Machine learning is a lucrative niche in the data science industry – and there's more than one way to make your entrance. Some chose to carve their place in the machine learning space following the more traditional way – enrolling in a university or college – while others might go for a more unconventional approach. And if you consider yourself the latter, our overview of DataCamp machine learning courses is here to help.
If you're not familiar with DataCamp yet – well, we'll surely rectify that today. However, for a brief introduction, it's one of the biggest and best-rated online learning platforms for data science and analysis. It's known for its courses covering topics like Python programming, business analysis, and – yes – machine learning for everyone, from complete beginners to advanced learners.
Our focus today will be on the DataCamp Machine Learning Fundamentals with Python skill track. We'll cover each course, one by one, in the order that they follow on the track. However, your learning journey won't end there – I'll also show you a few courses you can take alongside the skill track to round up your machine learning knowledge.
But before we get to it, let's first see why taking DataCamp machine learning courses is worth it.
Table of Contents
- 1. Why Should You Choose DataCamp Machine Learning?
- 2. Getting Started
- 2.1. Understanding Machine Learning (Enroll HERE)
- 3. Machine Learning Fundamentals with Python
- 3.1. Supervised Learning with scikit-learn (Enroll HERE)
- 3.2. Unsupervised Learning in Python (Enroll HERE)
- 3.3. Linear Classifiers in Python (Enroll HERE)
- 3.4. Introduction to Deep Learning in Python (Enroll HERE)
- 4. More DataCamp Machine Learning, More Fun
- 4.1. Machine Learning for Business (Enroll HERE)
- 4.2. Machine Learning for Finance in Python (Enroll HERE)
- 5. What about R?
- 5.1. Machine Learning in the Tidyverse (Enroll HERE)
- 5.2. Machine Learning for Marketing Analytics in R (Enroll HERE)
- 6. Conclusions
Why Should You Choose DataCamp Machine Learning?
You might be wondering – what makes DataCamp machine learning courses stand out compared to other learning platforms? After all, with so many options out there, why is this the platform you should choose to get started with your machine learning journey? Let's unravel this from two sides – why you should pick DataCamp in particular and why picking up machine learning is worth it.
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We'll start with the latter question. Machine learning is certainly not a new field in data science. It's been around since roughly the late 1950s, and the term itself was coined by the developer Arthur Samuel who worked on some of the first self-learning programs. However, in recent years, machine learning has become increasingly important – all thanks to social media.
Of course, platforms like Facebook or TikTok are not the only reason why machine learning experts are in such high demand. However, since this field is closely tied to artificial intelligence and the use of algorithms, it's understandable why social media platforms are often the focus. After all, machine learning handles the continuous processing of large qualities of data, which computers then 'learn' from.
How lucrative is machine learning, exactly? Well, from the salary perspective alone, an entry-level position can lead to you earning over $96,000 annually, while professionals with more experience can exceed a yearly average wage of $110,000!
Machine learning engineers are highly sought after by FAANG companies – that's Meta (the F for Facebook has stuck in the acronym), Amazon, Apple, Netflix, and Google (or Alphabet). These companies are infamous for their handling of various user algorithms and implementation of AI technology.
So, why DataCamp machine learning courses specifically? Well, DataCamp is one of the highest-rated platforms for learners interested in data science and data analysis. It offers its users over 390 courses, ranging from introductions to programming languages to more advanced data management tools.
Additionally, with DataCamp, you can access hundreds of projects and case studies that use real-world data and grant you hands-on experience that lets you put your skills to the test as soon as you've acquired them. The learning process is based on the principles of gamification – it's interactive, combines a broad scope of visual content, and even uses XP-gaining mechanics to make things more fun for you.
And we can talk about the price before we even get started with the courses. The Free plan grants you access to several courses completely for free, and you can try out the first chapter of all other courses on the catalog. However, the Premium plan is where it's really at.
At $25/month, the Premium plan unlocks the entire catalog – that's more than 390 courses, every single career and skill track, projects, employment-ready programs, certificates – I could go on but there are some surprises for you to discover on your own. Additionally, you can find some special DataCamp discount codes here.
So, now that we're set up, let's see where the DataCamp machine learning journey begins.
Getting Started
To start our DataCamp machine learning review (though an 'overview' is perhaps more accurate), I'm going to assume that you have no prior experience with this field. If you have, you can feel free to skip this section and jump ahead to the Skill Track section. However, it's worth sticking around, as our first course might help you get the hang of how DataCamp works.
Before you settle on the specifics – do you plan on working with R or Python, will you use it for scientific research or financial analysis, and so on – you need a solid introduction to the field and its main concepts. This first DataCamp machine learning course is set to do just that.
