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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