Data Science & Machine Learning Masterclass

Develop strong practical skills while building an impressive portfolio.

Letter to Prospective Students

Dear Prospective Student:

If you're like many other developers, analysts, or aspiring data scientists, there's a good chance you've felt overwhelmed in the past.

You want to learn data science, but the moment you dove into this world of machine learning you were flooded with jargon, math formulas, and hundreds of different concepts.

And as a result, you simply don't know where to start, or how to bring everything together coherently.

Maybe you've tried watching lectures on YouTube... and now you know some neat theory, but you're still unsure how to apply each algorithm in practice.

Or, maybe you've browsed online forums for advice, only to discover that you have a wall of pre-requisites ahead of you: Linear Algebra this... Statistics that...

We totally get it and we want you to know that what you're feeling is completely normal.

Think of it like this...

You've basically opened up a jigsaw puzzle and poured all 500 of the jumbled pieces onto your table.

It's a confusing mess that makes no sense.

Jigsaw Puzzle

How it feels to start down the path of data science.

Trying to piece it all together on your own is possible, but it will take you a very long time.

Thousands of other students who dive into this world feel the exact same sense of confusion and overwhelm that you might feel. In fact, many of those students eventually give up.

As practitioners, it pained us to see students give up because we know it's not as complicated as it seems.

Well, we were determined to be part of the solution, instead of adding to the problem. Since launching this site, we've helped thousands of students learn this exciting skill with our easy-to-follow tutorials and courses.

Our #1 Tip:

Our number 1 tip for mastering applied machine learning without feeling overwhelmed is to learn in context by tackling interesting projects.

You see, when we were young children, we were able to build very large vocabularies by reading books and learning new words in context. Yet, for some reason, machine learning is often taught in a silo'd and piecemeal way...

Mastering a hard skill like machine learning can hugely benefit from contextual learning.

Yes, some theory is important. Yes, you need to know about various algorithms. And yes, you need basic programming chops to get started.

But by jumping into projects sooner (rather than later), you'll benefit in 3 major ways:

  1. First, you won't burn out or get bored as easily. This is a hidden pitfall that's often overlooked in self-study programs. If you don't feel excited to continue, then nothing else matters...
  2. Second, your practical skills make picking up theory easier. The best textbook in the world can't compete with rolling up your sleeves and testing the concepts for yourself...
  3. Finally, you'll learn much faster... When you get to practice each step within the big picture, you'll be learning in context.

Instead of watching additional lectures, which basically dump even more puzzle pieces into your lap, it's much easier to learn by doing.

It's what will allow you to gain confidence, and it's what will allow you to develop a tangible, valuable skill.

Completing entire projects from start to finish is the fastest way to learn data science and machine learning.

The Machine Learning Workflow

Unless you wish to become a researcher who scrutinizes individual algorithms hoping to discover nuances, you'll achieve more by building strong, practical intuition...

Applied machine learning emphasizes efficiency and effectiveness because results can be quantified.

Therefore, if your goal is to use machine learning in your work or to become a data scientist, then the best advice we can give you is to learn how to apply the right algorithms, the right place, and in the right way.

Rest assured...

You can pick up the essential theory and concepts along the way, as long as your projects are comprehensive and have a carefully planned progression.

If you'd like mentorship for machine learning and data science, we'd be happy to teach you what we know...

Earlier this year, we lovingly crafted a complete course on practical machine learning and data science.

  • If you want to learn machine learning skills efficiently...
  • If you are tired of feeling overwhelmed...
  • If you want real-world skills that are in high demand...
  • And if you want to learn through fun, hands-on projects...

Then we sincerely believe you'll enjoy our course.

Sincerely,

~ EliteDataScience Team

Course Overview

Learn how to build professional-grade machine learning models.

Our masterclass is laser-focused on building effective models. We will teach you the entire machine learning workflow, and you'll get to implement it on multiple real-world datasets.

This course is self-paced and can be completed in about 30-50 hours. You will have lifetime access to the content, so you'll never feel rushed.