Understanding Machine Learning (Enroll HERE)
- Offered by: Lis Sulmont, Hadrien Lacroix and Sara Billen
- Duration: 2 hours
- Price: FREE
- Certificate: Yes
- Level: Beginner
- Where to apply? HERE
Machine learning can seem intimidating at first, especially if programming isn't your strongest suit. However, a lack of coding skills is not something that should keep you from learning more about this field if you're interested in it. Even if you end up deciding that it's not for you, taking this DataCamp machine learning for beginners is worth considering.
Understanding Machine Learning is a two-hour, coding-free course that covers the essentials of this data science field. Its goal is not to teach you how to do the technical tasks – that's what the DataCamp Machine Learning Fundamentals with Python skill track will be about. This course, on the other hand, makes sure to answer all the burning questions you might have about machine learning.
The course consists of three chapters:
- What is Machine Learning? – as the name implies, this chapter will provide you with the definition of machine learning. You'll learn how machine learning differs from AI learning, what the essential industry lingo is, and what myths surround this field. You'll also get a chance to work on building a simple machine learning model;
- Machine Learning Models – you'll cover the principles of supervised and unsupervised machine learning and what strategies the two require. You'll also see how performance evaluation works and how you can use the feedback to improve your learning models;
- Deep Learning – in the final chapter, you'll be introduced to the concept of deep learning. You'll briefly cover how neural networks function and see two of the most common use cases for deep learning – computer vision and natural language processing.
Once you finish the course, you'll have a good understanding of all essential terminology related to machine learning. You'll be guided by a trio of instructors – Lis Sulmont, Hadrien Lacroix and Sara Billen.
Two of the instructors are part of the DataCamp team – Lacroix is the curriculum manager and Billen is a data scientist – while Sulmont is the content program manager at the language learning platform Duolingo. So, they're more than aware of what online learners might need, and how machine learning systems are used in practice.
And here's a treat for you – the Understanding Machine Learning course is free! So, if you're not ready to commit to the full skill track yet, you can finish this course and see for yourself whether machine learning is the right field for you.
Your DataCamp machine learning journey can begin as soon as today – and you don't need to be a programmer to get started. So, sign up, and begin!
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Machine Learning Fundamentals with Python
Skill and career tracks are perhaps one of the most learner-friendly features that DataCamp has to offer. Getting started with programming, data management, and other skill development can be tricky. So, to make things simpler, DataCamp has arranged a number of career and skill tracks to help you find the right path to professional and self-development.
One such path is the DataCamp Machine Learning Fundamentals with Python skill track. This four-course program will help you gain the foundational programming skills that every machine learning engineer must know as they enter the job market. It'll last 16 hours to help you master machine learning model development using the scikit-learn Python library.
Let's take a look at the courses available on this DataCamp machine learning Python skill track.
Supervised Learning with scikit-learn (Enroll HERE)
- Offered by: George Boorman
- Duration: 4 hours
- Price: $25/month
- Certificate: Yes
- Level: Beginner
- Where to apply? HERE
While you can understand the principles and terminology related to the field without having typed a single line of code in your life before, at the end of the day, this field requires an extensive understanding of how programming languages – particularly Python and R – work. In this case, we'll be focusing on the DataCamp machine learning Python courses.
Python is among the most widely used programming languages in the world, so naturally, there are numerous libraries for you to utilize. scikit-learn (formatted in all lowercase) is among the most popular options for machine learning engineers. It's open-source software that features all the tools you'll need, from basic to advanced data clustering algorithms.
The Supervised Learning with scikit-learn course is going to introduce you to this library and show you how you can take advantage of the resources at hand. In the four hours that'll take you to complete this course, you'll work with real-world databases and get a taste of how machine learning is handled by professionals.
Your four courses are:
- Classification – you'll learn about classification problems and how supervised learning is used to solve them. Your first practical task will involve determining the churn status of a telecom company;
- Regression – you'll be introduced to the concept of regression and how it's used to build learning models that are used to predict sales values. You'll work with two types of regression – linear and regularized;
- Fine-Tuning Your Model – your models will handle large quantities of data, so you need to train them to filter out anything that's unnecessary. This chapter will teach you how to evaluate your machine learning models and use visualization tools for performance analysis;
- Preprocessing and Pipelines – you won't always have all the data you need. So, it's best to learn early on how to handle missing values. In the final chapter of this course, you'll learn the essentials of streamlining your workflow – from data scaling to simultaneous evaluation of several supervised learning models at once.
