This page lists selected good posts from machinelearningmastery by Adrian Rosebrock.

- Implementation Patterns for the Encoder-Decoder RNN Architecture with Attention (by on October 19, 2017 in Long Short-Term Memory Networks)
- Best Practices for Document Classification with Deep Learning (by on October 23, 2017 in Natural Language Processing)
- How to Index, Slice and Reshape NumPy Arrays for Machine Learning in Python (by on October 25, 2017 in Python Machine Learning)
- Understand the Difference Between Return Sequences and Return States for LSTMs in Keras (by on October 24, 2017 in Long Short-Term Memory Networks)
**How to Grid Search Hyperparameters for Deep Learning Models in Python With Keras****(by on August 9, 2016 in Deep Learning)**- 8 Inspirational Applications of Deep Learning (by on July 14, 2016 in Deep Learning)
**A Tour of Machine Learning Algorithms**(November 25, 2013)**8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset**(August 19, 2015)**Machine Learning for Programmers (**August 17, 2015**) –**repost**Predict Sentiment From Movie Reviews Using Deep Learning**(by on July 4, 2016 in Deep Learning)- Machine Learning Algorithms Mini-Course (April 29, 2016) – repost
- Machine Learning In A Year (October 7, 2016)
- Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras (July 21, 2016)
- Develop Your First Neural Network in Python With Keras Step-By-Step (May 24, 2016)
- Introduction to Python Deep Learning with Keras (May 10, 2016)
**Sequence Classification with LSTM Recurrent Neural Networks in Python with Keras**(July 26, 2016)**Multi-Class Classification Tutorial with the Keras Deep Learning Library**(June 2,2016)

- one hot encoding from a categorical variable.

We can do this by first encoding the strings consistently to integers using the scikit-learn class LabelEncoder. Then convert the vector of integers to a one hot encoding using the Keras function to_categorical().

- Evaluate The Model with
**k-Fold Cross Validation**

The scikit-learn has excellent capability to evaluate models using a suite of techniques. The gold standard for evaluating machine learning models is k-fold cross validation.

- Simple 3-Step Methodology To The Best Machine Learning Algorithm (January 22, 2016)
- 10 Standard Datasets for Practicing Applied Machine Learning (November 25, 2016) – repost
- Machine Learning Performance Improvement Cheat Sheet (November 23, 2016) – repost
- Your First Machine Learning Project in Python Step-By-Step (June 10, 2016)
- Save and Load Machine Learning Models in Python with scikit-learn (June 8, 2016)
- Spot-Check Classification Machine Learning Algorithms in Python with scikit-learn (May 27, 2016)
- How To Prepare Your Data For Machine Learning in Python with Scikit-Learn (May 18, 2016)
- Introduction to Machine Learning with scikit-learn (May 6, 2016)
- scikit-learn Cookbook Book Review (May 9, 2016)
- Machine Learning Algorithm Recipes in scikit-learn (June 20, 2014)
- How to Tune Algorithm Parameters with Scikit-Learn (July 16, 2014)
- Use Keras Deep Learning Models with Scikit-Learn in Python (May 31, 2016)
- A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library (April 16, 2014)
- Stochastic Gradient Boosting with XGBoost and scikit-learn in Python (September 19, 2016)
- Ensemble Machine Learning Algorithms in Python with scikit-learn (June 3, 2016)
- Metrics To Evaluate Machine Learning Algorithms in Python (May 25, 2016)
- Feature Selection For Machine Learning in Python (May 20, 2016)
- Visualize Machine Learning Data in Python With Pandas (May 16, 2016)
- Overfitting and Underfitting With Machine Learning Algorithms (March 21, 2016) – repost
- 5 Step Life-Cycle for Neural Network Models in Keras (August 11, 2016 in Deep Learning)
- Regression Tutorial with the Keras Deep Learning Library in Python
**How to Grid Search Hyperparameters for Deep Learning Models in Python With Keras**(on August 9, 2016 in Deep Learning)

- Time Series Prediction With Deep Learning in Keras (July 19, 2016 in Deep Learning)
- How To Implement Naive Bayes From Scratch in Python (December 8, 2014 in Algorithms From Scratch)
- How to Implement Random Forest From Scratch in Python (November 14, 2016 in Algorithms From Scratch)
- How To Implement The Decision Tree Algorithm From Scratch In Python (November 9, 2016 in Algorithms From Scratch)
- How To Implement Simple Linear Regression From Scratch With Python (October 26, 2016 in Algorithms From Scratch)
- How To Implement
**Machine Learning Algorithm**From Scratch With Python (October 19, 2016 in Algorithms From Scratch)*Performance Metrics* - How to Implement Resampling Methods From Scratch In Python (October 17, 2016 in Algorithms From Scratch)
- How to Scale Machine Learning Data From Scratch With Python ( October 14, 2016 in Algorithms From Scratch)
- Tutorial To Implement k-Nearest Neighbors in Python From Scratch (September 12, 2014 in Algorithms From Scratch)