Breaking down Machine Learning Week 8 Assignment: A Step-by-Step Guide
Machine Learning is an exciting field that has made inroads into several industries, including healthcare, finance, and e-commerce. It involves the ability of computers to learn from previous experience and make data-driven decisions. As Machine Learning becomes more prevalent, its demand in the job market has surged, and companies are searching for skilled professionals to aid them in utilizing this technology.
If you are interested in Machine Learning, you might be familiar with Coursera, a leading online platform for computer science courses offered by top universities. Coursera’s course on Machine Learning by Andrew Ng is a popular course that has equipped students worldwide with the requisite skills to pursue careers in this field.
Ng’s course culminates with a set of practical exercises and assignments designed to help students consolidate their knowledge in Machine Learning. The week 8 assignment is a multi-class image classification problem that aids in the development of essential skills like implementing Neural Networks.
This article provides a comprehensive breakdown of the week 8 assignment in Andrew Ng’s Machine Learning Course on Coursera. It offers a step-by-step guide that will help you better understand the intricacies of the assignment.
Step 1: Download the Assignment
The first step is to navigate to the assignment available on Coursera’s course page. You can download the assignment from there.
Step 2: Introduction to the Problem
The week 8 assignment involves multi-class image classification, which refers to the sorting of images into specific image classes. In this task, an algorithm will predict the particular category of a given image.
The data set contains images that are 20 by 20 pixels, with RGB image color channels and 5000 training examples. The problem requires you to create a model that classifies input images into categories based on pixel intensity.
Step 3: Breaking Down the Assignment
Coursera provides a set of implementation libraries to help students implement the models required to solve the problem. The implementation libraries for this assignment are available in Matlab, Octave, or Python.
The first part of the implementation requires you to set up the architecture of the Neural Network. The Neural Network Architecture should consist of an input layer, one hidden layer, and an output layer. You can adjust the size of the hidden layer, and you should use a Softmax activation function to generate the output probabilities.
The next step requires you to implement the cost function that estimates the how far off the predictions are from the actual values for any given set of parameters. For this problem, the cost function should be cross-entropy, which measures the distance between the predicted values and the actual labels.
Lastly, you need to apply a backpropagation algorithm to update the weights of the Neural Network. This algorithm allows you to adjust the weights of the Neural network iteratively until a desired accuracy level is achieved.
Step 4: Conclusion
The week 8 assignment in Andrew Ng’s Machine Learning Course on Coursera is an excellent practical exercise that helps students consolidate their knowledge in Machine Learning. It involves multi-class image classification and requires students to implement Neural Networks using the provided implementation libraries.
You should aim to complete this assignment as well as other assignments in the course as they aid in equipping you with essential skills in Machine Learning. This knowledge is precious as Machine Learning becomes increasingly critical in data-driven decision-making.
In conclusion, Machine Learning involves computers capable of learning from previous experiences and making data-driven decisions. Students pursuing this field should take advantage of resources like Coursera’s course on Machine Learning and practical assignments like the week 8 assignment to develop requisite skills and consolidate knowledge.
