Udacity Machine Learning Nanodegree Review
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Do you want to get training in machine learning, but you are not quite sure where to start? Going back to school and studying doesn’t feel like an option, you may not have the time, money, or patience for that. So, what about online courses?
There are so many online courses now, some are credible, some are not. Others can give you fast and easy learning, whereas some others are more drawn out and have similarities to university academic curriculums.
Making a choice is hard and can be very overwhelming, and that is why today we are going to review one of the best online machine learning nanodegree courses available today.
Through Udacity, you can take a course on machine learning and work towards building your knowledge. We will take the time today to talk you through the course modules, the instructors, and even what previous graduates have had to say about the course.
Let’s get to it!
About The Course
The machine learning nanodegree course is run by Udacity. It is in place to help you work towards becoming a part of a new area of exciting technology, AI systems. Machine learning is about complex systems and mathematical algorithms that synchronize to create a system that can learn on its own.
While the idea of this seems like something from ‘The Matrix’, it is a technology that many of the biggest tech companies are working on, think of the Amazon Alexa, or Google Nests, or even the Cortana software.
The Udacity course for this learning is split into two terms, that is estimated to be completed over three months. You learn the advanced machine learning deployment techniques and software engineering best practices.
All Udacity courses offer real-world projects from industry experts, technical mentor support, career services, and a flexible learning program that you can tailor to fit around your busy life. It is suitable for doing while you are also working, as a way to help broaden your career aspects.
Due to rapid technological advancements that are ever-changing, there is always a high demand for those who understand how to build complex machine systems, in nearly every market that exists. Not only can taking a course like this to broaden your career horizons, but it can also help you keep up to date with the ever-changing technology industry. Of course, you learn the basics and the foundations that are needed in such an area, but you also get the hands-on experience that helps your resume look even more attractive when you decide to get out there.
Meeting the Instructors
When taking any course it is important to know who you are learning from. Individuals who have a background in the area, with plenty of experience are what any learner seeks from their instructors. This is why we have got all you need to know about them right here.
Cezanne Camacho
Cezanne Camacho, is the curriculum leader. She is a machine learning education with a Masters’s Degree in Electrical Engineering from Standford University. Her background work was as a former researcher in genomics and biomedical imaging, in which she applied machine learning to medical diagnostic applications.
Mat Leonard
Mat Leonard is one of the instructors and is a former physicist, research neuroscientist, and data scientist. He did his Ph.D. and Postdoctoral Fellowship at the University of California, Berkeley.
Luis Serrano
Luis Serrano is one of the instructors and is a former Machine Learning Engineer for Google. He has a Ph.D. in mathematics from the University of Michigan, as well as a Postdoctoral Fellowship from the University of Quebec at Montreal.
Dan Romuald Mbanga
Dan Mbanga is one of the instructors. He leads Amazon AI’s Business Development effort for Machine Learning Services. On a day-to-day basis, he works with customers, from startups to enterprises, to ensure that they are successful at building and deploying models on Amazon SageMaker.
Jennifer Staab
Jennifer Staab is one of the instructors. She has a Ph.D. in Computer Science and a Masters Degree in Biostatistics. She was a professor at Flordia Polytechnic University. She has also previously worked at RTI International and United Therapeutics as a statistician and computer scientist.
Sean Carrell
Sean Carrell is an instructor. He is a former research mathematician who specialized in Algebraic Combinatorics. He completed his Ph.D. and his Postdoctoral Fellowship at the University of Waterloo, Canada.
Josh Bernhard
Josh Bernhard is a Data Scientist at Nerd Wallet. Josh has been sharing his passions for data for nearly a decade at all levels of University, and as well, as a Lead Data Science Instructor at Galvanize. He’s used data science for work ranging from Cancer Research to process automation.
Jay Alammar
Jay Alammar is an instructor. Jay has a degree in computer science and loves visualizing machine learning concepts. He is the investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.
Andrew Paster
Andrew Paster is an instructor. Andrew has an engineering degree from Yale. He has used his data science skills to build a jewelry business from the ground up. He has also created courses for Udacity’s Self-Driving Car Engineer Nanodegree program.
What are the Prerequisites?
Prerequisites are something that you must have for you to be able to do something else. In this sense, you must have prerequisites to do this course.
