Going Through Coursera Machine Learning Course in 1 Week

by Leon Feb. 10, 2019

You have a data scientist job interview in about 1 week, and you want to take an online machine learning class to help you be better prepared, in a rush mode.

You found the 5 stars machine learning course by Dr. Andrew Ng on Coursera, but it is going to take 11 weeks, how can you go through all of its materials within 1 week?

If you are like many data scientists job candidates, you are not alone. In this article, I will walk you through Dr. Andrew Ng's online machine learning class section by section, help you focus on materials that matter most, so you can finish the course in 6 days.

I assume you have known most of the machine learning and data science concepts from the class, but just wanted to go over them once again.

Alternatively, if you are really in a rush and don't want to read the rest of this article, feel free to download recommended course timetable in here:

Going through Coursera Machine Learning Course in 6 days.

For your convenience, I have embedded a link to all the videos you should watch. In other words, as long as the section title is clickable, you should watch it.

Week 1

Introduction (⏩SKIP)

"Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed."

Justification: many of the concepts are only briefly mentioned and are repeated in future sections.

Time saved: 2 hours

Linear Regression with One Variable (📺WATCH)

"Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning."

Finish it on: Day 1

Linear Algebra Review (⏩SKIP)

"This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables."

Justification: we assume you don't need to go through college algebra.

Time saved: 2 hours

Week 2

Linear Regression with Multiple Variables (📺WATCH)

"What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression."

Finish it on: Day 1

Octave/Matlab Tutorial (⏩SKIP)

This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.

Justification: Octave was a great tool but due to its lack of adoption in industry, I highly recommend you choose Python as your main programming language instead of Octave. I personally have never seen anyone use it at work during my 11 years career.

Time saved: 5 hours

Week 3

Logistic Regression (📺WATCH)

"Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification."

Finish it on: Day 2

Regularization (📺WATCH)

"Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data."

Finish it on: Day 2

Week 4

Neural Networks: Representation (⏸OPTIONAL)

"Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks."

Justification: Neural Networks is the capstone algorithm for modern day deep learning, I personally felt it should be more appropriate to ask related questions if you are interviewing for AI companies such as self driving cars. I have almost always seen a better results with Random Forest or Gradient Boosting algorithm than a shallow neural network.

Time saved: 5 hours

Week 5

Neural Networks: Learning (⏸OPTIONAL)

"In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition."

Time saved: 5 hours

Week 6

Advice for Applying Machine Learning (📺WATCH)

"Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models."

Justification: this is actually a very helpful section, lots of valuable experiences shared by Dr. Ng handling different real scenarios.

Finish it on: Day 3

Machine Learning System Design (📺WATCH)

"To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data."

Finish it on: Day 3

Week 7

Support Vector Machines (📺WATCH)

"Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice."

Finish it on: Day 4

Week 8

Unsupervised Learning (📺WATCH)

"We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points."

Finish it on: Day 5

Dimensionality Reduction (📺WATCH)

"In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets."

Finish it on: Day 5

Week 9

Anomaly Detection(📺WATCH)

"Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection."

Finish it on: Day 5

Recommender Systems(📺WATCH)

"When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm..."

Finish it on: Day 6

Week 10

Large Scale Machine Learning(📺WATCH)

"Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets."

Finish it on: Day 6

Comments: very practical advices when dealing with a large amount of data

Week 11

Application Example: Photo OCR(⏩SKIP)

Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and. improve the performance of such a system.

Justification: the technology is a bit outdated, a deep learning model trained with Tensor Flor or other framework can easily create a much better performance.


If you follow the new timetable, you should be able to finish the course in 6 days, and be ready for your interview.

Good luck, as always, feedback or comments are more than welcome.

Machine Learning Expert, former Amazon research scientist