Download Free Download : Oreilly - Grokking Machine Learning, video edition
mp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:2.13 GB
Files Included :
001 Chapter 1 What is machine learning It is common sense, except done by a computer.mp4 (33.69 MB)
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002 Chapter 1 What is machine learning.mp4 (24.3 MB)
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003 Chapter 1 Some examples of models that humans use.mp4 (16.29 MB)
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004 Chapter 1 Example 4 More.mp4 (13.1 MB)
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005 Chapter 2 Types of machine learning.mp4 (21.11 MB)
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006 Chapter 2 Supervised learning The branch of machine learning that works with labeled data.mp4 (30.28 MB)
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007 Chapter 2 Unsupervised learning The branch of machine learning that works with unlabeled data.mp4 (22.15 MB)
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008 Chapter 2 Dimensionality reduction simplifies data without losing too much information.mp4 (23.26 MB)
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009 Chapter 2 What is reinforcement learning.mp4 (17.35 MB)
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010 Chapter 3 Drawing a line close to our points Linear regression.mp4 (19.14 MB)
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011 Chapter 3 The remember step Looking at the prices of existing houses.mp4 (24.57 MB)
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012 Chapter 3 Some questions that arise and some quick answers.mp4 (18.2 MB)
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013 Chapter 3 Crash course on slope and y-intercept.mp4 (22.39 MB)
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014 Chapter 3 Simple trick.mp4 (22.07 MB)
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015 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)
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016 Chapter 3 How do we measure our results The error function.mp4 (21.21 MB)
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017 Chapter 3 Gradient descent How to decrease an error function by slowly descending from a mountain.mp4 (28.53 MB)
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018 Chapter 3 Real-life application Using Turi Create to predict housing prices in India.mp4 (23.28 MB)
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019 Chapter 3 Parameters and hyperparameters.mp4 (21.53 MB)
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020 Chapter 4 Optimizing the training process Underfitting, overfitting, testing, and regularization.mp4 (34.94 MB)
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021 Chapter 4 How do we get the computer to pick the right model By testing.mp4 (30.4 MB)
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022 Chapter 4 A numerical way to decide how complex our model should be The model complexity graph.mp4 (27.39 MB)
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023 Chapter 4 Another example of overfitting Movie recommendations.mp4 (23.19 MB)
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024 Chapter 4 Modifying the error function to solve our problem Lasso regression and ridge regression.mp4 (25.37 MB)
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025 Chapter 4 An intuitive way to see regularization.mp4 (13.54 MB)
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026 Chapter 4 Polynomial regression, testing, and regularization with Turi Create.mp4 (15.92 MB)
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027 Chapter 4 Polynomial regression, testing, and regularization with Turi Create The testing RMSE for the models follow.mp4 (20.12 MB)
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028 Chapter 5 Using lines to split our points The perceptron algorithm.mp4 (31.39 MB)
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029 Chapter 5 The problem We are on an alien planet, and we don't know their language!.mp4 (25.01 MB)
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030 Chapter 5 Sentiment analysis classifier.mp4 (22.01 MB)
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031 Chapter 5 The step function and activation functions A condensed way to get predictions.mp4 (21.6 MB)
