Guest
Learning Scientific Computing and Machine Learning in Python in a top-down approach by simple coding examples. Today we will cover essential libraries for plotting, data handling and numerical algorithms. Find the repo here: Here is Part 1: Here is Part 2: Here is Part 4 (equivalent to the workshop recording): Link to the Playlist: ------- 📝 : Check out the GitHub Repository of the channel, where I upload all the handwritten notes and source-code files (contributions are very welcome): 📢 : Follow me on LinkedIn or Twitter for updates on the channel and other cool Machine Learning & Simulation stuff: and 💸 : If you want to support my work on the channel, you can become a Patreon here: ------- ⚙️ My Gear: (Below are affiliate links to Amazon. If you decide to purchase the product or something else on Amazon through this link, I earn a small commission.) - 🎙️ Microphone: Blue Yeti: - ⌨️ Logitech TKL Mechanical Keyboard: - 🎨 Gaomon Drawing Tablet (similar to a WACOM Tablet, but cheaper, works flawlessly under Linux): - 🔌 Laptop Charger: - 💻 My Laptop (generally I like the Dell XPS series): - 📱 My Phone: Fairphone 4 (I love the sustainability and repairability aspect of it): If I had to purchase these items again, I would probably change the following: - 🎙️ Rode NT: - 💻 Framework Laptop (I do not get a commission here, but I love the vision of Framework. It will definitely be my next Ultrabook): As an Amazon Associate I earn from qualifying purchases. ------- Timestamps: 00:00:00 Intro & Overview 00:11:15 Sk-Learn: Ordinary Linear Regression (cont.) 00:34:51 Sk-Learn: Linear Regression with Feature Engineering 01:08:03 Sk-Learn: Regularization 01:58:01 Sk-Learn: Linear Regression in High Dimensions 02:22:21 Sk-Learn: Logistic Regression 02:48:50 More on Classification 02:56:51 Sk-Learn: K-Means Clustering 03:32:47 Sk-Learn: PCA Dimensionality Reduction 03:54:21 Sk-Learn: Wrap-Up 04:08:01 Deep Learning Intro 04:23:20 Automatic Differentiation in TensorFlow 04:59:38 Summary & Outlook & Outro
