Одну секунду
Что-то сучилось. Попробуйте еще раз.
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: 🤍🤍youtube.com/c/YCubed ⭐️ Code and Resources ⭐️ 🔗 Supervised learning (classification/MAGIC): 🤍colab.research.google.com/drive/16w3TDn_tAku17mum98EWTmjaLHAJcsk0?usp=sharing 🔗 Supervised learning (regression/bikes): 🤍colab.research.google.com/drive/1m3oQ9b0oYOT-DXEy0JCdgWPLGllHMb4V?usp=sharing 🔗 Unsupervised learning (seeds): 🤍colab.research.google.com/drive/1zw_6ZnFPCCh6mWDAd_VBMZB4VkC3ys2q?usp=sharing 🔗 Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters) 🔗 MAGIC dataset: 🤍archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope 🔗 Bikes dataset: 🤍archive.ics.uci.edu/ml/datasets/Seoul+Bike+Sharing+Demand 🔗 Seeds/wheat dataset: 🤍archive.ics.uci.edu/ml/datasets/seeds 🏗 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 Learn to code for free and get a developer job: 🤍🤍freecodecamp.org Read hundreds of articles on programming: 🤍freecodecamp.org/news
I've watched this video since 3 am to 7 am
This is for beginners?
Lazar machine
It is not outdated to say that there are only two genders.
i_LwzRVP7bg&t=3m20s 3:20 not clear what you actually did to import the dataset ?
i_LwzRVP7bg&t=29m39s 29:39
When doing the val_loss < least_val_loss I get TypeError : '<' not supported between instances of 'list' and 'float'. Did anyone get this error as well?
from kmeans its hard :)
on test and train set, why dont use the train_test_split from ski-learn to split the data instead of that complicated python function?
i_LwzRVP7bg&t=32m38s 32:38
Who cares if she's a genius... my bestie from Caltech is the 2nd smartest person on Earth, and he doesn't go around bragging about really being a Real Genius. IQ 250, learned C++ professionally in 1 week. Shave the beard and be careful about your words.
Lol😂 she had to be careful because people might get mad thinking there is only 2 genders😂
Yeah but what can I build and what problems can I solve
i_LwzRVP7bg&t=18m13s 18:13 anyone get the silicon valley reference
Accessible to absolute beginners....
Her voice and way of teaching is so soothing. I fell asleep listening to her and I am gonna watch this every night.
What is the name oof the presenter?
too weak , it's just confusing course with 0 math
Hi
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