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Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts.✏️ Kylie Ying developed this course. Check out her channel: ⭐️ Code and Resources ⭐️🔗 Supervised learning (classification/MAGIC): 🔗 Supervised learning (regression/bikes): 🔗 Unsupervised learning (seeds): 🔗 Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters)🔗 MAGIC dataset: 🔗 Bikes dataset: 🔗 Seeds/wheat dataset: 🏗 Google provided a grant to make this course possible. ⭐️ Contents ⭐️⌨️ (0:00:00) Intro⌨️ (0:00:58) Data/Colab Intro⌨️ (0:08:45) Intro to Machine Learning⌨️ (0:12:26) Features⌨️ (0:17:23) Classification/Regression⌨️ (0:19:57) Training Model⌨️ (0:30:57) Preparing Data⌨️ (0:44:43) K-Nearest Neighbors⌨️ (0:52:42) KNN Implementation⌨️ (1:08:43) Naive Bayes⌨️ (1:17:30) Naive Bayes Implementation⌨️ (1:19:22) Logistic Regression⌨️ (1:27:56) Log Regression Implementation⌨️ (1:29:13) Support Vector Machine⌨️ (1:37:54) SVM Implementation⌨️ (1:39:44) Neural Networks⌨️ (1:47:57) Tensorflow⌨️ (1:49:50) Classification NN using Tensorflow⌨️ (2:10:12) Linear Regression⌨️ (2:34:54) Lin Regression Implementation⌨️ (2:57:44) Lin Regression using a Neuron⌨️ (3:00:15) Regression NN using Tensorflow⌨️ (3:13:13) K-Means Clustering⌨️ (3:23:46) Principal Component Analysis⌨️ (3:33:54) K-Means and PCA Implementations🎉 Thanks to our Champion and Sponsor supporters:👾 Raymond Odero👾 Agustín Kussrow👾 aldo ferretti👾 Otis Morgan👾 DeezMaster Hosted on Acast. See acast.com/privacy for more information.