We, at Udacity, not only help in the development of skills in a field but also make sure our students are under proper guidance in the form of Mentorship Support. Mentorship support is like carrying a compass while sailing in the vast sea.

The sea can be overwhelming and may pose several challenges. Our mentors stand with the students at each step during the course of their Nanodegree program. Our mentorship model gives the students a sense of belonging and enables them to wrack their brains. This is exactly like physical classroom teaching which ensures no doubt is left in the student’s mind once he walks out of that classroom door. Our mentors hold expertise in the subject as they are previous Nanodegree holders and industry experts. Our mentors make sure to guide the students in every step.

Knowledge is a recently launched in-classroom tool that enables students to post their queries on a forum. This allows students to not only post their doubts but also looks at what others have posted. It saves time consumed in the process of asking a mentor. At times, the same doubt might have occurred to another student, so it makes it easier to find answers to that question. Once, answers to a question are posted, the correct answer is approved and highlighted.

One more enticing feature is the newly introduced, called Study Groups. As part of the Nanodegree, students join a Study Group with your classmates and several expert graders in the Udacity classroom. As they progress through the videos and work on projects, students find help with a community of students and mentors online from around the world, helping and pushing each other towards graduation.

Project Reviews are an essential part of our Nanodegree Programs. Our courses are enriched with various projects. We have a vast network of project reviewers who examine the projects submitted by the students line-by-line. They test the code and review each line of code. The reviewers provide substantial feedback which helps the students to understand their strengths and weaknesses. The feedback is unbiased and pertinent. This process of reviewing projects is rapid and effective. We often hold webinars conducted by our in-house experts for various projects, mostly to help our students with the projects.

The newly enhanced features that we provide during our Nanodegree Programs have helped our students tremendously. We continuously keep moving a step further to ease learning for our students. Our motive is to provide a world-class online learning experience and create job-ready individuals fit for the industry.

Udacity Advantage: Mentorship Support and Project Reviews was originally published in Udacity India Inc. on Medium, where people are continuing the conversation by highlighting and responding to this story.

Learning is a personal endeavour. Every individual has different learning objectives, skill sets, experiences, and preferences. Udacity provides multiple entry points into advanced technologies to help you achieve your career goal in the right order. The learning path not only helps individuals willing to start their careers but also make a significant shift or advancement in it.

Nanodegree Programs are a ticket to not only acquiring a new skill but also implementing it in real life. At every step, Udacity ensures that the students are fully aware of their progress and give them a proper picture of the industry, punctually. One such individual who reaped benefits from the Front-End Nanodegree Program is Mariam Fatima. Formally a social science teacher with little to no background knowledge of coding, took the program purely out of interest. What peaked due to sheer interest changed her life in an unexpected manner. After completion of the Nanodegree, she was able to score an internship from our in-house career fair, Propel, and make a career switch.

One of our most sought-after Nanodegree Programs is the ‘Computer Vision Nanodegree’. The field of self-driving car is still a budding technology and its scope in India will be incalculable over time. One of our graduates, Srikanth Vidapanakal is a prime example. With an experience of more than a decade in the field of Data Science, Srikanth successfully switched lanes by taking our Self-driving Car Nanodegree Program. Scoring a package of a whopping 40 Lakhs INR through Propel, his success story inspires many.

Learning is a vital aspect of our lives. Our next graduate defied all odds in her life and never compromised on her learning. Rajani M shows that with perseverance anything is feasible. Having been confined to her bed due to an unforeseen health condition, she was not deterred and restarted her career with pure determination. She was a recipient of the Google Developer Scholarship and is close to graduating from Android Developer Nanodegree.

In conclusion, Udacity has something to offer to everyone in all walks of life. It is only a matter of awareness and choosing the precise path. We, at Udacity, understand our students and have forged numerous career paths for advanced technologies. The students can recognise a path of their desire depending on the level of expertise, time commitment, and experience. The following is a list of learning paths meticulously created by our experts :

Career Paths | Udacity

Confused how to choose a learning path for you? Refer to this blog on how to choose your learning path.

Udacity Advantage: A learning path for everyone was originally published in Udacity India Inc. on Medium, where people are continuing the conversation by highlighting and responding to this story.

Finding a job is one of the most amazing feelings in the world. Irrespective of the track, all Udacity Nanodegree Programs have one ultimate goal — Prepare you for the jobs of today, tomorrow, and beyond!

With access to exclusive hiring events, competitions, and networking opportunities, thousands of Udacity students have already landed new jobs. We built the Career Services based on years of experience teaching in-demand skills, working with leading companies to develop valuable curriculum, and collaborating with hiring partners to ensure our graduates have exactly the skills they hire for. All career-ready Nanodegree Programs feature extensive career services. Udacity Career Services also feature Career Portal — a customized path for you to getting hired, where you can track your step-by-step progress and get one-on-one feedback from experts along the way.

Resume Review

Resume is the first step to knowing a candidate and it speaks volumes about his past experience and potential. A resume can either get a hiring manager interested in an individual or completely push him to chose the other route. Hence, it is necessary to carefully build a resume. Our graduates work closely with industry mentors to analyze the weak and strong points of their profile.