Reliable and Valid Results
Interactive Machine Learning Projects

Enjoy 4 interactive, portfolio-ready projects.

This course features 4 epic, hands-on projects that you'll complete through interactive Jupyter Notebooks. This approach is more fun, and you'll learn in context. Plus, these projects look very impressive in your portfolio (yes, we cover data visualization, too).

Each of the projects would be considered "capstone projects" in other courses.

Gain valuable skills that can accelerate your career.

Practice tackling real business challenges. You'll get to see how ML can help in e-commerce, HR analytics, and even real-estate valuation.

Whether you're an aspiring data scientist or you just want to apply machine learning in your current work, this course will provide you the right skills.

Machine Learning Salary
Learn from real data scientists

Get practical advice from data scientists, not from professors.

Get down-to-earth advice straight from professional data scientists. We exclusively focus on actionable tips and industry best practices.

By the end of the course, you will be self-sufficient thanks to carefully designed progression and mentorship.

Projects

Project 1: Meet the Quartermaster

Project 1: Meet the Quartermaster

If you've watched any of the classic James Bond films, you'll know that every successful mission requires at least two elements:

  1. A witty action star.
  2. Cool gadgets, sexy cars, and reliable tools (provided by Q, the quartermaster).

So who's the action star in this course? It's you, of course! 

And you guessed it... this first project is all about equipping the best tools for the mission.

Think of this as an "orientation project on steroids." We'll introduce the big picture concepts and the libraries that will help us throughout the course.

Includes:

  • A beginner-friendly crash-course on Python.
  • The best tools and libraries for machine learning.
  • The big picture concepts every practitioner must understand.
  • How to plan an efficient machine learning workflow.
  • Exactly where all the moving pieces fit in.
  • And much more...
Project 2: Real-Estate Tycoon

Project 2: Real-Estate Tycoon

What does it take to become a real-estate tycoon? A huge bank account? Shark-like negotiation skills? Connections in every corner of the town?

Well, those certainly help (a lot!)... But what you definitely need is the ability to value houses accurately for spotting good deals...

And that's what we'll tackle in our second project... We'll apply machine learning to create a pricing model using data from thousands of homes.

After Project #2, you'll find that so many previously confusing concepts will just "click" because you'll get to see the entire ML process from start to finish, with special emphasis on the algorithms used for Supervised Learning: Regression.

Concepts Include:

  • Practical comparison of regression algorithms.
  • How to perform exploratory analysis like a pro.
  • Exactly when to apply different data preprocessing steps.
  • How to sample and split your data the right way.
  • The #1 guideline for choosing the best model for the problem.
  • And much more...
Project 3: Employee Retention

Project 3: Employee Retention

Data science and machine learning are revolutionizing many fields in exciting ways. One of the hottest uses for machine learning right now is in human resources (HR) analytics.

For many companies, their most challenging task is finding, training, and keeping talented employees. Therefore, it's in their best interest to identify employees likely to leave and then bribe them with a pay raise proactively address their concerns.

In this project, you'll build ML models for predicting employees likely to quit based on factors such as the average number of hours worked per week and the time since their last promotion.

In Project #3, you'll take your skills to the next level by stepping into the driver's seat and learning more advanced concepts, with emphasis on algorithms for Supervised Learning: Classification.

Concepts Include:

  • Practical comparison of classification models.
  • How to perform feature engineering like a pro.
  • The easiest way to avoid overfitting your model.
  • How to evaluate your model using different error metrics.
  • Smart shortcuts that can save you hours of work.
  • And much more...
Project 4: Customer Segments

Project 4: Customer Segments

Successful businesses use ML to create win-win situations for themselves and their customers. One way is to use clustering algorithms to create better customer segments.

This allows companies to provide better support, cut marketing waste, and offer more relevant products. Companies save money. Customers save more time to watch cat videos on YouTube. Win. Win.