For the course of machine learning, it is recommended that you have experience. To succeed in this course, it is best to have previous experience in programming using ‘Python’, know inferential statistics, probability, algebra, and calculus. If you have never programmed before, or if you want a refresher you can prepare for this with either an intro to computer science or the nanodegree with lessons 1-4.
Though it is not 100% necessary, it is recommended, otherwise, you may find yourself bewildered by some of the topics and it is good to have a starting knowledge first.
Course breakdown- Looking at the Modules.
First off the educational objectives of this course are to teach you how to become a machine learning engineer, to build machine learning models, and apply them to data sets in fields like finance, healthcare, education, and more.
There are two terms to this course, each has modules and projects that need to be completed.
In term 1, you will learn machine learning foundations. The project given for this module is to predict Boston housing prices.
For this module, you also get supporting lesson content, the model evaluation and validation with lesson titles, and the learning outcomes from this.
| Lesson Title | Learning Outcomes |
| Training and Testing models. | Load data with Pandas, then train and test models with scikit-learn. |
| Evaluation Metrics | Learn about metrics such as accuracy, precision, and recall used to measure the performance of your models. |
| Evaluation and Validation. | Choose the best model using cross-validation and grid searching. |
The second project for term one is titled, ‘ FInding Donors for CharityML’. This module has even more learning outcomes than the first. The assignment is as follows;
The supporting lesson content for this module focuses on supervised learning.
| Lesson Title | Learning Outcomes |
| Linear regression | To learn the difference between regression and classificationTo learn to predict values with linear regression |
| Perception Algorithm | To learn the definition of perception as a building block for neural networks, and the perception algorithm for classification. |
| Logistic Regression | To learn to predict states using logistic regression. |
| Neural Networks | To learn the definition of a neural network.To learn to train them using backpropagation.To build a neural network starting from a single perception. |
| Decision Trees | To train decision trees to predict states. To use entropy to build decision trees recursively. Random forests. |
| Naive Bayes | To learn Bayes rules, and how to apply them to predicting data using the naive Bayes algorithm. To train models using Bayesian learning.To use Bayesian inference to create Bayesian Networks of several variables. Bayes NLP min-Project. |
| Support Vector Machines | Learn to train a support vector machine to separate data linearly. Using Kernel methods to train SVMs on data that is not linearly separable. |
| Ensemble of Learners | To enhance traditional algorithms via boosting. AdaBoost. |
Your third project in term 1, is based on unsupervised learning and created customer segments.
The lesson content here is based on unsupervised learning.
| Lesson Title | Learning Outcomes |
| Clustering | Learning the basics of clustering Data.Cluster data with the K-means algorithm. |
| Hierarchical and density-based clustering | Cluster data with single linkage clustering.Cluster data with DBSCAN, a clustering method that captures the insight that clusters are dense groups of points. |
| Gaussian mixture models. | Cluster data with Gaussian mixture models.Optimize Gaussian mixture models with expectation maximization. |
| Feature scaling. | Leaning to scale features in your data.Learn to select the best features for training data. |
| Dimensionality reduction. | To reduce the dimensionality of the data using Principal Component Analysis and Independent Component Analysis. |
Then in term 2, you get four more projects/ modules. These help to advance your machine learning knowledge. Building on even more what you learned in the first term.
Project one for term two focuses on deep learning. It goes over ‘dog breed classifiers’ and involves understanding challenges in piecing together models designed to perform tasks in data processing.
| Lesson Title | Learning Outcomes. |
| Machine learning to deep learning | Learn the basics of deep learning, inc; softmax, one-hot encoding, cross-entropy. Basic linear classification models; logistic regression, and associated error function. |
| Deep Neural Learning | Review: what is a neural network.Activation functions, sigmoid, tanh, and reLus. How to train a neural network using backpropagation and the chain rule. How to improve a neural network. |
| Convolutional Neural Networks. | What is a Convolutional neural network? How CNN’s are used in image recognition. |
The fifth module, and second of term 2, is based on reinforcement learning.
