2.19 GB | 00:11:11 | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
001  Chapter 1  What is machine learning It is common sense, except done by a computer.mp4 (33.69 MB)
002  Chapter 1  What is machine learning It is common sense, except done by a computer.mp4 (33.69 MB)
003  Chapter 1  What is machine learning.mp4 (24.3 MB)
004  Chapter 1  Some examples of models that humans use.mp4 (16.29 MB)
005  Chapter 1  Example 4 More.mp4 (13.1 MB)
006  Chapter 2  Types of machine learning.mp4 (21.11 MB)
007  Chapter 2  Supervised learning The branch of machine learning that works with labeled data.mp4 (30.28 MB)
008  Chapter 2  Unsupervised learning The branch of machine learning that works with unlabeled data.mp4 (22.15 MB)
009  Chapter 2  Dimensionality reduction simplifies data without losing too much information.mp4 (23.26 MB)
010  Chapter 2  What is reinforcement learning.mp4 (17.35 MB)
011  Chapter 3  Drawing a line close to our points Linear regression.mp4 (19.14 MB)
012  Chapter 3  The remember step Looking at the prices of existing houses.mp4 (24.57 MB)
013  Chapter 3  Some questions that arise and some quick answers.mp4 (18.2 MB)
014  Chapter 3  Crash course on slope and y-intercept.mp4 (22.39 MB)
015  Chapter 3  Simple trick.mp4 (22.07 MB)
016  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)
017  Chapter 3  How do we measure our results The error function.mp4 (21.21 MB)
018  Chapter 3  Gradient descent How to decrease an error function by slowly descending from a mountain.mp4 (28.53 MB)
019  Chapter 3  Real-life application Using Turi Create to predict housing prices in India.mp4 (23.28 MB)
020  Chapter 3  Parameters and hyperparameters.mp4 (21.53 MB)
021  Chapter 4  Optimizing the training process Underfitting, overfitting, testing, and regularization.mp4 (34.94 MB)
022  Chapter 4  How do we get the computer to pick the right model By testing.mp4 (30.4 MB)
023  Chapter 4  A numerical way to decide how complex our model should be The model complexity graph.mp4 (27.39 MB)
024  Chapter 4  Another example of overfitting Movie recommendations.mp4 (23.19 MB)
025  Chapter 4  Modifying the error function to solve our problem Lasso regression and ridge regression.mp4 (25.37 MB)
026  Chapter 4  An intuitive way to see regularization.mp4 (13.54 MB)
027  Chapter 4  Polynomial regression, testing, and regularization with Turi Create.mp4 (15.92 MB)
028  Chapter 4  Polynomial regression, testing, and regularization with Turi Create The testing RMSE for the models follow.mp4 (20.12 MB)
029  Chapter 5  Using lines to split our points The perceptron algorithm.mp4 (31.39 MB)
030  Chapter 5  The problem We are on an alien planet, and we don t know their language!.mp4 (25.01 MB)
031  Chapter 5  Sentiment analysis classifier.mp4 (22.01 MB)
032  Chapter 5  The step function and activation functions A condensed way to get predictions.mp4 (21.6 MB)
033  Chapter 5  The bias, the y-intercept, and the inherent mood of a quiet alien.mp4 (26.38 MB)
034  Chapter 5  Error function 3 Score.mp4 (19.47 MB)
035  Chapter 5  Pseudocode for the perceptron trick (geometric).mp4 (22.03 MB)
036  Chapter 5  Bad classifier.mp4 (22.35 MB)
037  Chapter 5  Pseudocode for the perceptron algorithm.mp4 (29.39 MB)
038  Chapter 5  Coding the perceptron algorithm using Turi Create.mp4 (26.92 MB)
039  Chapter 6  A continuous approach to splitting points Logistic classifiers.mp4 (30.87 MB)
040  Chapter 6  The dataset and the predictions.mp4 (16.21 MB)
041  Chapter 6  Error function 3 log loss.mp4 (25.2 MB)
042  Chapter 6  Formula for the log loss.mp4 (30.55 MB)
043  Chapter 6  Pseudocode for the logistic trick.mp4 (19.5 MB)
044  Chapter 6  Coding the logistic regression algorithm.mp4 (21.57 MB)
045  Chapter 6  Classifying into multiple classes The softmax function.mp4 (22.94 MB)
046  Chapter 7  How do you measure classification models Accuracy and its friends.mp4 (26.06 MB)
047  Chapter 7  False positives and false negatives Which one is worse.mp4 (28.44 MB)
048  Chapter 7  Recall Among the positive examples, how many did we correctly classify.mp4 (28.31 MB)
049  Chapter 7  Combining recall and precision as a way to optimize both The F-score.mp4 (26.53 MB)
050  Chapter 7  A useful tool to evaluate our model The receiver operating characteristic (ROC) curve.mp4 (16.34 MB)
051  Chapter 7  The receiver operating characteristic (ROC) curve A way to optimize sensitivity and specifiCity in a model.mp4 (20.25 MB)
052  Chapter 7  A metric that tells us how good our model is The AUC (area under the curve).mp4 (20.18 MB)
053  Chapter 7  Recall is sensitivity, but precision and specifiCity are different.mp4 (14.65 MB)