LinkedIn Profile Review

Along with resume review, we also offer LinkedIn reviews as it is one of the very common platforms nowadays for professionals for seeking potential candidates. Once, the resume of a graduate appeals to the hiring partner, they might want to take this a step ahead and place an interview call. The interview might seem like a very nerve-wracking stage. Our guidance also branches out to preparing our graduates to face these interviews confidently and completely present what the hiring manager is expecting.

Interview Preparation

Virtually all technical interviews follow a very set pattern: you get asked a very specific type of question, you’re required to have very specific knowledge, and you’re expected to answer — and interact with your interviewer — in a very particular way. Knowing this in advance, and preparing accordingly, is the key to success. Udacity Interview Preparation provides an introduction to topics that often show up on technical interviews for different roles. You’ll experience a mock interview, and review detailed analysis on how to field key industry questions.

Udacity Propel

All Nanodegree Programs are closely linked to the industry. We make that possible from the first step of ‘course curation’ to ‘helping our graduates to connect with our various hiring partners’. Through our flagship career fair - Propel, we connect our Nanodegree graduates to new-age tech companies and help them achieve their career goals. All Advanced Nanodegree program graduates are automatically eligible for Propel. So far, there are 100+ hiring partners who participated in Propel, 1200+ job opportunities created, and 40 lac+ highest annual CTC offered in Propel. Refer to this page for more details .

Our Hiring Partners in India

and more…

Student Stories from Propel

• When you start loving something that you initially did for fun — Rohan Taneja
• One Nanodegree, Udacity Propel, and four job offers — Tushar Mudgal
• From electrical engineer to a Software architect at Wipro Digital — Shivanand Thangalapalli
• How Udacity helped me explore a career path in new-age technologies — Sarvaswa Tandon
• Udacity Propel : Here's what you need to do
• Udacity Propel, Bangalore, June 2018
• Propel past career inertia
• Udacity Nanodegrees & Udacity Propel — here’s what you need to know

Enroll into a career-ready Nanodegree Program today to avail Career Services.

Udacity Advantage: Career Services, Hiring Partners, Propel, and Jobs was originally published in Udacity India Inc. on Medium, where people are continuing the conversation by highlighting and responding to this story.

With the advancement in technologies like Machine Learning and Artificial Intelligence, the job sector has also exponentially expanded. But this creation of more jobs also has its own setbacks. While industrialists profess that there’s a great demand for professionals in these fields, majority of the professionals lack the level of skill set they are looking for. There is a dearth of supply to this ever-growing demand. Hence, skilling up and reskilling is crucial to be stay relevant in the jobs market.

Udacity realized that with the milieu of online educators across the world, not many were focusing on the equivalent of vocational skills in the tech world specifically. Sure there were many degrees on the online platform but not many of them were focused on making an individual successful at the job they had chosen in the real world. A program that is aimed at teaching the missing link between your current education level and the practical degree that will launch you towards your career.

With Nanodegree Programs, we make sure our graduates are capable for taking on these employment opportunities with the relevant skills. Our team works closely with top professionals from companies like Google Inc., AT&T, Facebook, Github and many more to understand the mindset of the industry. This detailed study of the industry enables us to devise the right Nanodegree for our students.

The syllabus of Nanodegree is valued by top industry professionals from all around the world. Some of our prestigious Nanodegree Programs, follow a certain framework that the students need to follow. This path has been carefully devised to ensure that students learn in a systematic manner and master the field completely — from beginning to an advanced level. The Nanodegree Programs ensure an all-inclusive development of relevant skills. Since, the market values individuals with more practical knowledge, we make sure to infuse our curriculum with projects that hold a great level of desirability in the industry. These projects help in putting the understanding of the students to test. They are thoroughly reviewed by our mentors and are required to pass a certain threshold benchmark for our students to graduate successfully. The experts review the coding style of the student and help them to improve as well. Through this we ascertain that our graduates are fully versed and coherent in taking on industrial challenges.

The current wave of technological change around AI and ML is affecting many companies. There is a general fear that people, especially those working with IT companies, will become less relevant due to the rapid evolution of technologies and changing job requirements. But today, every sector needs to adapt as technology advances. In India, companies don’t want only want engineers to learn new technologies but engender greater familiarity and insight into new-age technologies and skills. As a result, companies are gradually adopting a mindset that focuses on growth and lifelong learning across verticals. Our curriculum is not only centered for individuals willing to start their career, but also for industry professionals who want to advance in their respective fields. Hence, bridging the skills-demand gap prevalent today.

Here’s how we collaborated with Infosys team to skill up their workforce in autonomous vehicles engineering space:

Infosys-Udacity Offer Self-Driving Car Engineer Nanodegree Program

Start learning today with Udacity Nanodegree Programs: http://bit.ly/UdacityND

Udacity Advantage: Content built and backed by the industry was originally published in Udacity India Inc. on Medium, where people are continuing the conversation by highlighting and responding to this story.