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032 Chapter 5 The bias, the y-intercept, and the inherent mood of a quiet alien.mp4 (26.38 MB)
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033 Chapter 5 Error function 3 Score.mp4 (19.47 MB)
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034 Chapter 5 Pseudocode for the perceptron trick (geometric).mp4 (22.03 MB)
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035 Chapter 5 Bad classifier.mp4 (22.35 MB)
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036 Chapter 5 Pseudocode for the perceptron algorithm.mp4 (29.39 MB)
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037 Chapter 5 Coding the perceptron algorithm using Turi Create.mp4 (26.92 MB)
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038 Chapter 6 A continuous approach to splitting points Logistic classifiers.mp4 (30.87 MB)
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039 Chapter 6 The dataset and the predictions.mp4 (16.21 MB)
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040 Chapter 6 Error function 3 log loss.mp4 (25.2 MB)
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041 Chapter 6 Formula for the log loss.mp4 (30.55 MB)
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042 Chapter 6 Pseudocode for the logistic trick.mp4 (19.5 MB)
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043 Chapter 6 Coding the logistic regression algorithm.mp4 (21.57 MB)
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044 Chapter 6 Classifying into multiple classes The softmax function.mp4 (22.94 MB)
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045 Chapter 7 How do you measure classification models Accuracy and its friends.mp4 (26.06 MB)
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047 Chapter 7 Recall Among the positive examples, how many did we correctly classify.mp4 (28.31 MB)
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048 Chapter 7 Combining recall and precision as a way to optimize both The F-score.mp4 (26.53 MB)
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049 Chapter 7 A useful tool to evaluate our model The receiver operating characteristic (ROC) curve.mp4 (16.34 MB)
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050 Chapter 7 The receiver operating characteristic (ROC) curve A way to optimize sensitivity and specificity in a model.mp4 (20.25 MB)
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051 Chapter 7 A metric that tells us how good our model is The AUC (area under the curve).mp4 (20.18 MB)
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052 Chapter 7 Recall is sensitivity, but precision and specificity are different.mp4 (14.65 MB)
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053 Chapter 7 Summary.mp4 (18.67 MB)
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054 Chapter 8 Using probability to its maximum The naive Bayes model.mp4 (21.93 MB)
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055 Chapter 8 Sick or healthy A story with Bayes' theorem as the hero Let's calculate this probability.mp4 (16.97 MB)
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056 Chapter 8 Prelude to Bayes' theorem The prior, the event, and the posterior.mp4 (22.7 MB)
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057 Chapter 8 What the math just happened Turning ratios into probabilities.mp4 (19.53 MB)
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058 Chapter 8 What the math just happened Turning ratios into probabilitiesProduct rule of probabilities.mp4 (8.47 MB)
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059 Chapter 8 What about two words The naive Bayes algorithm.mp4 (32.54 MB)
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060 Chapter 8 What about more than two words.mp4 (12.73 MB)
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061 Chapter 8 Implementing the naive Bayes algorithm.mp4 (16.52 MB)
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062 Chapter 9 Splitting data by asking questions Decision trees.mp4 (22.41 MB)
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063 Chapter 9 Picking a good first question.mp4 (27.36 MB)
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064 Chapter 9 The solution Building an app-recommendation system.mp4 (16.07 MB)
