kingers Posted May 8 Report Share Posted May 8 Download Free Download : Oreilly - Grokking Machine Learning, video editionmp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz Genre:eLearning | Language: English | Size:2.13 GBFiles Included :001 Chapter 1 What is machine learning It is common sense, except done by a computer.mp4 (33.69 MB)MP4002 Chapter 1 What is machine learning.mp4 (24.3 MB)MP4003 Chapter 1 Some examples of models that humans use.mp4 (16.29 MB)MP4004 Chapter 1 Example 4 More.mp4 (13.1 MB)MP4005 Chapter 2 Types of machine learning.mp4 (21.11 MB)MP4006 Chapter 2 Supervised learning The branch of machine learning that works with labeled data.mp4 (30.28 MB)MP4007 Chapter 2 Unsupervised learning The branch of machine learning that works with unlabeled data.mp4 (22.15 MB)MP4008 Chapter 2 Dimensionality reduction simplifies data without losing too much information.mp4 (23.26 MB)MP4009 Chapter 2 What is reinforcement learning.mp4 (17.35 MB)MP4010 Chapter 3 Drawing a line close to our points Linear regression.mp4 (19.14 MB)MP4011 Chapter 3 The remember step Looking at the prices of existing houses.mp4 (24.57 MB)MP4012 Chapter 3 Some questions that arise and some quick answers.mp4 (18.2 MB)MP4013 Chapter 3 Crash course on slope and y-intercept.mp4 (22.39 MB)MP4014 Chapter 3 Simple trick.mp4 (22.07 MB)MP4015 Chapter 3 The linear regression algorithm Repeating the absolute or square trick many times to move the line closer to the points.mp4 (20.14 MB)MP4016 Chapter 3 How do we measure our results The error function.mp4 (21.21 MB)MP4017 Chapter 3 Gradient descent How to decrease an error function by slowly descending from a mountain.mp4 (28.53 MB)MP4018 Chapter 3 Real-life application Using Turi Create to predict housing prices in India.mp4 (23.28 MB)MP4019 Chapter 3 Parameters and hyperparameters.mp4 (21.53 MB)MP4020 Chapter 4 Optimizing the training process Underfitting, overfitting, testing, and regularization.mp4 (34.94 MB)MP4021 Chapter 4 How do we get the computer to pick the right model By testing.mp4 (30.4 MB)MP4022 Chapter 4 A numerical way to decide how complex our model should be The model complexity graph.mp4 (27.39 MB)MP4023 Chapter 4 Another example of overfitting Movie recommendations.mp4 (23.19 MB)MP4024 Chapter 4 Modifying the error function to solve our problem Lasso regression and ridge regression.mp4 (25.37 MB)MP4025 Chapter 4 An intuitive way to see regularization.mp4 (13.54 MB)MP4026 Chapter 4 Polynomial regression, testing, and regularization with Turi Create.mp4 (15.92 MB)MP4027 Chapter 4 Polynomial regression, testing, and regularization with Turi Create The testing RMSE for the models follow.mp4 (20.12 MB)MP4028 Chapter 5 Using lines to split our points The perceptron algorithm.mp4 (31.39 MB)MP4029 Chapter 5 The problem We are on an alien planet, and we don't know their language!.mp4 (25.01 MB)MP4030 Chapter 5 Sentiment analysis classifier.mp4 (22.01 MB)MP4031 Chapter 5 The step function and activation functions A condensed way to get predictions.mp4 (21.6 MB)MP4032 Chapter 5 The bias, the y-intercept, and the inherent mood of a quiet alien.mp4 (26.38 MB)MP4033 Chapter 5 Error function 3 Score.mp4 (19.47 MB)MP4034 Chapter 5 Pseudocode for the perceptron trick (geometric).mp4 (22.03 MB)MP4035 Chapter 5 Bad classifier.mp4 (22.35 MB)MP4036 Chapter 5 Pseudocode for the perceptron algorithm.mp4 (29.39 MB)MP4037 Chapter 5 Coding the perceptron algorithm using Turi Create.mp4 (26.92 MB)MP4038 Chapter 6 A continuous approach to splitting points Logistic classifiers.mp4 (30.87 MB)MP4039 Chapter 6 The dataset and the predictions.mp4 (16.21 MB)MP4040 Chapter 6 Error function 3 log loss.mp4 (25.2 MB)MP4041 Chapter 6 Formula for the log loss.mp4 (30.55 MB)MP4042 Chapter 6 Pseudocode for the logistic trick.mp4 (19.5 MB)MP4043 Chapter 6 Coding the logistic regression algorithm.mp4 (21.57 MB)MP4044 Chapter 6 Classifying into multiple classes The softmax function.mp4 (22.94 MB)MP4045 Chapter 7 How do you measure classification models Accuracy and its friends.mp4 (26.06 MB)MP4047 Chapter 7 Recall Among the positive examples, how many did we correctly classify.mp4 (28.31 MB)MP4048 Chapter 7 Combining recall and precision as a way to optimize both The F-score.mp4 (26.53 MB)MP4049 Chapter 7 A useful tool to evaluate our model The receiver operating characteristic (ROC) curve.mp4 (16.34 MB)MP4050 Chapter 7 The receiver operating characteristic (ROC) curve A way to optimize sensitivity and specificity in a model.mp4 (20.25 MB)MP4051 Chapter 7 A metric that tells us how good our model is The AUC (area under the curve).mp4 (20.18 MB)MP4052 Chapter 7 Recall is sensitivity, but precision and specificity are different.mp4 (14.65 MB)MP4053 Chapter 7 Summary.mp4 (18.67 MB)MP4054 Chapter 8 Using probability to its maximum The naive Bayes model.mp4 (21.93 MB)MP4055 Chapter 8 Sick or healthy A story with Bayes' theorem as the hero Let's calculate this probability.mp4 (16.97 MB)MP4056 Chapter 8 Prelude to Bayes' theorem The prior, the event, and the posterior.mp4 (22.7 MB)MP4057 Chapter 8 What the math just happened Turning ratios into probabilities.mp4 (19.53 MB)MP4058 Chapter 8 What the math just happened Turning ratios into probabilitiesProduct rule of probabilities.mp4 (8.47 MB)MP4059 Chapter 8 What about two words The naive Bayes algorithm.mp4 (32.54 MB)MP4060 Chapter 8 What about more than two words.mp4 (12.73 MB)MP4061 Chapter 8 Implementing the naive Bayes algorithm.mp4 (16.52 MB)MP4062 Chapter 9 Splitting data by asking questions Decision trees.mp4 (22.41 MB)MP4063 Chapter 9 Picking a good first question.mp4 (27.36 MB)MP4064 Chapter 9 The solution Building an app-recommendation system.mp4 (16.07 MB)MP4065 Chapter 9 Gini impurity index How diverse is my dataset.mp4 (14.18 MB)MP4066 Chapter 9 Entropy Another measure of diversity with strong applications in information theory.mp4 (20.82 MB)MP4067 Chapter 9 Classes of different sizes No problem We can take weighted averages.mp4 (26.38 MB)MP4068 Chapter 9 Beyond questions like yesno.mp4 (17.87 MB)MP4069 Chapter 9 The graphical boundary of decision trees.mp4 (17.93 MB)MP4070 Chapter 9 Setting hyperparameters in Scikit-Learn.mp4 (29.43 MB)MP4071 Chapter 9 Applications.mp4 (17.57 MB)MP4072 Chapter 10 Combining building blocks to gain more power Neural networks.mp4 (25.95 MB)MP4073 Chapter 10 Why two lines Is happiness not linear.mp4 (24 MB)MP4074 Chapter 10 The boundary of a neural network.mp4 (26.12 MB)MP4075 Chapter 10 Potential problems From overfitting to vanishing gradients.mp4 (27.65 MB)MP4076 Chapter 10 Neural networks with more than one output The softmax function.mp4 (21.24 MB)MP4077 Chapter 10 Training the model.mp4 (22.22 MB)MP4078 Chapter 10 Other architectures for more complex datasets.mp4 (20.16 MB)MP4079 Chapter 10 How neural networks paint paintings Generative adversarial networks (GAN).mp4 (24.66 MB)MP4080 Chapter 11 Finding boundaries with style Support vector machines and the kernel method.mp4 (24.86 MB)MP4081 Chapter 11 Distance error function Trying to separate our two lines as far apart as possible.mp4 (21.68 MB)MP4082 Chapter 11 Training SVMs with nonlinear boundaries The kernel method.mp4 (23.62 MB)MP4083 Chapter 11 Going beyond quadratic equations The polynomial kernel.mp4 (27.9 MB)MP4084 Chapter 11 A measure of how close points are Similarity.mp4 (23.49 MB)MP4085 Chapter 11 Overfitting and underfitting with the RBF kernel The gamma parameter.mp4 (22.23 MB)MP4086 Chapter 12 Combining models to maximize results Ensemble learning.mp4 (26.51 MB)MP4087 Chapter 12 Fitting a random forest manually.mp4 (21.17 MB)MP4088 Chapter 12 Combining the weak learners into a strong learner.mp4 (21.33 MB)MP4089 Chapter 12 Gradient boosting Using decision trees to build strong learners.mp4 (22.65 MB)MP4090 Chapter 12 XGBoost similarity score A new and effective way to measure similarity in a set.mp4 (15.3 MB)MP4091 Chapter 12 Building the weak learners Split at 25.mp4 (13.32 MB)MP4092 Chapter 12 Tree pruning A way to reduce overfitting by simplifying the weak learners.mp4 (24.39 MB)MP4093 Chapter 13 Putting it all in practice A real-life example of data engineering and machine learning.mp4 (29.3 MB)MP4094 Chapter 13 Using Pandas to study our dataset.mp4 (21.04 MB)MP4095 Chapter 13 Turning categorical data into numerical data One-hot encoding.mp4 (29.01 MB)MP4096 Chapter 13 Feature selection Getting rid of unnecessary features.mp4 (23.54 MB)MP4097 Chapter 13 Testing each model's accuracy.mp4 (18.94 MB)MP4098 Chapter 13 Tuning the hyperparameters to find the best model Grid search.mp4 (20.26 MB)MP4 https://fikper.com/s63ivui3c3/Oreilly.-.Grokking.Machine.Learning.video.edition.part1.rar.htmlhttps://fikper.com/qifSJ0k1Hk/Oreilly.-.Grokking.Machine.Learning.video.edition.part2.rar.html https://rapidgator.net/file/5e1d3804f4b3bb80fa749ffdbd0ff14f/Oreilly.-.Grokking.Machine.Learning.video.edition.part1.rarhttps://rapidgator.net/file/27fc30d1d80930f3abf9b0f657d8fa5b/Oreilly.-.Grokking.Machine.Learning.video.edition.part2.rar https://katfile.com/89wjzpcxf28g/Oreilly.-.Grokking.Machine.Learning.video.edition.part1.rarhttps://katfile.com/jfytsvvrdoyc/Oreilly.-.Grokking.Machine.Learning.video.edition.part2.rar Free search engine download: Oreilly - Grokking Machine Learning, video edition Link to comment Share on other sites More sharing options...
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