Udacity is an online global community for young learners from all around the globe to delve into the world of technology. With its vast array of online courses to choose from. These courses are put together for all curious minds looking forward to establishing a career in the field of computer sciences. Udacity’s course catalog is bifurcated into two categories, that are the Free Courses and the Nanodegree Programs. Both of these categories have clear-cut differences in the way they approach and offer to the learner.

Nanodegree Programs are industry-recognized credential designed for students to advance their skills or get new jobs. With the collective effort of our expert teams and industry leaders, Nanodegree Programs are curated to provide a hands-on learning experience of upcoming and current computer technologies. Our Nanodegree Programs are built in-collaboration with, and valued by, industry leaders like Google, Facebook, AT&T, Salesforce, GitHub, and more.

They focus extensively on job-readiness and cover every fundamental skill required to becoming successful in your career. The Nanodegree Programs heavily concentrate on current industry requirements and job outcomes. The Nanodegree Programs are divided into topic-wise modules which in turn comprise of short video lectures, exercises, quizzes, and projects to augment the comprehension of the topic. The courses are taught by highly scholarly instructors who have relevant industrial experience. Industry-relevant in the curriculum ensure that the students are ready to take up industrial challenges. The Nanodegree programs enable students to interact one-on-one with various industry specialists who are our Experts-In-Residence as a form of mentorship support to the students. Students will have the opportunity to engage in weekly office hours sessions with these highly-skilled individuals. After successful completion of a nanodegree programs, we link the graduates to our various hiring partners which allows them to get directly hired and advance in their careers.

Most common queries:

We also figured it would be helpful to answers to some of the most popular Udacity-related questions from around the Web. We hope this helps answer your inquiries about the Nanodegree programs.

What is the cost difference between a free course and a Nanodegree?

A Free Course costs not even a single dime. Nanodegree programs feature cutting-edge online curriculum built with industry leaders and taught by expert instructors. One-on-one coaching, moderated forums, and project review ensure a personalized experience. Graduates earn an industry-recognized credential and benefit from extensive career support.

What is the duration for a Nanodegree Program?

Our ultimate goal is student outcomes. Nanodegree Programs have a fixed timeline which they strictly follow and it needs to be completed within this period. However, projects may be submitted at any time during the Nanodegree, and individual project deadlines are recommendations, not requirements. So within the Nanodegree, there is opportunity to learn at your own pace. You should plan to follow our recommended timeline, as this will best enable you to keep pace with your peers, and complete the program on time.

Why is the Nanodegree course content valuable?

Our Nanodegree Programs are credential programs built with, and valued by, industry leaders like Google, Facebook, AT&T, Salesforce, GitHub, and more.

What kind of course content can I expect?

The level of instruction in both the types of courses varies greatly. While a free course is a good start to gaining basic knowledge on a particular topic, this level is taken up several notches with the renowned Nanodegree Programs. The medium of instruction is pre-recorded video lectures which have closed captioning. The degree of difficulty and content relevance is what makes a nanodegree superior to the free courses. The lectures are assisted by interactive quizzes, exercises and industry-relevant projects that assess the students carefully in Nanodegree Programs.

How will projects help my career and provide me an edge over not doing any?

The industry-relevant projects in the Nanodegree curriculum ensure that the students are ready to take up industrial challenges. These projects are carefully designed to impart practical knowledge. Once successfully completed and added to the student’s GitHub profile, it adds a great value to profile of the student.

What kind of classroom support can I expect?

Mentorship support is absent in free courses. A student enrolled in a nanodegree can interact one-on-one with their course instructor and clarify doubts. This individual attention on the student enables the student to comprehend the topics better. Mentorship support is provided in the form of Study Groups, a new and more advanced way to connecting students and mentors. Study Groups is a social learning model for students that want to truly immerse themselves in the student experience. Students that participate in Study Groups will form a sub-cohort that have their own private social community in the Classroom, with certified mentors. Along with Study Groups, we have introduced an online forum called Knowledge. It is a student-driven help experience that surfaces the most relevant questions and answers. Quality and relevance is ensured by upvotes of questions and answers.

How is the course different from other online learning platforms?

The Nanodegree Programs are devised in such a way that it provides job-readiness to its graduates as per industry standards. Since the curriculum focuses primarily on what kind skills are necessary in the market. These programs are highly detail oriented and focuses on overall understanding of a topic. Nanodegree Programs also focus highly on overall student experience and has multiple interventions to push students towards graduation like mentorship, student collaboration tools such as Knowledge and Study groups, and personalized project reviews.

How is the foundational Nanodegree program different from advanced Nanodegree program?

While a nanodegree makes an individual well versed in a field but some topics like Machine Learning which are extremely vast might take some more time and follow-up courses. Foundational Nanodegree programs is designed to provide the student foundational skills and do not make one job-ready. Advance Nanodegree programs are imperative for being employable in such a competitive market.

Can I get a job after graduating from a Nanodegree?

Nanodegree makes an individual capable of getting a job in their desirable field. Udacity connects students to its long list of hiring partners. We, at Udacity, have partnerships with over 100+ companies who have hired several Nanodegree graduates in the past. We connect our graduates regularly to our hiring partners through Propel.

Is Nanodegree equivalent to a college degree?