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065 Chapter 9 Gini impurity index How diverse is my dataset.mp4 (14.18 MB)
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066 Chapter 9 Entropy Another measure of diversity with strong applications in information theory.mp4 (20.82 MB)
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067 Chapter 9 Classes of different sizes No problem We can take weighted averages.mp4 (26.38 MB)
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068 Chapter 9 Beyond questions like yesno.mp4 (17.87 MB)
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069 Chapter 9 The graphical boundary of decision trees.mp4 (17.93 MB)
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070 Chapter 9 Setting hyperparameters in Scikit-Learn.mp4 (29.43 MB)
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071 Chapter 9 Applications.mp4 (17.57 MB)
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072 Chapter 10 Combining building blocks to gain more power Neural networks.mp4 (25.95 MB)
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073 Chapter 10 Why two lines Is happiness not linear.mp4 (24 MB)
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074 Chapter 10 The boundary of a neural network.mp4 (26.12 MB)
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075 Chapter 10 Potential problems From overfitting to vanishing gradients.mp4 (27.65 MB)
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076 Chapter 10 Neural networks with more than one output The softmax function.mp4 (21.24 MB)
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077 Chapter 10 Training the model.mp4 (22.22 MB)
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078 Chapter 10 Other architectures for more complex datasets.mp4 (20.16 MB)
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079 Chapter 10 How neural networks paint paintings Generative adversarial networks (GAN).mp4 (24.66 MB)
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080 Chapter 11 Finding boundaries with style Support vector machines and the kernel method.mp4 (24.86 MB)
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081 Chapter 11 Distance error function Trying to separate our two lines as far apart as possible.mp4 (21.68 MB)
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082 Chapter 11 Training SVMs with nonlinear boundaries The kernel method.mp4 (23.62 MB)
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083 Chapter 11 Going beyond quadratic equations The polynomial kernel.mp4 (27.9 MB)
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084 Chapter 11 A measure of how close points are Similarity.mp4 (23.49 MB)
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085 Chapter 11 Overfitting and underfitting with the RBF kernel The gamma parameter.mp4 (22.23 MB)
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086 Chapter 12 Combining models to maximize results Ensemble learning.mp4 (26.51 MB)
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087 Chapter 12 Fitting a random forest manually.mp4 (21.17 MB)
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088 Chapter 12 Combining the weak learners into a strong learner.mp4 (21.33 MB)
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089 Chapter 12 Gradient boosting Using decision trees to build strong learners.mp4 (22.65 MB)
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090 Chapter 12 XGBoost similarity score A new and effective way to measure similarity in a set.mp4 (15.3 MB)
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091 Chapter 12 Building the weak learners Split at 25.mp4 (13.32 MB)
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092 Chapter 12 Tree pruning A way to reduce overfitting by simplifying the weak learners.mp4 (24.39 MB)
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093 Chapter 13 Putting it all in practice A real-life example of data engineering and machine learning.mp4 (29.3 MB)
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094 Chapter 13 Using Pandas to study our dataset.mp4 (21.04 MB)
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095 Chapter 13 Turning categorical data into numerical data One-hot encoding.mp4 (29.01 MB)
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096 Chapter 13 Feature selection Getting rid of unnecessary features.mp4 (23.54 MB)