054  Chapter 7  Summary.mp4 (18.67 MB)
055  Chapter 8  Using probability to its maximum The naive Bayes model.mp4 (21.93 MB)
056  Chapter 8  Sick or healthy A story with Bayes  theorem as the hero Let s calculate this probability.mp4 (16.97 MB)
057  Chapter 8  Prelude to Bayes  theorem The prior, the event, and the posterior.mp4 (22.7 MB)
058  Chapter 8  What the math just happened Turning ratios into probabilities.mp4 (19.53 MB)
059  Chapter 8  What the math just happened Turning ratios into probabilitiesProduct rule of probabilities.mp4 (8.47 MB)
060  Chapter 8  What about two words The naive Bayes algorithm.mp4 (32.54 MB)
061  Chapter 8  What about more than two words.mp4 (12.73 MB)
062  Chapter 8  Implementing the naive Bayes algorithm.mp4 (16.52 MB)
063  Chapter 9  Splitting data by asking questions Decision trees.mp4 (22.41 MB)
064  Chapter 9  Picking a good first question.mp4 (27.36 MB)
065  Chapter 9  The solution Building an app-recommendation system.mp4 (16.07 MB)
066  Chapter 9  Gini impurity index How diverse is my dataset.mp4 (14.18 MB)
067  Chapter 9  Entropy Another measure of diversity with strong applications in information theory.mp4 (20.82 MB)
068  Chapter 9  Classes of different sizes No problem We can take weighted averages.mp4 (26.38 MB)
069  Chapter 9  Beyond questions like yesno.mp4 (17.87 MB)
070  Chapter 9  The graphical boundary of decision trees.mp4 (17.93 MB)
071  Chapter 9  Setting hyperparameters in Scikit-Learn.mp4 (29.43 MB)
072  Chapter 9  Applications.mp4 (17.57 MB)
073  Chapter 10  Combining building blocks to gain more power Neural networks.mp4 (25.95 MB)
074  Chapter 10  Why two lines Is happiness not linear.mp4 (24 MB)
075  Chapter 10  The boundary of a neural network.mp4 (26.12 MB)
076  Chapter 10  Potential problems From overfitting to vanishing gradients.mp4 (27.65 MB)
077  Chapter 10  Neural networks with more than one output The softmax function.mp4 (21.24 MB)
078  Chapter 10  Training the model.mp4 (22.22 MB)
079  Chapter 10  Other architectures for more complex datasets.mp4 (20.16 MB)
080  Chapter 10  How neural networks paint paintings Generative adversarial networks (GAN).mp4 (24.66 MB)
081  Chapter 11  Finding boundaries with style Support vector machines and the kernel method.mp4 (24.86 MB)
082  Chapter 11  Distance error function Trying to separate our two lines as far apart as possible.mp4 (21.68 MB)
083  Chapter 11  Training SVMs with nonlinear boundaries The kernel method.mp4 (23.62 MB)
084  Chapter 11  Going beyond quadratic equations The polynomial kernel.mp4 (27.9 MB)
085  Chapter 11  A measure of how close points are Similarity.mp4 (23.49 MB)
086  Chapter 11  Overfitting and underfitting with the RBF kernel The gamma parameter.mp4 (22.23 MB)
087  Chapter 12  Combining models to maximize results Ensemble learning.mp4 (26.51 MB)
088  Chapter 12  Fitting a random forest manually.mp4 (21.17 MB)
089  Chapter 12  Combining the weak learners into a strong learner.mp4 (21.33 MB)
090  Chapter 12  Gradient boosting Using decision trees to build strong learners.mp4 (22.65 MB)
091  Chapter 12  XGBoost similarity score A new and effective way to measure similarity in a set.mp4 (15.3 MB)
092  Chapter 12  Building the weak learners Split at 25.mp4 (13.32 MB)
093  Chapter 12  Tree pruning A way to reduce overfitting by simplifying the weak learners.mp4 (24.39 MB)
094  Chapter 13  Putting it all in practice A real-life example of data engineering and machine learning.mp4 (29.3 MB)
095  Chapter 13  Using Pandas to study our dataset.mp4 (21.04 MB)
096  Chapter 13  Turning categorical data into numerical data One-hot encoding.mp4 (29.01 MB)
097  Chapter 13  Feature selection Getting rid of unnecessary features.mp4 (23.54 MB)
098  Chapter 13  Testing each model s accuracy.mp4 (18.94 MB)
099  Chapter 13  Tuning the hyperparameters to find the best model Grid search.mp4 (20.26 MB)]
Screenshot

https://fikper.com/7RagUy7xN3/Oreilly.-.Grokking.Machine.Learning.video.edition.part1.rar.html
https://fikper.com/8rs6KVCND9/Oreilly.-.Grokking.Machine.Learning.video.edition.part2.rar.html

https://rapidgator.net/file/83b26a572328bda39e1b611e3ff0ba4e/Oreilly.-.Grokking.Machine.Learning.video.edition.part1.rar
https://rapidgator.net/file/981ab0c34b8be294e96bc9f8d75127ec/Oreilly.-.Grokking.Machine.Learning.video.edition.part2.rar

https://nitroflare.com/view/52A88E89725743A/Oreilly.-.Grokking.Machine.Learning.video.edition.part1.rar
https://nitroflare.com/view/1122DD0CED8F8BF/Oreilly.-.Grokking.Machine.Learning.video.edition.part2.rar

Подпись автора

www.prizrak.ws Аниме Форум. Софт, игры, фильмы, музыка, anime скачать бесплатно ^_^