No. We are not accredited to UGC or AICTE. Whereas, we have various partnerships with industry experts. The industry requires a certain level of qualification for competing for a job profile and a Nanodegree program certification acts as a valuable credential. The Nanodegree Program makes a student job ready. Education is a personal preference and we do not encourage our students to skip college.

Do I get some monetary support?

No, we do not provide any monetary support. Students also have an option to pay the fee in installments through EMI on leading credit cards. We are also working towards bringing in more payment options that will help our students.

What is Udacity Propel and who are eligible for Udacity Propel?

We regularly host our renowned Career Fair called Udacity Propel. All graduates of career-ready Nanodegree Programs are automatically eligible for Propel. Through this we connect our graduates to our potential hiring partners. In Nanodegree Programs with two terms, you must complete your term 2 to become eligible for Propel.

Udacity Advantage: What is a Nanodegree Program? was originally published in Udacity India Inc. on Medium, where people are continuing the conversation by highlighting and responding to this story.

Motivation

I was always interested in Artificial Intelligence from an early age. It’s potential and applications filled me with an inspiring awe. Adaptation of Artificial Intelligence in Hollywood like Iron Man and I, Robot showed great promise about the wonders of AI to a me as a kid. But, I had no idea on how to get started in this field practically. I first came to know about Machine Learning when Google open sourced TensorFlow in November 2015. I tried to understand the framework myself but could not make much progress at first. I had previously done a few free courses from Udacity and enjoyed their teaching style and content quality. I had been coding in Python since 2015 and fortunately it turned out to be the language of choice for Data Scientists and Machine Learning Engineers. Therefore, I finally enrolled in Machine Learning Nanodegree in 2017 after self-learning the libraries NumPy, Pandas and Scikit-learn for a few months.

Components of the Udacity Nanodegree

• Video lectures and exercises from Udacity’s website
• Forums
• Slack Community (the best part)
• Mentoring up to 2nd project
• Projects on GitHub

The content of Udacity Nanodegree program is a perfect balance of equal parts theory and practical knowledge. The lecturers first explained theoretical concepts related to Maths and Algorithms, and then showed how to implement them in code.

The video lectures were intermittently followed by mini-exercises which served as points for revision and milestones in learning progress. If I ever got stuck on any topic, the Forums on Udacity and Slack team were always there to help. This was indeed the best part of Nanodegree experience for me. The Slack team has thousands of talented individuals who were either completing the Nanodegree or graduated from it. Therefore, someone was always there to help, no matter what time you ask a question. And instead of spilling the answer directly, they help you guide yourself towards it which is a great learning experience. Being a part of such an awesome community was a new and exhilarating experience for me.

To keep you on track, you are assigned a mentor until you complete your project. The mentor is available for 2–3 days/week. They are people with sound knowledge in Machine Learning and help you in every aspect to progress forward. My mentor guided me a lot when it came to complete my project. The initial boost gained by them helped me to complete the Nanodegree even after their unavailability.

The submission system for MLND allowed for two options: -

1. A zip file containing all the required files
2. A GitHub repository containing the same

I always preferred submission through a GitHub repository and I urge anyone reading this to do the same because it shows all your work (including your mistakes and how you fixed them) to others and also builds your portfolio.

For each project, there is a rubric stating the requirements clearly. The submission format was of Jupyter Notebooks. Earlier I was accustomed to using Python scripts for Machine Learning through the course of Nanodegree, I fell in love with Jupyter Notebooks which provide a perfect way to blend Code, Visualization and Documentation in a single file. I now use them on a regular basis and prefer them over plain scripts whenever needed.

Reviewers — The stars of the Nanodegree

Going through my project reviews felt simply awesome. Whenever, I failed to meet any requirements, the reviewer mentioned it clearly and showed possible workarounds. The project review was completely mapped to the rubric and hence it was super easy to zero in on the errors. Also, even when I had completed a requirement correctly, the reviewer discussed other possible methods to achieve it, which helped to expand my knowledge a lot.

Curriculum

The MLND curriculum consisted of the following sections when I graduated from it: -

1. Introduction to Machine Learning — Titanic Survival project
2. Model Evaluation and Validation
3. Supervised Learning
4. Unsupervised Learning
5. Reinforcement Learning
6. Deep Learning
7. Capstone Project

One doesn’t need to complete them in the order specified, but this order would be beneficial if you are completely unfamiliar to Machine Learning. In fact, I attempted to complete Deep Learning section after Model Evaluation and Validation but after a failed try I returned to the original order. This only proved useful because by the time I sequentially reached Deep learning, it was more comprehendible. I got hands on experience of Keras library which was previously not a part of the project.

The Capstone project is a nice inclusion to the curriculum. It gave me a brief idea about the entire process which Machine Learning Engineers follow while solving a problem. It allows you to choose a problem/dataset of your choice and complete the following steps: -

1. Problem scoping
2. Data Collection
3. Data Visualisation
4. Data Preprocessing
5. Feature Engineering
6. Model Selection
7. Model Hyper-parameter Tuning
8. Model Testing and Deployment

And the best part is that even though your idea is not ground breaking, or a convention defying research you still get acquainted with the entire Machine Learning process, which is a crucial factor for preparing you for a job in Machine Learning.