In this project, you'll use unsupervised learning to create customer segments from a cool online retail dataset. This project is especially relevant for e-commerce and digital marketing.

Project #4 will round out your machine learning skillset. Plus, you'll learn how apply Unsupervised Learning algorithms to solve meaningful business problems... That is a true badge of honor, even among ML professionals!

Concepts Include:

  • Practical comparison of clustering models.
  • How to perform dimensionality reduction like a pro.
  • Easy, yet effective ways to visualize your data.
  • How to create business value from your data.
  • Advanced techniques for data wrangling.
  • And much more...

Alumni Reviews

I hadn't heard of EDS before and wondered if it would be high quality, but the course has been great. Very well put together, easy to follow, and I feel like I'm learning effectively. I like that it's very interactive - learn by doing rather than lectures.

The course has exposed me to things I would not learn in my job but are highly relevant to the analysis I do every day. The things I've learned in this course have been a differentiator for me in my day job.

Zachary Washam
Zachary Washam Investment Banking Analyst, USA

I just wanted to learn the best contents and not waste my time. I am familiar with Javascript and R, but seldomly use python.

With your course, I like that I can learn stuff in a humorous way. It keeps me studying and I never lose interest. After taking your course, I learned how to solve problems in an efficient way. It's good for Data science and learning python, and it is time saving!

Yunbum C.
Yunbum C. Nuclear Engineer, South Korea

LOVE the practical approach vs. spending a ton of time on theory... and I gained a solid understanding of ML and how it could be applied to my business. Your practical approach is a great way to learn these complex concepts.

Jeffrey Stilwell
Jeffrey Stilwell Startup Founder, USA

I've been let down by online courses before, and I was weary of sinking money into something else. I'd tried MOOC Machine Learning classes before, but they focused on algorithms too much.

I fell right into the driver's seat — the seat where you LEARN! — with your course. I found it very hard to lose gumption through the course because the topics built upon each other well, and you always had those cheat sheets if I was really stuck.

Now, I'm no longer totally opaque about the machine learning space, and the course reinvigorated my motivation for learning.

Kash Nouroozi
Kash Nouroozi Software Developer, Canada

My intention by buying this course was to have an overview of Data Science. I don't believe in Certifications but I believe in company networks that validate the content.

After joining, I found that the course is case based and well structured. Therefore the content is good and progressive. I also liked the fact that we can access the course whenever we want.

Valentino Doria
Valentino Doria Student, Belgium

Pricing Comparison

Course Access
Self-Study
Python & Programming
Programming Basics
NumPy
Pandas
Matplotlib
Seaborn
Scikit-Learn
Git / Github
Supplemental Theory
Big Picture Concepts
Descriptive Statistics
Inferential Statistics
Applied Machine Learning
Exploratory Analysis
Data Wrangling
Data Cleaning
Feature Engineering
Regression
Classification
Clustering
Time Series
Dimensionality Reduction
N.L.P.
Model Validation
Special Features
1:1 Video Calls
Accessible Without Math Expertise
Interview Review
End-to-End Projects
Money-Back Guarantee

$295 One Time

Machine Learning Masterclass

  • Course AccessLifetime
  • Self-Study
  • Python & Programming
  • Programming Basics
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit-Learn
  • Git / Github
  • Supplemental Theory
  • Big Picture Concepts
  • Descriptive Statistics
  • Inferential Statistics
  • Applied Machine Learning
  • Exploratory Analysis
  • Data Wrangling
  • Data Cleaning
  • Feature Engineering
  • Regression
  • Classification
  • Clustering
  • Time Series
  • Dimensionality Reduction
  • N.L.P.
  • Model Validation
  • Special Features
  • 1:1 Video Calls
  • Accessible Without Math Expertise
  • Interview Review
  • End-to-End Projects
  • Money-Back Guarantee30-Day

