Download Grokking Machine Learning, video edition

What you would learn in Grokking Machine Learning, video edition course?

Do you think that machine learning is complicated to grasp? It's not! Check out this book! Serrano exposes some of the most cherished secrets of a machine learning society. Sebastian Theron, Founder, Udacity

Discover powerful machine learning methods you can apply and understand with just math from high school.

Learn more about Grokking Machine Learning. You will be taught:

Supervised algorithms to classify and separate data
Methods to clean and simplify data
Machine learning tools and packages
Neural networks and Ensemble methods for extensive data

Grokking Machine Learning will teach students how to implement machine learning to your project using basic Python codes and school math. It doesn't require any expertise to complete the hands-on activities with Python and easily accessible machine learning software. With easy-to-follow Python-based activities and mini-projects guides you towards becoming a machine-learning specialist.

About the technology

Discover the most effective machine learning techniques you can apply and understand with only math from high school! Put the term machine learning refers to a collection of methods for data analysis that is based on algorithms that produce superior results when you provide them with more information. ML is the basis of many advanced technologies, including recommendation systems, facial recognition software, smart speakers, and even self-driving vehicles. This book explains the fundamental concepts of machine learning by providing a range of relatable examples with engaging exercises and clear illustrations.

About the book

Grokking Machine Learning introduces machine learning algorithms and techniques in a manner everyone can comprehend. This book steers clear of the confusing academic jargon and provides simple explanations based on the basics of algebra. As you learn, you'll develop exciting projects using Python and algorithms for spam detection and image recognition. Additionally, you'll learn practical techniques for cleaning and preparing data.

about the audience

There is no need for machine learning expertise. However, basic Python is required.

Course Content:

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 suitable 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. The 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 valuable 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. The 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 an excellent 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 solid 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 learners
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

Download Grokking Machine Learning, video edition from below links NOW!

Write your comment!

Grokking Machine Learning, video edition.rar (Size: 1.3 GB - Date: 8/6/2022 11:17:38 AM)

Note
Download speed is limited, for download with higher speed (2X) please register on the site and for download with MAXIMUM speed please join to our VIP plans.