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097 Chapter 13 Testing each model's accuracy.mp4 (18.94 MB)
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098 Chapter 13 Tuning the hyperparameters to find the best model Grid search.mp4 (20.26 MB)
MP4
MP4
002 Chapter 1 What is machine learning.mp4 (24.3 MB)
MP4
003 Chapter 1 Some examples of models that humans use.mp4 (16.29 MB)
MP4
004 Chapter 1 Example 4 More.mp4 (13.1 MB)
MP4
005 Chapter 2 Types of machine learning.mp4 (21.11 MB)
MP4
006 Chapter 2 Supervised learning The branch of machine learning that works with labeled data.mp4 (30.28 MB)
MP4
007 Chapter 2 Unsupervised learning The branch of machine learning that works with unlabeled data.mp4 (22.15 MB)
MP4
008 Chapter 2 Dimensionality reduction simplifies data without losing too much information.mp4 (23.26 MB)
MP4
009 Chapter 2 What is reinforcement learning.mp4 (17.35 MB)
MP4
010 Chapter 3 Drawing a line close to our points Linear regression.mp4 (19.14 MB)
MP4
011 Chapter 3 The remember step Looking at the prices of existing houses.mp4 (24.57 MB)
MP4
012 Chapter 3 Some questions that arise and some quick answers.mp4 (18.2 MB)
MP4
013 Chapter 3 Crash course on slope and y-intercept.mp4 (22.39 MB)
MP4
014 Chapter 3 Simple trick.mp4 (22.07 MB)
MP4
015 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)
MP4
016 Chapter 3 How do we measure our results The error function.mp4 (21.21 MB)
MP4
017 Chapter 3 Gradient descent How to decrease an error function by slowly descending from a mountain.mp4 (28.53 MB)
MP4
018 Chapter 3 Real-life application Using Turi Create to predict housing prices in India.mp4 (23.28 MB)
MP4
019 Chapter 3 Parameters and hyperparameters.mp4 (21.53 MB)
MP4
020 Chapter 4 Optimizing the training process Underfitting, overfitting, testing, and regularization.mp4 (34.94 MB)
MP4
021 Chapter 4 How do we get the computer to pick the right model By testing.mp4 (30.4 MB)
MP4
022 Chapter 4 A numerical way to decide how complex our model should be The model complexity graph.mp4 (27.39 MB)
MP4
023 Chapter 4 Another example of overfitting Movie recommendations.mp4 (23.19 MB)
MP4
024 Chapter 4 Modifying the error function to solve our problem Lasso regression and ridge regression.mp4 (25.37 MB)
MP4
025 Chapter 4 An intuitive way to see regularization.mp4 (13.54 MB)
MP4
026 Chapter 4 Polynomial regression, testing, and regularization with Turi Create.mp4 (15.92 MB)
MP4
027 Chapter 4 Polynomial regression, testing, and regularization with Turi Create The testing RMSE for the models follow.mp4 (20.12 MB)
MP4
028 Chapter 5 Using lines to split our points The perceptron algorithm.mp4 (31.39 MB)
MP4
029 Chapter 5 The problem We are on an alien planet, and we don't know their language!.mp4 (25.01 MB)
MP4
030 Chapter 5 Sentiment analysis classifier.mp4 (22.01 MB)
MP4
031 Chapter 5 The step function and activation functions A condensed way to get predictions.mp4 (21.6 MB)
MP4
032 Chapter 5 The bias, the y-intercept, and the inherent mood of a quiet alien.mp4 (26.38 MB)
MP4
033 Chapter 5 Error function 3 Score.mp4 (19.47 MB)
MP4
034 Chapter 5 Pseudocode for the perceptron trick (geometric).mp4 (22.03 MB)
MP4
035 Chapter 5 Bad classifier.mp4 (22.35 MB)
MP4
036 Chapter 5 Pseudocode for the perceptron algorithm.mp4 (29.39 MB)
MP4
037 Chapter 5 Coding the perceptron algorithm using Turi Create.mp4 (26.92 MB)
MP4
038 Chapter 6 A continuous approach to splitting points Logistic classifiers.mp4 (30.87 MB)
MP4
039 Chapter 6 The dataset and the predictions.mp4 (16.21 MB)
MP4
040 Chapter 6 Error function 3 log loss.mp4 (25.2 MB)
MP4
041 Chapter 6 Formula for the log loss.mp4 (30.55 MB)
MP4
042 Chapter 6 Pseudocode for the logistic trick.mp4 (19.5 MB)
MP4
043 Chapter 6 Coding the logistic regression algorithm.mp4 (21.57 MB)
MP4
044 Chapter 6 Classifying into multiple classes The softmax function.mp4 (22.94 MB)
MP4
045 Chapter 7 How do you measure classification models Accuracy and its friends.mp4 (26.06 MB)
MP4
047 Chapter 7 Recall Among the positive examples, how many did we correctly classify.mp4 (28.31 MB)
MP4
048 Chapter 7 Combining recall and precision as a way to optimize both The F-score.mp4 (26.53 MB)
MP4
049 Chapter 7 A useful tool to evaluate our model The receiver operating characteristic (ROC) curve.mp4 (16.34 MB)
MP4