$499 / mo. Per Month

Data Science Intensive

  • Course AccessMonthly
  • Self-Study
  • Python & Programming
  • Programming Basics
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit-Learn
  • Git / Github
  • Supplemental Theory
  • Big Picture Concepts
  • Descriptive Statistics
  • Inferential Statistics
  • Applied Machine Learning
  • Exploratory Analysis
  • Data Wrangling
  • Data Cleaning
  • Feature Engineering
  • Regression
  • Classification
  • Clustering
  • Time Series
  • Dimensionality Reduction
  • N.L.P.
  • Model Validation
  • Special Features
  • 1:1 Video Calls30 minutes / week
  • Accessible Without Math Expertise
  • Interview Review
  • End-to-End Projects
  • Money-Back Guarantee7-Day

$4,950 One Time

Part-Time Data Course

  • Course Access10 Weeks
  • Self-Study
  • Python & Programming
  • Programming Basics
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit-Learn
  • Git / Github
  • Supplemental Theory
  • Big Picture Concepts
  • Descriptive Statistics
  • Inferential Statistics
  • Applied Machine Learning
  • Exploratory Analysis
  • Data Wrangling
  • Data Cleaning
  • Feature Engineering
  • Regression
  • Classification
  • Clustering
  • Time Series
  • Dimensionality Reduction
  • N.L.P.
  • Model Validation
  • Special Features
  • 1:1 Video Calls
  • Accessible Without Math Expertise
  • Interview Review
  • End-to-End Projects
  • Money-Back Guarantee
  • I found this course to have an excellent layout and approach in comparison to traditional 'bootcamps'. The "hands-on" approach with excellent explanations on academics where needed is the best I have ever seen. Your course helped someone who has limited knowledge on statistics and primarily with a software development background learn ML techniques.

    The course delivers. This one of the best I have ever seen in terms of teaching (including other areas outside of Data Science). I can see that a lot of effort and heart went into creating this course.
    Mike R.Software Developer (Cloud native apps), USA

30-Day 'Triple Guarantee'

Within 30 days of purchase...

  1. If you are disappointed with the projects...
  2. If you don't feel like you're learning useful skills...
  3. Or even if you just hate the font we use in the course materials...

Simply email us for a full refund, no questions asked. If you don't learn, we don't want to earn. It's that simple.

Click the link below to take your next step forward for applied machine learning. You're fully protected by our 'Triple Guarantee,' and we will happily shoulder that risk because we believe in this product.

Enroll Today for $295

Use code CELEBRATEMAY to get $100 off at checkout.

You'll have instant access to the course dashboard, and you can start on Project 1 within minutes.

If you've been looking for a clear, efficient, and comprehensive way to learn machine learning, you've found it.

Let's do this together.

  • Before enrolling, I was concerned about the cost, and I wasn't sure if the material would be useful to me, but your course exceeded my expectations:

    1. Step-by-step instructions - I never felt like I was getting lost.

    2. Explanations - you give insight about why certain things are done. That knowledge and intuition is extremely valuable in helping to build a solid understanding.

    3. Comprehensive - we go through the entire data science process, not just a single part of it, so we can see how everything ties together.

    4. Jupyter - the interactive notebooks are great, and you also teach us to use things currently being used in the industry.


    I recommend this course to others because it's project based learning, not just straight theory, which really differentiates you guys. Things are comprehensive, clearly-explained, and overall, a really high quality class.
    Annie T. Software Developer, USA

Complementary Upgrades

Upgrade #1: Quickstart Setup

First, you'll get the most stress-free way of getting started with machine learning, thanks to...

  1. Step-by-step guide to setting up your data science workstation.
  2. Companion Workbooks for each project, where you can write all your code.
  3. All datasets included.

You'll spend less time setting up and more time learning.

Bonus Quickstart Guide
Machine Learning Masterclass Companion Guide

Upgrade #2: Companion E-Book

Plus, review will be a breeze with our companion e-book rich with notes, code, and images. You'll have all the key concepts from each project all in one place, so you can focus on enjoying the course instead of taking notes.

Tip: This guide is also an awesome way to prepare for interviews.