Essentially, after completing this course, you'll be able to do a few things. For starters, you'll know how to navigate scikit-learn and utilize the right model management resources. You'll also know how to handle the models themselves and fine-tune them based on your personal requirements.
The course is taught by George Boorman, who's DataCamp's Analytics and Data Science Curriculum Manager. Boorman has worked with data management in various fields, including health and applied research, so everything you learn will be grounded in real-field experience.
To complete this course and get started with your DataCamp Machine Learning Fundamentals with Python journey, you'll need to sign up for the DataCamp Premium plan. At just $25/month, you'll have access to the full course catalog, as well as a broad range of other handy resources.
Unsupervised Learning in Python (Enroll HERE)
- Offered by: Benjamin Wilson
- Duration: 4 hours
- Price: $25/month
- Certificate: Yes
- Level: Beginner
- Where to apply? HERE
Supervised machine learning? Check. Naturally, there's the other side of the coin that we should take a look at now – unsupervised machine learning. The difference between supervised and unsupervised learning is that the former is set to predict patterns, while the latter is used to discover new, hidden patterns using unlabeled data.
The Unsupervised Learning in Python course will help you get the hang of how this works. Once you take this four-hour course, you'll learn a whole range of model management activities – with unlabeled datasets, you'll see how to cluster, extract, visualize, and transform new insights. You'll complete the following four chapters:
- Clustering for dataset exploration
- Visualization with hierarchical clustering and t-SNE
- Decorrelating your data and dimension reduction
- Discovering interpretable features
The names might appear complicated at first sight, but it's all actually a lot easier than you might fear. In the first chapter, you'll learn how you can find clusters in your datasets and evaluate them. Moving on, you'll try your hand at two data visualization techniques – hierarchical clustering and t-SNE.
Chapter 3 explores ways to conduct dimension reduction techniques. This is done to summarize your datasets by discovering the patterns among them. Finally, you'll work with a specific dimension reduction technique, Non-negative matrix factorization (NMF), to express your samples as various combinations.
As you learn, you'll work with examples that use real-world data so you can get some easy hands-on experience with unsupervised data management. This data was provided by your instructor, Benjamin Wilson. He's a machine learning specialist with a PhD in mathematics, as well as the director of research at lateral.io. He brings his academic and professional prowess to the course.
If you're following the skill track in order, I don't need to remind you this – however, if you want to focus solely on unsupervised learning, the DataCamp Premium plan at $25/month is the key to access every chapter and once you're done, the certificate of completion. So, go ahead and get started!
Linear Classifiers in Python (Enroll HERE)
- Offered by: Mike Gelbart
- Duration: 4 hours
- Price: $25/month
- Certificate: Yes
- Level: Intermediate
- Where to apply? HERE
Linear classifiers are models often used in machine learning to predict the values of a certain category within a line (for example, the x-axis). They are a foundational part of machine learning algorithms, as they can be trained and tuned using specific tools. To help you learn how to handle linear classifiers, DataCamp is offering the Linear Classifiers in Python course.
You'll once again be working with scikit-learn here. As you start this course, you'll have a quick refresher to get you up to speed and prepare for this next step in your learning. The Linear Classifiers in Python course lasts four hours and consists of four chapters:
- Applying logistic regression and SVM – you'll cover the fundamentals of using logistic regressions whenever you encounter classification problems. You'll also be introduced to the concept of support vector machines (SVM);
- Loss functions – in this chapter, you'll cover the framework of how logistic regression and support vector machines work. You'll also learn how to implement logistic regressions;
- Logistic regression – you'll take a closer look at logistic regressions. This chapter focuses on model output interpretation and probabilities;
- Support Vector Machines – finally, you'll learn more about support vector machines. You'll see what hyperparameters they contain and how you can tune them based on your needs.
By the end of this course, you'll be able to confidently apply linear classifiers to your machine learning models and tune them according to your own data research. The course and its tasks were developed by Mike Gelbart, who teaches at the University of British Columbia. During his own studies, Gelbart focused on hyperparameter optimization for machine learning.
If you're ready to start taking the Linear Classifiers in Python course, don't forget to take a look at our DataCamp discount codes here. Take your algorithms to the next level by adding logistic regression and support vector machines to your machine learning toolkit.
Introduction to Deep Learning in Python (Enroll HERE)
- Offered by: Dan Becker
- Duration: 4 hours
- Price: $25/month
- Certificate: Yes
- Level: Beginner
- Where to apply? HERE
To be a bit biased, if I were to write a DataCamp machine learning review based on course titles alone, this one would certainly stand out as the coolest. Neural networks, natural language processing, computer vision – doesn't it sound like a sci-fi film?