My Key Takeaways from the Nanodegree

Photo by Aaron Burden on Unsplash

• A solid theoretical of Machine Learning, its types and their applications
• Practical knowledge of implementing Machine Learning models in Python and best practices in Machine learning
• An opportunity to be part of an amazing community of learners from around the world
• First job out of college as a Data Scientist
• A passion for Lifelong learning

Enroll into Machine Learning Nanodegree here

About the Author | Divyesh Peshavaria

Pythonista. Love AI and Computer Vision. Passionate about using Technology for solving problems.

Machine Learning Nanodegree Experience was originally published in Udacity India Inc. on Medium, where people are continuing the conversation by highlighting and responding to this story.

Udacity?

“e-learning” — possibility of losing interest is very high in the journey.

BUT!!!

Udacity has a special way to maintain enthusiasm for every next video, next project, next Nanodegree Program. Trust me it will make you addictive towards learning.

“Udacity” — A tech DRUG!!!

“In general every one of us is a teacher.” — OK….Is it possible to teach a machine? Fortunately YES!!! This is where I started. I was fed up of manually deleting duplicate & blurry images from directories. Since I am a passionate CSE guy , I tried to find a solution to this problem by using technology, & at last I found a way to build a model using Machine Learning. I started learning Machine Learning especially fell in love with a section in Machine Learning known as Deep Learning.

“Deep Learning” — The spinal cord for Artificial intelligence.

I believe it will open the doors of intelligence one by one for a machine in particular conditions. I enrolled in Deep Learning Nanodgree with a fear(will I make it?)but now I have the courage to complete my nanodgree. Thanks to Udacity! It formed a one way bridge between fear and courage. Every quiz at videos made me more intuitive about the concepts. I would sometimes jump into other sections of the course just for brief insight. During the process I got a lot of exposure in the form of :

1. Slack channel — the best part is office hours where I can interact with industrial experts directly.

2. Project review — they have excellent supporting review system where I learnt in depth about some difficult concepts like parameter tuning.

3. Forums — a place to learn and clarify doubts with a great community.

3. Mentor support — a dedicated mentor checks your progress every week.I am lucky to have my mentor @NG Fang.

4. AWS credits — gpu support is the best part for experimenting on projects.

DLND has 6 sections with 5 projects, |Introduction|Neural Networks|Constitutional Networks|Recurrent Networks|Generative adversarial Networks|Deep reinforcement learning|

In section-1 a good knowledge about deep learning along with some basics was given.

In section-2 I learned how to build a neural network, how neural network works, how to tune the parameters much more. After completing the section I had submitted a project. Working on this project review gave me a complete practical overview on the neural network.

Going through section-3 I learned about CNN and image processing to build a neural network, which analyzes dog image and predicts it’s breed. I found this project the most amazing and fun project of all.

Moving on to section-4 I built a RNN in tensor flow to process text and generate a new TV script.

At present I am going through section-5 where I will soon build a pair of multi layer neural networks and make them compete with each other in order to generate faces.

At every project I had a milestone to achieve, but still I came through. All thanks to the amazing support and help that my mentors and community gave me! The best part of Udacity is that it flexibly fir with my time & weekly goals and bracket due dates helped me to stay on track.

“Delay is nothing decision is everything”

Enroll into Deep Learning Nanodegree here

About the Author | P Nithin Jayakar

Al Optimist. •Epigrammatist. •Sketch Artist. •Palestrato.

My Journey with Udacity’s Deep Learning Nanodegree Program was originally published in Udacity India Inc. on Medium, where people are continuing the conversation by highlighting and responding to this story.

I graduated from Udacity Machine Learning Nanodegree in July,2017 and I will be sharing how it has helped me learn,grow and apply my gained nanodegree skills to real world problems.

What is Nanodegree anyway?

A regular degree from a university takes around 4 years to complete. A regular degree also has a few core courses, some electives, and some open-ended projects. Regular degrees provide certification to signal that the student is ready to work in a field.

A Nanodegree program is like a regular degree in the sense that it also has some core parts and electives. But the timeline for a Nanodegree program is much smaller, around 6–12 months. (hence ‘nano’). Udacity partners with different companies like Kaggle, Facebook, and Google for creating courses. Each Nanodegree program finishes with a capstone project. Students apply their knowledge gained in the coursework to the capstone in novel ways.

My Background

I am a software developer having 2 years of experience in software development.My interest in machine learning piqued in final year of my engineering degree in which i was making a project using machine learning techniques.Since then i am continuously adding machine learning skills to my portfolio.But until completing my nanodegree i had only theoretical knowledge of machine learning.Udacity’s MLND ensures that each term finishes with a project so as to give the student practical application of the gained skills.

Prerequisites

Udacity lists their prerequisites on the MLND homepage.

Program Structure

The Machine Learning Nanodegree program is now split up in two sub modules which was not there earlier.It facilitates a beginner to quickly pick up the track and an advanced to quickly choose the right course needed.

>Basics : Contains two technical project with minimal prerequisites.

>Advanced : Contains three technical projects including one capstone project.

