# Fundamentals of Machine Learning with Python - Part 9: Anomaly Detection and Recommender Systems

This post - like all others in this series - refers to Andrew Ng's machine learning class on Coursera and provides Python code for the  exercises.  The pure code, exercise text, and data files for all parts of the series are available here. Part 1:  Linear Regression with One Variable Part 2 :  Linear Regression with Multiple Variables Part 3 : Logistic Regression Part 4:  Multi-class Classification Part 5:  Neuronal Network Learning Part 6 : Regularized Linear Regression and Bias Variance  Part 7: Support Vector Machines Part 8: Dimensionality…

# Fundamentals of Machine Learning with Python - Part 8: Dimensionality Reduction - K Means Clustering and PCA

This post - like all others in this series - refers to Andrew Ng's machine learning class on Coursera and provides Python code for the  exercises.  The pure code, exercise text, and data files for all parts of the series are available here. Part 1:  Linear Regression with One Variable Part 2 :  Linear Regression with Multiple Variables Part 3 : Logistic Regression Part 4:  Multi-class Classification Part 5:  Neuronal Network Learning Part 6 : Regularized Linear Regression and Bias Variance  Part 7: Support Vector Machines Part 8: Dimensionality…

# Fundamentals of Machine Learning with Python - Part 7: Support Vector Machines

This post - like all others in this series - refers to Andrew Ng's machine learning class on Coursera and provides Python code for the  exercises.  The pure code, exercise text, and data files for all parts of the series are available here. Part 1:  Linear Regression with One Variable Part 2 :  Linear Regression with Multiple Variables Part 3 : Logistic Regression Part 4:  Multi-class Classification Part 5:  Neuronal Network Learning Part 6 : Regularized Linear Regression and Bias Variance  Part 7: Support Vector Machines Part 8: Dimensionality…

# Fundamentals of Machine Learning with Python - Part 6: Regularized Linear Regression and Bias Variance

This post - like all others in this series - refers to Andrew Ng's machine learning class on Coursera and provides Python code for the  exercises.  The pure code, exercise text, and data files for all parts of the series are available here. Part 1:  Linear Regression with One Variable Part 2 :  Linear Regression with Multiple Variables Part 3 : Logistic Regression Part 4:  Multi-class Classification and Neuronal Networks Part 5:  Neuronal Network Learning Part 6 : Regularized Linear Regression and Bias Variance  Part 7: Support Vector Machines…

# Fundamentals of Machine Learning with Python - Part 5: Neural Network Learning

This post - like all others in this series - refers to Andrew Ng's machine learning class on Coursera and provides Python code for the  exercises.  The pure code, exercise text, and data files for all parts of the series are available here. Part 1:  Linear Regression with One Variable Part 2 :  Linear Regression with Multiple Variables Part 3 : Logistic Regression Part 4:  Multi-class Classification Part 5:  Neuronal Network Learning Part 6 : Regularized Linear Regression and Bias Variance  Part 7: Support Vector Machines Part 8: Dimensionality…

# Fundamentals of Machine Learning with Python - Part 4: Multi-class Classification

This post - like all others in this series - refers to Andrew Ng's machine learning class on Coursera and provides Python code for the  exercises.  The pure code, exercise text, and data files for all parts of the series are available here. Part 1:  Linear Regression with One Variable Part 2 :  Linear Regression with Multiple Variables Part 3 : Logistic Regression Part 4:  Multi-class Classification Part 5:  Neuronal Network Learning Part 6 : Regularized Linear Regression and Bias Variance  Part 7: Support Vector Machines Part 8: Dimensionality…

# Fundamentals of Machine Learning with Python - Part 3: Logistic Regression

This post - like all others in this series - refers to Andrew Ng's machine learning class on Coursera and provides Python code for the  exercises.  The pure code, exercise text, and data files for all parts of the series are available here. Part 1:  Linear Regression with One Variable Part 2 :  Linear Regression with Multiple Variables Part 3 : Logistic Regression Part 4:  Multi-class Classification Part 5:  Neuronal Network Learning Part 6 : Regularized Linear Regression and Bias Variance  Part 7: Support Vector Machines Part 8: Dimensionality…

# Fundamentals of Machine Learning with Python - Part 1: Linear Regression with One Variable

This post - like all others in this series - refers to Andrew Ng's machine learning class on Coursera and provides Python code for the  exercises.  The pure code, exercise text, and data files for all parts of the series are available here. Part 1:  Linear Regression with One Variable Part 2 :  Linear Regression with Multiple Variables Part 3 : Logistic Regression Part 4:  Multi-class Classification Part 5:  Neuronal Network Learning Part 6 : Regularized Linear Regression and Bias Variance  Part 7: Support Vector Machines Part 8: Dimensionality…

# How to choose the right algorithm for your machine learning problem

With the recent machine learning boom, more and more algorithms have become available that perform exceptionally well on a number of tasks. But knowing beforehand which algorithm will perform best on your specific problem is often not possible. If you had infinite time at your disposal, you could just go through all of them and try them out. The following post shows you a better way to do this, step by step, by relying on known techniques from model selection and hyper-parameter tuning.   Step…

# Free Your Data With Open APIs For Banking

Flashy fintech solutions promise to help financial services companies thrive in the digital economy. Although these innovative offerings are getting lots of news coverage, there is a quieter but more transformational shift emerging: a movement to free data from the proprietary applications that create, process, and store information. Although these apps store terabytes of valuable data, the information is not traditionally available to third parties. Closed apps cannot be consumed by fintech solutions or used to make information available that might help financial services companies…