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Jason brownlee machine learning mini course notes and examples are gathered through subscribed emails from https://machinelearningmastery.com

1. Applied Machine Learning With Weka
2. XGBoost With Python Mini-Course
3. Deep Learning With Python Mini-Course

1. Be a machine learning engineer
2. Better results by structuring your problem
3. Catalog of machine learning algorithms
4. Combine predictions with ensemble methods
5. Deep learning for sequence prediction
6. Kick your math envy
7. Machine learning has a trap
8. Machine learning without a single line of code
9. Nonlinear algorithms for when you need performance
10. Practical machine learning problems
11. Related fields of study
12. Standard machine learning terms
13. Start with simple linear algorithms
14. Visualize your data with Pandas
15. What is deep learning?

1. How To Talk About Data in Machine Learning
2. The Principle That Underpins All Algorithms
3. Parametric and Nonparametric Algorithms
4. Bias, Variance and the Trade-off
5. Linear Regression Algorithm
6. Logistic Regression Algorithm
7. Linear Discriminant Analysis Algorithm
8. Classification and Regression Trees
9. Naive Bayes Algorithm
10. K-Nearest Neighbors Algorithm
11. Learning Vector Quantization
12. Support Vector Machines
13. Bagging and Random Forest
14. Boosting and AdaBoost

1. 4 Prediction Models and 3 Types of Gradient Descent
2. Attentional LSTMs, BPTT in Keras, and Long Sequences
3. AWS commands, Keras metrics and LSTM tests
4. Deep Learning for Natural Language Processing Courses
5. Differencing, One Hot Encoding and Validation Sets
6. Get great results by being systematic
7. Multivariate Forecasting, Mini-Course and Stacked LSTMs
8. RNNs, Adam Optimization and Data Scaling
9. Sequence Prediction, RNN Unrolling and NLP Books
10. Training Data Size, Hyperparameters and Why One Hot Encode

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