Each project has video lectures and in-lecture quizzes for practice.Each project has its own rubric. Reviewers grade projects based on these rubrics. Students can submit as many times as they like. Project submission is either via Github repository or zipped files.

What is unique to Udacity is the peer review system in which the reviewers not only review your project submission but also give you additional friendly comments on strategy,machine learning trends and resources to read more about a particular topic of the project.Each project has a timeline duration in which you have to submit the project as github link or zipped files.

General Projects

I’ll briefly cover my version of the syllabus which may be different from what is there now as Udacity keeps on reviewing and updating the project and course content to keep it industry ready.

Each project container boilerplate python code in jupyter notebooks with instructions step by step so that you can start off quickly and code only what is required.

P0 : Titanic Survival Exploration(optional)

This is an optional project using the Titanic dataset from Kaggle. It’s a classification problem where we have to predict the survival outcome of passengers. It’s supposed to get students familiar with the submission and review cycle.

P1: Predicting Boston Housing Prices

This is one of the mandatory projects. It uses supervised learning techniques to predict the price of houses in the Boston area. It’s a classic dataset from the UCI Machine Learning Repository . The goal is to choose the best performing model using supervised learning.

P2 : Building a Student Intervention System

This is a classification problem where the task is to identify students who might need early intervention before they fail to graduate.This project has been replaced by a newer version in current curriculum.

P3: Creating Customer Segments

Unsupervised learning techniques like PCA and clustering are explored here with the wholesale customers dataset from the UCI repository.

P4: Train a Smartcab to Drive

The lessons in this project focus on reinforcement learning. The goal is to take a smartcab from a starting point to an end point by giving it feedback through reinforcement learning.

P5 : Classify images

This project implements deep learning methods to CIFAR-10 dataset using Tensorflow.

Capstone

The capstone is the final technical project in the MLND. It’s divided into two parts: proposal and project implementation. Students are supposed to choose a technical problem from a domain they are interested in, write-up a proposal, solve the problem using machine learning techniques, and submit a final report for the project.

This capstone project is what unique to Udacity.The proposal and implementation parts lets you document your project and your approach process so that you actually do a research on a particular topic just like writing a research paper.Its thoroughly reviewed by Udacity reviewers with friendly comments on places to improve.

I worked on the Kaggle Invasive species monitoring competition using Keras for my capstone. It was a binary image classification problem.It took me a lot of research on which model to use which technique to use and document it in proper format as given by the capstone project rubric.But this helped get to know the research paper writing process and its implementation and later I published my own research paper in a national conference on machine learning.

Certification

Students who successfully complete all the program requirements earn and receive their Nanodegree credential.

Support

Each Nanodegree program has its own discussion forums. MLND also has a slack channel where many former graduates give advice. Also with new Udacity 1 to 1 mentorship be it a small doubt about course or be it if you are stuck in your projects you will always find someone to help.Also the project reviewers comments on your submitted projects gives you their experienced knowledge that you will not find easily.

Overall

My experience with this program has helped me to learn how to research, experiment, and finish an open-ended machine learning project on my own. It taught me how to start a project from scratch,document it and explain it to someone else.As said earlier the reviewers were incredibly helpful and gave leads to many great resources that you won’t find just searching.

Enroll Now into Machine Learning Nanodegree here

About the Author | Divyansh Sharma

Engineer by profession,Writer by heart and Musician by soul.

My experience with Udacity’s Machine Learning Nanodegree Program was originally published in Udacity India Inc. on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s incredible how technology can change our everyday lives and impact humans so much. Some of the new technologies like Machine Learning and Deep Learning are playing a major role in detecting rare diseases like cancer, medical history, image analysis in radiology and pathology, and much more!

I am a Data Scientist with my expertise in the field of Machine Learning and Deep Learning. I am a Machine Learning Nanodegree as well as Self-Driving Car Nanodegree graduate from Udacity.

After my graduation, I had some offers from many big firms. I didn’t want to join any of them because I didn’t want to do what everyone else was doing. There was no course on Artificial Intelligence or Machine Learning in my university, but I knew that these were the real game changers. So, I started learning these things online. I started Machine Learning courses from other platforms. Then after completing it, I came across a free course on Deep Learning from Udacity. But, when I started doing Kaggle in August 2016, I realized that there is a lot of things that I am missing in applied Machine Learning. I thought to give a shot to Machine Learning Nanodegree as it covered a lot of aspects I wasn’t even aware of, for e.g., Reinforcement learning. I liked the content and the way in which it was taught. It was the first time I paid for an online course, but it was damn good in every aspect.
I opted for Machine Learning in Sep 2016, binge-studied, and finished it in Oct 2016.

Technology-wise, I am biased towards Machine Learning, and have much interest in that. Things made me interested in Self-Driving Car and its role in saving lives was a personal incident where I lost one of my very close friends. I believe autonomous vehicles could be the great public-health achievement of the 21st century. Driver-less Cars are poised to save millions of lives in future.