Upgrade #3: Premium Support

Next, you'll get access to a premium support channel. Enjoy ultra-fast turnaround times on any questions that arise, even over the weekend.

Think of this like "on-demand office hours."

Unlike other self-paced courses that dump a bunch of videos into your lap and say 'good luck,' we are fully committed to helping you succeed.

Premium Support
Lifetime Access and Updates

Upgrade #4: Lifetime Access & Updates

Finally, unlike bootcamps where you only have access to the material for a limited time, you'll get lifetime access to the course dashboard, including all future updates.

Machine learning is a rapidly evolving field, and a great machine learning course should evolve as well.

This course will never be outdated, and you're warmly welcomed back at any time.

FAQ

Do I need to know Python to take this course?

No. In fact, some of our students do not even have prior coding experience.

Our comprehensive Python Crash Course teaches the essentials needed for machine learning. It starts with programming fundamentals (which you can skip if you have prior experience) and then covers Python syntax, libraries, best practices, and examples.

How does this compare to Andrew Ng's machine learning course (at Stanford)?

We think Andrew Ng’s machine learning course is fantastic. However, it’s heavy on theory and very light on practice. Andrew’s course will give you a good understanding of the math, but applying those algorithms the right way is a whole ‘nother beast.

This course is the missing link between academic theory and the real-world best practices professionals are actually using. It’s also beginner-friendly, as we cover all the essential concepts.

How much time investment is required?

The course is designed to take about 30-50 hours to complete, depending on your background. Most students finish the course in 1-3 months, part time. The course is self-paced, and you get lifetime access, so you can find the best pace for you.

Can I list these projects on my resume/LinkedIn profile?

Yes, absolutely. We want you to learn the skills and walk away with some shiny trophies to show for it.

What if I don't like the course?

Our philosophy is very simple: if you don’t learn, we don’t earn. If you’re unhappy with the course for any reason, simply email us within 30 days, and we’ll give you a full refund, no questions asked.

Will this help me become a data scientist or ML engineer?

Short answer: Yes, very much.

Long answer: This course was created to help anyone in any industry apply machine learning to get professional-grade results. Companies want to see if you can deliver value, so these business-oriented projects are 100x more impressive than academic ones.

Is this course compatible with Python 3?

Yes, this course is compatible with both Python 2.7+ and Python 3. In fact, our bonus Quickstart Guide will help you painlessly get set up with either version of Python and all of the required libraries.

What if I have another question?

Send us an email, and we’ll be happy to answer it! You can reach us at support [at] elitedatascience [dot] com.

  • I am 20% into the course so far so good. I feel course contents has reduced the complexity in understanding machine learning concepts.

    Content is crisp not too much of reading theory which is usually what I found in others. Though it is important but lots of examples with associated theory is fruitful. So I think I will complete this course unlike others which I had opted into earlier.
    Hima M.Java Software Developer, India
  • I am not too experienced with Python and was unsure to what extent it would be covered in the course. But I was very impressed with the Extended Python Course.

    It is very helpful to follow the examples in the Jupyter notebook, as it helps solidify the topics. I am learning a lot about Python as well as Pandas and NumPy. Most importantly, it makes understanding the concepts easy due to the language used and accompanying examples.

    Also, I would like to thank everyone that put this course together. It has been very entertaining and I am glad I have bought the course.
    Ricardo C.Analyst, Canada
  • The course had everything I needed. It's practical, and very good value for the money.
    Christophe P.Business Analyst and Project Manager, Switzerland
  • My favorite part was how the companion ebook is not simply a repetition of the course text, but a very good summary.
    Istvan KorcsmarosBI professional, Hungary
  • I was worried about receiving similar content to other courses like udemy or Andrew ng YouTube series.

    But after enrolling, I found that the steps were very detailed which i like because often courses will skip over what the instructor thinks is obvious but not to the student.

    I'd recommend this course to others. Great price value. Learn a lot. Get hands on experience.
    Terry ZhangStudent, Canada