| Lesson Title | Learning Outcomes. |
| Welcome to RL | The basics of reinforcement learning and OpenAI gym. |
| The RL Framework: The Problem | Learning how to define Markov Decision Processes to solve real-world issues. |
| The RL Framework: The Solution | Learning about policies and value functions.Derive the bellman equations |
| Dynamic Programming | Write your implementations of iterative policy evaluation, policy improvement, policy iteration, and value iteration. |
| Monte Carlo methods | Implement classic Monte Carlo prediction and control methods. Learn about greedy and epsilon-greedy policies.Explore solutions to the exploration-exploitation dilemma. |
| Temporal-difference methods | Learn the difference between the sarsa, Q-learning, and expected sarsa algorithms. |
| RL in continuous spaces | Learn how to adapt traditional algorithms to work with continuous spaces. |
| Deep Q-Learning | Extend value-based reinforcement earning methods to complex problems. |
| Policy Gradients | Policy-based methods try to directly optimize for the optimal policy. Learn how they work and why they are important. |
| Actor-critic methods | Learn how to continue value-based and policy-based methods. |
In modules 6 and 7, the final modules, you complete the ‘capstone proposal’ and the ‘capstone project’. Working on a proposal and a project that covers all you have learned throughout the Machine Learning course. You will have to encompass several key points and then implement your algorithms and metrics of choice, culminating in your final project.
How long does the course take?
The course does not take too long to complete, certainly not when compared to university learning. It has an estimated competition time of 3 months based on an average of 10 hours per week.
Since you can complete the course at your leisure it is entirely up to you how much time you put in per week. If you were to only do 5 hours a week then you could expect the course to take up to 6 months to complete. It is your decision.
How much does the course cost?
For all that the course offers you, you can purchase it with a three-month access pass, if you think that you can complete it at this time, this costs around $1,163, or $387 per month. However, there is also an option for pay as you go, which you can get for around $457 per month.
It is up to you which option you take. If you do not think you will be able to fully complete the course in three months then the pay-as-you-go option is best. However, if you feel confident that you could complete the course in three months then you should take that option as it works out more financially viable. Be aware that Udacity does offer deals and discounts so if you can catch one of these you will be smooth sailing.
What have other learners said about the course?
Many other learners have taken on this course and have left reviews for your benefit, most have had nothing but good things to say about the course.
“The program is an excellent refresher of ML concepts. I took an ML online class in 2014 (Andrew Ng’s course) and this class was a good way to refresh the basic concepts. I am not sure how I would have performed if this was my first exposure to the themes. The exercises and projects were an excellent resource to familiarize myself with sklearn.”
–Mauricio B.
Some people had some very good things to say about the lectures and the understanding they gained from them.
“The lectures were engaging. The projects helped to gain a better understanding of how to apply the concepts learned. The structured and guided questionnaires help a student learn about how to approach a given problem.”
–Parag A.
Others had more to say about specific instructors.
“The program content is simply outstanding. Same goes for the instructor- Luis is simply amazing. The concepts have been explained so well. A key differentiator is the intuition behind the concepts that Luis explains so well.”
–Madhu B.
Here is someone who is simply loving the course!
“The program has been going great for me. There isn’t any “time filler”, everything was useful. I like how the lectures are very concise, to the point. I am excited to see more real-world use cases in the upcoming sections.”
How is the job market?
The job market for Machine Learning is very open. With the growth of technology, everyone is looking for those who are trained and well-versed in the area. It is highly applicable to many different industries and therefore has so many uses that make it highly desirable in the working world.
This program will help to prepare you for a multitude of roles. You will learn about machine learning algorithms, crucial deployment techniques, and you will bit fit for any roles for companies that seek learning engineers and specialists. These skills can also be applied to data science and companies seeking someone who can introduce machine learning techniques into their organization.
Is this the right course for me?
This program assumes that you are familiar with common supervised and unsupervised machine learning techniques. So, it is geared towards people who are interested in building and deploying a machine learning product or application.
You should ask yourself if you are interested in this field, in deploying an application that is powered by machine learning. If this is a field that interests you then this program is right for you.
You also need not apply subject to any criteria. This program accepts all applicants regardless of experience or specific background, all it asks is that you are interested in the area at hand.
About the Author Jacob M.
Jacob has a background in finance and engineering. Outside of his day job, he is a lifelong learner, who enjoys reading, taking online courses, and writing about what he's learned.
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