When Self-Driving Car Nanodegree was announced, I knew that it was a lot of money for an online course. But my past experience with Nanodegree was so good that I knew that it would be good no matter what. Also, some of my fellow Kagglers also opted for the same. To be very honest, I was going to opt out in the first week, but then I realized that this could be my chance to stand out from the crowd even without having MS or Ph.D. Also, as one of my friends suggested: “Look for the future. You will be years ahead of others by the time they get into this thing. ” I don’t think I could have made a better decision. Every aspect of Nanodegree was worth doing it.

There are a number of reasons why Udacity is much better than other platforms but some of the things that really make Udacity stand out of the crowd are:
1) Top notch content developed by tech giants: Yes, you heard it right. The content is developed by some of the big names in the industry. For example, term 2 in SDCND was developed by Mercedes-Benz while term 3 was developed by Nvidia. These are the things that engineers in these companies use for research and production. You don’t get this kind of content anywhere else.

2) Highly talented instructors: Not only the content is developed by tech giants, even the instructors are the highly talented engineers working in these companies. For example, during SDCND, term 1 was taught by Sr. engineers, and team lead at Uber whereas, term 2 was taught by the robotics team at Mercedes-Benz and term3 was taught by leading researchers at Nvidia.

3) Real-world projects: During my SDCND, we did Behavioral cloning, Vehicle detection and tracking in real time, path planning, semantic segmentation of road. All these projects are a part of a real self-driving car. Each of these is done at scale by self-driving car companies.

4) Hiring Partners: Udacity not only trains you for a particular field but it also helps you in getting a job in that field. There are dozens of hiring partners associated with each Nanodegree.

No other platform provides such things. And trust me, I have checked almost all good platforms for e-learning but, none compares to the standards of Udacity.

I think it’s the learning curve that matters the most. After doing the Nanodegrees, I found that my skills have leveled up a lot as compared to the people I was working with. I learned so many things in the field of robotics as well as Machine Learning or Deep Learning, while doing the Nanodegrees. In short, Nanodegrees has helped in polishing my skills. If you are currently enrolled into a Nanodegre, My advise would be “Don’t give up. Commitment and focus, that’s all you need while pursuing the Nanodegrees. Things can be overwhelming at times. Trust me, it is going to be worth it in the end. You don’t get such opportunities every day. Learn as much as you can. This is the time and this is the platform.”

Through the skills that I have acquired, I want to contribute to the society as much as I can. I want to apply my knowledge the fields of healthcare, self-driving cars, flying cars, drones, etc. These are the things that actually matter, create an impact, and would save millions of lives every year.

Saving lives through technology — Akash Kumar Nain was originally published in Udacity India Inc. on Medium, where people are continuing the conversation by highlighting and responding to this story.

For those who aren’t familiar, Kotlin is a programming language developed by JetBrains that targets the JVM and is 100% compatible with Java for Android development.

*Kotlin is actually capable of much more than just Android development. For more info checkout the language webpage.

On the mobile team here at Udacity, we’ve been using Kotlin in production for ~9 months, and have adopted it as our primary development language moving forward.

Recently, we’ve been asked a number of times about our usage of the language and why we’ve invested in it, so we thought we would share a bit about the following:

• Why we’ve invested in Kotlin
• Our adoption process
• How are we using Kotlin today
• Tips for incorporating Kotlin into your project

Why have we invested in Kotlin?

If you ask each member of our team this question, you’d get a slightly different response, but the general consensus would likely sound something like this:

“Kotlin provides a more enjoyable developer experience by allowing us to express our features & ideas using safer, more concise code. This, in turn, enables us to build a better product for our users”

More concretely, we believe Kotlin provides several key benefits to our team:

1. Improved crash rate
2. Reduction in lines of code written
3. Decreased time spent in code review
4. Renewed excitement around experimentation and innovation

Ultimately, our goal is to provide our users with the best learning experience possible. We believe these items help us do that.

How did we start?

Our adoption of Kotlin started through the recognition of “pain points” that we experienced on a regular basis. During code review, comments like this started to crop up often:

“this 10 line block could be done in 3 lines using Kotlin”
“it would be great here if we could avoid checking for null in 4 different places”

Having experimented with Kotlin outside Udacity, we knew the language might help us solve these problems and build better Android apps. Because of this, it wasn’t long before we had our first Kotlin PR

A small feature was implemented in Java, and a pull-request opened; but along with that PR another was created that provided the same feature; only written in Kotlin. The Kotlin PR wasn’t merged in that instance, but it demonstrated that we could easily integrate Kotlin into our existing app without compatibility issues or a slowdown in development time.

That was the jumping off point. We decided we would migrate a single, small feature in the app to Kotlin and see how it did in production.

We started by migrating our Settings screens in a separate develop branch. This branch received a lot of internal testing while we evaluated closely for 2 types of issues:

1. Usability issues: what impact did Kotlin have on crashes/bugs?
2. Development process: did Kotlin cause CI issues, slow down our build, or slow down feature development?

When nothing arose, we shipped the new Kotlin feature to users and crossed our fingers. Thankfully, as we expected, no major issues were found. We were now happily using Kotlin in production.

After that, we slowed down a bit. We loved using Kotlin, but recognized the potential risks of going all-in on an unofficially supported language. We continued to add/convert some tests to Kotlin, but no new feature code was using it.

