, analog-to-digital conversion, acoustics fundamentals, and aspects of human hearing. By the end of the course, you'll be fully equipped with the skills to examine the speech recognition technology in greater depth and understand the fundamentals needed to build your own AI-powered model. This course—tailored for data analysts, scientists, audio engineers, AI enthusiasts, and anyone with a curious mind—demonstrates how to convert sound files into structured, text-based outputs for analysis. Whether you’re working with audio data or exploring AI, the Speech Recognition with Python course equips you with the knowledge to effectively transform audio into actionable insights.