Machine Learning for Everybody – Full Course

I have no idea how my YouTube algorithm brought me here while I was sleeping but it made for some strange dreams

I've been trying to learn ML for quite awhile but could never really grasp the algorithim. She explains how the formula comes about and why is it used in the classification or regression so well. My god. Thumbs up for sensei Kylie and free code camp!!!

For anyone getting an error related to converting a list to a float, the model.evaluate is actually returning a list. She has the correction in the code at around 2:05:51, but she doesn't explicitly mention the correction. You just grab the first value in the list (which is why she puts [0]). So change the line where you obtain the val_loss to: val_loss = model.evaluate(X_valid, y_valid)[0]

NichesPanel likes this xD we all know that they isn't, but do you think models buy followers to appear on the internet?

⌨ (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

falling asleep lands me in odd places

Thanks for an amazingly simplified approach to ML 👍

Yesterday I click on a video called 'learning phyton for Beginners'. Today youtube's algorithm sent this video. I was so confuse but somehow listen to it and when I feel I understand something from this explanation, it makes me excited. A genius can make someone understand complicated things, I am very grateful.

It seems half of us are here after falling asleep

I have to agree with those calling this tutorial too hard. I am a professional developer studying Cyber-Sec at the Master's level and found the first hour of the tutorial to be so intimidating that I had to go and learn Python again, just to boost my confidence. I followed it by getting a tutorial on Pandas as well as Numpy, those helped. I came back and realized that, while this is a really good tutorial, it isn't beginner-friendly at all. The kind of stuff Kylie accomplishes in a single line needs multiple lines from me and many more minutes to understand what's going on. As advice to all the newbies, don't be intimidated, try taking the Python basics, Pandas and Numpy courses before attempting this tutorial, perhaps watch the first hour to see what's required and come back.

Kylie Ying is a gift to humanity

Kylie is such a great teacher and obviously not only understands but applies these topics in the real world. What a great combination, thanks for the course!

If you're getting an error about comparing a list to a float. Changing the "least_val_loss" variable to a list with two infinite floats will fix it. Like this: least_val_loss = [float('inf'), float('inf')]

You are literally the best, I've been looking for a tutorial for three days and yours works

my 7th day - still not finished. Just so nice to see someone do ML work live! Thank you

Her voice and way of teaching is so soothing. I fell asleep listening to her and I am gonna watch this every night.

Absolutely brilliant. As mentioned in the intro Kylie is a true genius. god bless her

This is amazing. I'm a data analyst and had some formal training in machine learning, but my classes were really surface and "teach yourself" style. This is so much better. I also find it easier to listen to women, so that's a bonus lol

This is my first Course which I've completed from FCC, got a good understanding on ML now, Thank you !!

00:06 Kylie Ying teaches machine learning for absolute beginners 03:29 Importing and labeling data with pandas in machine learning. 10:47 Unsupervised learning uses unlabeled data to find patterns. 14:19 One hot encoding is used to represent categorical data as binary values. 21:04 Data set structure and model training process. 24:22 Training and validation sets are used to assess and tune machine learning models 31:10 Plotting histograms for different features based on class 34:24 Creating train, validation, and test data sets 41:23 Using random oversampling to rebalance data sets 44:57 Introduction to creating a scatter plot to represent data visually. 52:39 Implementing K Nearest Neighbors with SK learn package 55:42 Understanding precision, recall, f1 score and accuracy in machine learning 1:02:36 Bayes rule helps calculate probability based on given conditions. 1:05:51 Explaining Bayes' theorem and applying it to classification 1:13:06 Using probability to make classification predictions 1:16:39 Using MAP and Naive Bayes for classification. 1:23:49 Introduction to the sigmoid function and logistic regression 1:27:53 Logistic regression with L2 penalty 1:35:13 Introduction to Kernel Trick in SVM 1:38:37 Neural networks use neurons to process input and determine output. 1:45:12 Back propagation adjusts weights based on loss. 1:48:19 TensorFlow simplifies defining and training ML models 1:54:54 Training and evaluating a machine learning model 1:58:27 Setting up and compiling the neural network model for training. 2:06:19 Analyzing the accuracy and validation of the machine learning model. 2:09:24 Neural nets and SVM performed similarly, demonstrating different model options. 2:16:19 Simple and multiple linear regression explained 2:20:24 Residual plot shows distribution around line of best fit. 2:27:40 Understanding root mean squared error and coefficient of determination 2:31:32 Understanding total sum of squares and its significance 2:38:07 Creating and manipulating a data frame in Python for data analysis 2:41:29 Plotting and analyzing the relationship between various weather parameters and bike count. 2:48:48 Training and evaluation of a linear regression model 2:52:58 Using TensorFlow for regression with neural networks 3:00:14 Using a neural net with more than one node for prediction 3:04:00 Using neural net model for non-linear prediction. 3:12:12 Discussing the concept of unsupervised learning and introducing k means clustering 3:15:41 Assigning points to the closest centroid 3:23:32 Unsupervised learning techniques: clustering and dimensionality reduction 3:27:10 Principal Component Analysis (PCA) maps points onto one-dimensional space 3:34:09 Predicting wheat varieties based on wheat kernel features. 3:37:41 Unsupervised learning uses no class information for analysis. 3:45:20 K-means clusters and PCA explained 3:48:46 PCA reduces dimensions to 2 for better visualization