Then Google announced support for Kotlin as a first-class language for Android development.

Team buy in

Google’s backing was the push we needed to fully embrace Kotlin.

We met with our PM and expressed our desire/plan to adopt Kotlin as our primary development language. Understandably, we were met with a healthy dose of pragmatism and asked a few important questions:

• Is the language stable/safe to be using now?
• Will it slow down development?
• Are there tangible benefits?

“what is the value prop?”

We benefitted here from having already successfully shipped production Kotlin, and could point to ourselves as an example of production usage
of the language.

After expressing our beliefs that we could write safer, less error-prone code with a Google-backed language, and that it wouldn’t slow down our development process we received the thumbs up we were looking for.

Migration plan

From this point we decided Kotlin would be our language going forward and created a migration plan:

1. All new dev work in Kotlin
2. If anything larger than a small bug fix must be done to a class, consider migrating the class to Kotlin
3. Start migrating existing code

Where feasible, the in-progress features began using Kotlin, and all new work began being written with it.

Our plan for migrating existing code, was to tackle the low hanging fruit first (utilities, helpers, isolated classes) then work towards migrating enough code that the majority of our daily development would be in Kotlin.

We identified the packages we thought were good candidates, and spent ~ 2 months migrating code while still pressing forward with new feature development.

For most Java classes, we started by using the Java to Kotlin conversion tool within Android Studio. We would then make manual adjustments focusing on a few things:

1. Avoiding the use of !! operator
2. When possible, convert helper methods to top-level functions
3. Avoid companion object where possible
4. Reorganize the generated code
5. Expressing code in a more Kotlin idiomatic way

Reevaluating our handling of nullability was probably the most time consuming part of these manual adjustments, but lead to some of the greatest wins regarding stability and code maintenance.

In many places we focused on leveraging statements such as object?.let {...} rather than using if(object != null) { ... }.

We also tried to leverage the great collections stdlib that Kotlin provides. These apis allowed us to replace large blocks of code with a few lines of concise functional code.

When used together, these two features of Kotlin allow us to write code like this:

recentQueries
    ?.filterNotNull()
    .filter{ it.isNotBlank() }
    .take(5)

This snippet is short, easy to follow and has far fewer possible points of error than the equivalent Java code would have.

Where are we today?

Today we have about 1/3 of our main codebase written in Kotlin. As we touch features we continue to migrate that code, but we’ve slowed down on migrating existing code.

This is largely due to us shifting dev cycles to several experiments that are being carried out in separate codebases.

These new codebases are 100% Kotlin, and will ideally stay that way.

Working in an exclusively Kotlin codebase also means we don’t have to concern ourselves as much with java interoperability. This allows us to more fully explore the language.

Our aim is to discover and try what is possible with the language, rather than simply writing our familiar Java code; but in Kotlin.

We’ve enjoyed experimenting with new patterns that may have been not possible, or too cumbersome in Java. For example, the freedom to have top-level functions or easy property delegation have lead to cleaner and more semantically correct code.

Tips for incorporating Kotlin

Having gone through the process of incorporating Kotlin into an existing codebase, migrating a large portion of the codebase to Kotlin, and starting a new project from scratch using 100% Kotlin; here are a few tips that we’ve learned along the way.

• If new to the language, start with the conversion tool
• Don’t be afraid to further modify the converted code
• Starting with tests can help work out any issues with your build and start giving a feel for the language, but writing feature code will give a more comprehensive picture of how it can affect your code
• Don’t go wild trying to write “idiomatic” Kotlin. As you progress with the language, it’s likely your opinion of whats readable vs what’s possible will evolve.

Just because you can doesn’t mean you should

• As you migrate existing code, re-visit previously converted code from time to time. You may find that newly discovered conventions/patterns could be applied.
• Don’t forget to thoroughly test any interop layers between Java and Kotlin. Nullability issues can crop up during runtime when dealing with nullableplatform types.

Explore, experiment, migrate, refactor

Lastly, take advantage of this period of exploration while learning the language. Question existing patterns/conventions for writing Android apps and ask yourself if/how Kotlin might enable you to write alternative solutions.

— can your code be refactored to avoid null properties?

— can you replace helper classes/methods with top-level functions?

— can you avoid handling null properties by using a delegate?

— could a logical path be expressed using functional concepts to reduce a class’s state?

How can Kotlin help improve your product?

Confidence moving forward

We are very comfortable with our investment into Kotlin for the foreseeable future.

The language and tooling continue to improve. Between JetBrains, Google, and the community, there is a great team behind improving these developer stories.

As documentation and samples continue to be added for the language, it will become increasingly less daunting to try Kotlin which we believe will increase adoption and make it an even more viable option going forward.

We look forward to further exploring Kotlin and everything it can do to help us build great education products for our users.

Want to learn more about Kotlin?

Kotlin FAQ

Kotlin on Android: Now Official

Kotlin for Android development

What can Kotlin do for me?

This blog was originally published by Nate Ebel for Udacity Engg & Data Medium channel.

Adopting Kotlin — Nate Ebel was originally published in Udacity India Inc. on Medium, where people are continuing the conversation by highlighting and responding to this story.