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).
Create and activate conda environment:
conda create -n intro-to-ML-coursera python=3.10 conda activate intro-to-ML-courseraInstall
pip3 install -r requirements.txtpython -m IPythonIf you'd like to run these examples on your own machine, we've provided installation instructions in 1A_PyTorch_Installation.ipynb. We'll be spending most of our time coding in IPython notebooks; if you haven't used an IPython notebook before, 1B_Coding_Environments.ipynb will give you a quick primer, as well as a few alternatives.
Grokking the Cross Entropy Loss