Discover valuable machine learning techniques …
Discover valuable machine learning techniques you can understand and apply using just high-school math.
In Grokking Machine Learning you will learn:Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert.
Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations.
Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data.
No machine learning knowledge necessary, but basic Python required.
Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple.
The first step to take on your machine learning journey.Chapter 1. What is machine learning? It is common sense, except done by a computer
Chapter 1. What is machine learning?
Chapter 1. Some examples of models that humans use
Chapter 1. Example 4: More?
Chapter 2. Types of machine learning
Chapter 2. Supervised learning: The branch of machine learning that works with labeled data
Chapter 2. Unsupervised learning: The branch of machine learning that works with unlabeled data
Chapter 2. Dimensionality reduction simplifies data without losing too much information
Chapter 2. What is reinforcement learning?
Chapter 3. Drawing a line close to our points: Linear regression
Chapter 3. The remember step: Looking at the prices of existing houses
Chapter 3. Some questions that arise and some quick answers
Chapter 3. Crash course on slope and y-intercept
Chapter 3. Simple trick
Chapter 3. The linear regression algorithm: Repeating the absolute or square trick many times to move the line closer to the points
Chapter 3. How do we measure our results? The error function
Chapter 3. Gradient descent: How to decrease an error function by slowly descending from a mountain
Chapter 3. Real-life application: Using Turi Create to predict housing prices in India
Chapter 3. Parameters and hyperparameters
Chapter 4. Optimizing the training process: Underfitting, overfitting, testing, and regularization
Chapter 4. How do we get the computer to pick the right model? By testing
Chapter 4. A numerical way to decide how complex our model should be: The model complexity graph
Chapter 4. Another example of overfitting: Movie recommendations
Chapter 4. Modifying the error function to solve our problem: Lasso regression and ridge regression
Chapter 4. An intuitive way to see regularization
Chapter 4. Polynomial regression, testing, and regularization with Turi Create
Chapter 4. Polynomial regression, testing, and regularization with Turi Create :The testing RMSE for the models follow:
Chapter 5. Using lines to split our points: The perceptron algorithm
Chapter 5. The problem: We are on an alien planet, and we don’t know their language!
Chapter 5. Sentiment analysis classifier
Chapter 5. The step function and activation functions: A condensed way to get predictions
Chapter 5. The bias, the y-intercept, and the inherent mood of a quiet alien
Chapter 5. Error function 3: Score
Chapter 5. Pseudocode for the perceptron trick (geometric)
Chapter 5. Bad classifier
Chapter 5. Pseudocode for the perceptron algorithm
Chapter 5. Coding the perceptron algorithm using Turi Create
Chapter 6. A continuous approach to splitting points: Logistic classifiers
Chapter 6. The dataset and the predictions
Chapter 6. Error function 3: log loss
Chapter 6. Formula for the log loss
Chapter 6. Pseudocode for the logistic trick
Chapter 6. Coding the logistic regression algorithm
Chapter 6. Classifying into multiple classes: The softmax function
Chapter 7. How do you measure classification models? Accuracy and its friends
Chapter 7. False positives and false negatives: Which one is worse?
Chapter 7. Recall: Among the positive examples, how many did we correctly classify?
Chapter 7. Combining recall and precision as a way to optimize both: The F-score
Chapter 7. A useful tool to evaluate our model: The receiver operating characteristic (ROC) curve
Chapter 7. The receiver operating characteristic (ROC) curve: A way to optimize sensitivity and specificity in a model
Chapter 7. A metric that tells us how good our model is: The AUC (area under the curve)
Chapter 7. Recall is sensitivity, but precision and specificity are different
Chapter 7. Summary
Chapter 8. Using probability to its maximum: The naive Bayes model
Chapter 8. Sick or healthy? A story with Bayes’ theorem as the hero: Let’s calculate this probability.
Chapter 8. Prelude to Bayes’ theorem: The prior, the event, and the posterior
Chapter 8. What the math just happened? Turning ratios into probabilities
Chapter 8. What the math just happened? Turning ratios into probabilities:Product rule of probabilities
Chapter 8. What about two words? The naive Bayes algorithm
Chapter 8. What about more than two words?
Chapter 8. Implementing the naive Bayes algorithm
Chapter 9. Splitting data by asking questions: Decision trees
Chapter 9. Picking a good first question
Chapter 9. The solution: Building an app-recommendation system
Chapter 9. Gini impurity index: How diverse is my dataset?
Chapter 9. Entropy: Another measure of diversity with strong applications in information theory
Chapter 9. Classes of different sizes? No problem: We can take weighted averages
Chapter 9. Beyond questions like yes/no
Chapter 9. The graphical boundary of decision trees
Chapter 9. Setting hyperparameters in Scikit-Learn
Chapter 9. Applications
Chapter 10. Combining building blocks to gain more power: Neural networks
Chapter 10. Why two lines? Is happiness not linear?
Chapter 10. The boundary of a neural network
Chapter 10. Potential problems: From overfitting to vanishing gradients
Chapter 10. Neural networks with more than one output: The softmax function
Chapter 10. Training the model
Chapter 10. Other architectures for more complex datasets
Chapter 10. How neural networks paint paintings: Generative adversarial networks (GAN)
Chapter 11. Finding boundaries with style: Support vector machines and the kernel method
Chapter 11. Distance error function: Trying to separate our two lines as far apart as possible
Chapter 11. Training SVMs with nonlinear boundaries: The kernel method
Chapter 11. Going beyond quadratic equations: The polynomial kernel
Chapter 11. A measure of how close points are: Similarity
Chapter 11. Overfitting and underfitting with the RBF kernel: The gamma parameter
Chapter 12. Combining models to maximize results: Ensemble learning
Chapter 12. Fitting a random forest manually
Chapter 12. Combining the weak learners into a strong learner
Chapter 12. Gradient boosting: Using decision trees to build strong learners
Chapter 12. XGBoost similarity score: A new and effective way to measure similarity in a set
Chapter 12. Building the weak learners: Split at 25
Chapter 12. Tree pruning: A way to reduce overfitting by simplifying the weak learners
Chapter 13. Putting it all in practice: A real-life example of data engineering and machine learning
Chapter 13. Using Pandas to study our dataset
Chapter 13. Turning categorical data into numerical data: One-hot encoding
Chapter 13. Feature selection: Getting rid of unnecessary features
Chapter 13. Testing each model’s accuracy
Chapter 13. Tuning the hyperparameters to find the best model: Grid search