050 Chapter 7 The receiver operating characteristic (ROC) curve A way to optimize sensitivity and specificity in a model.mp4 (20.25 MB)
MP4
051 Chapter 7 A metric that tells us how good our model is The AUC (area under the curve).mp4 (20.18 MB)
MP4
052 Chapter 7 Recall is sensitivity, but precision and specificity are different.mp4 (14.65 MB)
MP4
053 Chapter 7 Summary.mp4 (18.67 MB)
MP4
054 Chapter 8 Using probability to its maximum The naive Bayes model.mp4 (21.93 MB)
MP4
055 Chapter 8 Sick or healthy A story with Bayes' theorem as the hero Let's calculate this probability.mp4 (16.97 MB)
MP4
056 Chapter 8 Prelude to Bayes' theorem The prior, the event, and the posterior.mp4 (22.7 MB)
MP4
057 Chapter 8 What the math just happened Turning ratios into probabilities.mp4 (19.53 MB)
MP4
058 Chapter 8 What the math just happened Turning ratios into probabilitiesProduct rule of probabilities.mp4 (8.47 MB)
MP4
059 Chapter 8 What about two words The naive Bayes algorithm.mp4 (32.54 MB)
MP4
060 Chapter 8 What about more than two words.mp4 (12.73 MB)
MP4
061 Chapter 8 Implementing the naive Bayes algorithm.mp4 (16.52 MB)
MP4
062 Chapter 9 Splitting data by asking questions Decision trees.mp4 (22.41 MB)
MP4
063 Chapter 9 Picking a good first question.mp4 (27.36 MB)
MP4
064 Chapter 9 The solution Building an app-recommendation system.mp4 (16.07 MB)
MP4
065 Chapter 9 Gini impurity index How diverse is my dataset.mp4 (14.18 MB)
MP4
066 Chapter 9 Entropy Another measure of diversity with strong applications in information theory.mp4 (20.82 MB)
MP4
067 Chapter 9 Classes of different sizes No problem We can take weighted averages.mp4 (26.38 MB)
MP4
068 Chapter 9 Beyond questions like yesno.mp4 (17.87 MB)
MP4
069 Chapter 9 The graphical boundary of decision trees.mp4 (17.93 MB)
MP4
070 Chapter 9 Setting hyperparameters in Scikit-Learn.mp4 (29.43 MB)
MP4
071 Chapter 9 Applications.mp4 (17.57 MB)
MP4
072 Chapter 10 Combining building blocks to gain more power Neural networks.mp4 (25.95 MB)
MP4
073 Chapter 10 Why two lines Is happiness not linear.mp4 (24 MB)
MP4
074 Chapter 10 The boundary of a neural network.mp4 (26.12 MB)
MP4
075 Chapter 10 Potential problems From overfitting to vanishing gradients.mp4 (27.65 MB)
MP4
076 Chapter 10 Neural networks with more than one output The softmax function.mp4 (21.24 MB)
MP4
077 Chapter 10 Training the model.mp4 (22.22 MB)
MP4
078 Chapter 10 Other architectures for more complex datasets.mp4 (20.16 MB)
MP4
079 Chapter 10 How neural networks paint paintings Generative adversarial networks (GAN).mp4 (24.66 MB)
MP4
080 Chapter 11 Finding boundaries with style Support vector machines and the kernel method.mp4 (24.86 MB)
MP4
081 Chapter 11 Distance error function Trying to separate our two lines as far apart as possible.mp4 (21.68 MB)
MP4
082 Chapter 11 Training SVMs with nonlinear boundaries The kernel method.mp4 (23.62 MB)
MP4
083 Chapter 11 Going beyond quadratic equations The polynomial kernel.mp4 (27.9 MB)
MP4
084 Chapter 11 A measure of how close points are Similarity.mp4 (23.49 MB)
MP4
085 Chapter 11 Overfitting and underfitting with the RBF kernel The gamma parameter.mp4 (22.23 MB)
MP4
086 Chapter 12 Combining models to maximize results Ensemble learning.mp4 (26.51 MB)
MP4
087 Chapter 12 Fitting a random forest manually.mp4 (21.17 MB)
MP4
088 Chapter 12 Combining the weak learners into a strong learner.mp4 (21.33 MB)
MP4
089 Chapter 12 Gradient boosting Using decision trees to build strong learners.mp4 (22.65 MB)
MP4
090 Chapter 12 XGBoost similarity score A new and effective way to measure similarity in a set.mp4 (15.3 MB)
MP4
091 Chapter 12 Building the weak learners Split at 25.mp4 (13.32 MB)
MP4
092 Chapter 12 Tree pruning A way to reduce overfitting by simplifying the weak learners.mp4 (24.39 MB)
MP4
093 Chapter 13 Putting it all in practice A real-life example of data engineering and machine learning.mp4 (29.3 MB)
MP4
094 Chapter 13 Using Pandas to study our dataset.mp4 (21.04 MB)
MP4
095 Chapter 13 Turning categorical data into numerical data One-hot encoding.mp4 (29.01 MB)
MP4
096 Chapter 13 Feature selection Getting rid of unnecessary features.mp4 (23.54 MB)
MP4
097 Chapter 13 Testing each model's accuracy.mp4 (18.94 MB)
MP4
098 Chapter 13 Tuning the hyperparameters to find the best model Grid search.mp4 (20.26 MB)
MP4
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