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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)
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