Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera

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Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models | ||||||||||

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Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera | ||||||||||

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Deep Learning Specialization Coursera | 266 | 4 months ago | apache-2.0 | Jupyter Notebook | ||||||

This repo contains the updated version of all the assignments/labs (done by me) of Deep Learning Specialization on Coursera by Andrew Ng. It includes building various deep learning models from scratch and implementing them for object detection, facial recognition, autonomous driving, neural machine translation, trigger word detection, etc. |

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Readme

If you've finished the amazing introductory Machine Learning on Coursera by Prof. Andrew Ng, you probably got familiar with Octave/Matlab programming. With this repo, you can re-implement them in Python, step-by-step, visually checking your work along the way, just as the course assignments.

This project was coded in Python 3.6

- numpy
- matplotlib
- scipy
- scikit-learn
- scikit-image
- nltk

The fastest and easiest way to install all these dependencies at once is to use Anaconda.

There are a couple of things to keep in mind before starting.

- all column vectors from octave/matlab are flattened into a simple 1-dimensional ndarray. (e.g., y's and thetas are no longer m x 1 matrix, just a 1-d ndarray with m elements.)
So in Octave/Matlab,

Now, it is`>> size(theta) >> (2, 1)`

`>>> theta.shape >>> (2, )`

- numpy.matrix is never used, just plain ol' numpy.ndarray

- Linear Regression
- Linear Regression with multiple variables

- Logistic Regression
- Logistic Regression with Regularization

- Multiclass Classification
- Neural Networks Prediction fuction

- Neural Networks Learning

- Regularized Linear Regression
- Bias vs. Variance

- Support Vector Machines
- Spam email Classifier

- K-means Clustering
- Principal Component Analysis

- Anomaly Detection
- Recommender Systems

You can check out my implementation of the assignments here. I tried to vectorize all the solutions.

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