Andrew Ng's Coursera ML coding exercises and notes

In this repository, I upload my 100 Days ML Code which I have learned from different courses(Coursera, udemy, edx, udacity), different websites blogs, different tutorials from YouTube, books, and research papers. And this code is basically Siraj Raval’s 100 Days of ML Code Challenge! Which I completed in 100 days from November 2018 to February 2019. On the basis of my past one and half years of experience, I have done different projects in 100 Days related to Machine Learning, Deep Learning, Computer Vision, Natural Language Processing.

The assignment code for Coursera by Ng's ML course

This my assignment code for Coursera ML course

Octave and occasionally Python/NumPy code for the Coursera ML class.

Answers! The source code of problem is from https://github.com/dibgerge/ml-coursera-python-assignments

This repository contains a comprehensive collection of mathematical concepts and techniques relevant to various fields of AI, including ML, DL & other areas.It also includes the corresponding source code for all programming tasks associated with the Mathematics for Machine Learning courses, which are taught at Coursera by Imperial College London.

python code for the homework problems for coursera machine learning class by Andrew Ng (https://www.coursera.org/course/ml)

Julia code snippets to help with general machine learning algorithm implementations. Based on Andrew Ng ML course on coursera.

Completed labs and projects from the Machine Learning Specialization by Andrew Ng (Coursera/DeepLearning.AI). Includes supervised, unsupervised, and advanced ML algorithms. Clean code, key insights, and organized folders for each course.

These are Python code for Machine Learning course's exercise from Coursera, the one taught by Andrew Ng. You can find the course available online here: https://www.coursera.org/course/ml

Coursera ML Octave Assignment Code

The coding exercise from Andrew Ng's ML course in Coursera, some codes refer to https://github.com/nsoojin/coursera-ml-py

Hand gesture recognition (NN implemented using coursera ML course code and real time image processing in Opencv)

This repository is beginner-friendly for ML and contains all the codes related to the Course: Supervised Machine Learning Regression and Classification in Coursera

Kaggle-Survey-2020-Commpetition The code in this Repo is submission for the annual Kaggle ML & DS survey Competition. Our goal In this competition we were given the data of the annual survey that Kaggle conducts every year and we were asked to explore this data and come up with some conclusions that might be unique and wouldn't be visible if we just glanced through the data. I performed EDA on the data on features like - Age of a person, the country they live in, The Language they use to code, the IDEs they preder, their job titles and much more. After exploration we were able to come up with some conclusions which i have mentioned at the end of my notebook. Few Results and Observations Most Kaggle user's are quite young with their age between 22-29. The Number of Men using Kaggle is huge as compared to the Woman. But we could see a significant growth in number of female Kagglers recently. Most Kaggler's are from India followed by USA and other countries. Most Kagglers have a Master's Degree. Majority of Kagglers are Students followed by Data Scientists and Machine Learning Engineers. Most Kagglers have Experience of 3-5 Years in the Programming and then there are Kagglers with an experience of 1-2 years. Most Kagglers use Python followed by SQL and R. The most Preffered IDEs are Jupyter, VScode and PyCharm. Most Recommended Languages for Data Science Beginners is Python followed by R. The most used data visualization Libraries are Matplotlib and Seaborn. The most used framework for Machine learning and Deep learning is Sci-Kit learn followed by Tensorflow along with Keras. The Most commonly used algorithms are Regression based followed by Decision trees, random forests and so on. Most users share their work on Github followed by Kaggle and Colab. There are also many who dont like to share their work. Most users preferred Coursera to learn Data science and Machine Learning followed by Kaggle Courses and Udemy. Most users make use of Kaggle notebooks and forums to stay updated about latest Data science and ML topics followed by Youtube and Blogs on various websites.

Coursera - Machine Learning by Andrew Ng

Machine Learning code from coursera ML assignments

Coursera-ML-AndrewNg-Notes/Code(Python)

Assignment code for Bayesian ML course on Coursera

Andrew Ng's Coursera ML coding exercises and pdf. One week one file.

This Repository is the Code base ( Solutions ) of Coursera ML course by Andrew Ng Solved by me.

Andrew Ng coursera ML codes, writing with python3, in some cases, it will use scikit-learn and tensorflow

Reimplementation of code in Andrew NG's ML course on Coursera and CMPUT466/551 in UAlberta with R

All codes related to some assignments from Coursera's ML course by Andrew Ng (credit to him and relevant people)

Programming Exercise 2 in Machine Learning course by Andrew Ng on Coursera. The course info can be found here https://class.coursera.org/ml-004/class/index. In this exercise, a logistic regression model to predict whether a student gets admitted into a university will be created step by step. The details of this assignment is described in ex2.pdf The codes are written by Octave. For further info of Octave please see http://www.gnu.org/software/octave/ This set includes: ex2.m - Octave script that will help step you through the exercise ex2_reg.m - Octave script for the later parts of the exercise ex2data1.txt - Training set for the first half of the exercise ex2data2.txt - Training set for the second half of the exercise mapFeature.m - Function to generate polynomial features plotDecisionBounday.m - Function to plot classifier’s decision boundary plotData.m - Function to plot 2D classification data sigmoid.m - Sigmoid Function costFunction.m - Logistic Regression Cost Function predict.m - Logistic Regression Prediction Function costFunctionReg.m - Regularized Logistic Regression Cost

coursera_ml_code

Coursera ML code

code of machline learning lesson in coursera

Homework from coursera ML courrse