Yesterday, 01:30 PM
![[Image: 541638588_oip.jpg]](https://img100.pixhost.to/images/791/541638588_oip.jpg)
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
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
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