If you're as curious about deep learning as I am, why not check out the final course on the DataCamp Machine Learning Fundamentals with Python skill track – Introduction to Deep Learning in Python?
This course will teach you everything you need to know about this sub-branch of machine learning. Deep learning is widely used in niches like robotics, artificial intelligence, and the aforementioned natural language procession – and qualified deep learning engineers are in high demand. So, taking this course will work in your favor.
This four-hour course covers four chapters:
- Basics of deep learning and neural networks
- Optimizing a neural network with backward propagation
- Building deep learning models with Keras
- Fine-tuning Keras models
At first, you'll be introduced to the concept of deep learning and what it encompasses. Although it's a difficult field, you won't get too overwhelmed – the course is beginner-friendly and acts as the starting point for several other deep-learning skill tracks.
Once you've built your first simple neural network, you'll move on to optimization. You'll be introduced to the mechanics of backward propagation and get to see how it works in practice. Your learning will involve the Keras library which is an open-source deep-learning API for Python. Finally, you'll see how you can optimize your deep learning models using the tools available on Keras.
While this course won't turn you into a deep learning expert in a day, it'll set a solid foundation. You can be assured that the resources and the hands-on experience you'll gain from this course are high-quality – your instructor, Dan Becker, is a contributor to the Keras library and has spent years working in the deep learning field.
By now, you know the drill. DataCamp Premium can unlock the doors to the world of deep learning for you. Simply sign up and start the Introduction to Deep Learning in Python course today!
More DataCamp Machine Learning, More Fun
Congratulations! You've successfully completed the DataCamp Machine Learning Fundamentals with Python skill track and obtained your statement of accomplishment! Now, you can start working on your portfolio and preparing for the job market.
However, if you're interested in a more niche subject – say, finance or marketing – I've got two more courses that you can complete and expand your skillset even further. Let me introduce you to DataCamp Machine Learning for Business and DataCamp Machine Learning for Finance in Python.
Machine Learning for Business (Enroll HERE)
- Offered by: Karolis Urbanas
- Duration: 2 hours
- Price: $25/month
- Certificate: Yes
- Level: Beginner
- Where to apply? HERE
Are you a business owner? Do you want to learn how you can improve your market performance by employing machine learning? Then the DataCamp Machine Learning for Business course is what you're looking for!
This course is not as technically intensive as some of the other DataCamp machine learning Python courses we've discussed so far. The goal is to introduce you to the concepts of machine learning and how they can be utilized to help you answer essential business questions.
Additionally, knowing when to avoid using machine learning can be just as important to make sure your processes aren't cluttered and stay optimized. In two hours, you'll cover these four chapters:
- Machine learning and data use cases – you'll find a basic introduction to the key terminology and examples related to machine learning;
- Machine learning types – you'll learn the differences between the different types and sub-types of machine learning, as well as what kind of models are used;
- Business requirements and model design – you'll gain some tips for determining what your business requirements are and which machine learning opportunities your company offers;
- Managing machine learning projects – you'll see the flow of machine learning project management, including both positive and negative use cases.
Once the course is complete, you'll be able to draw conclusions and decide whether machine learning is right for your company and, if so, what types of models you might need. The course is taught by Karolis Urbonas, Head of the Machine Learning and Science team at Amazon Web Services.
So, if implementing machine learning is relevant to you and your business, then grab your DataCamp Premium subscription and enroll in the Machine Learning for Business course!
Machine Learning for Finance in Python (Enroll HERE)
- Offered by: Nathan George
- Duration: 4 hours
- Price: $25/month
- Certificate: Yes
- Level: Intermediate
- Where to apply? HERE
And so, we've reached our final DataCamp machine learning Python course. If you happen to be a trader or if the stock markets simply interest you, then this one's going to be the right pick for you. Are you ready to put all your skills so far to the test and work with decision trees, neural networks, and linear models?
The DataCamp Machine Learning for Finance in Python course is here to help you expand your technical skills in the financial sector. In just four hours, you'll see how you can use machine learning algorithms on your datasets to create market prediction models.
You'll be covering four chapters in this course:
- Preparing data and a linear model – you'll learn about the use cases of machine learning in finance and develop your first linear machine learning model;
- Machine learning tree methods – you'll be introduced to tree-based models for future market value prediction;
- Neural networks and KNN – you'll learn about the processes of data scaling and see how KNN is used to predict future values;
- Machine learning with modern portfolio theory – you'll work with the modern portfolio theory (MPT) and use machine learning to predict portfolios and evaluate these predictions.
Keep in mind that DataCamp Machine Learning for Finance in Python is an intermediate course. Your instructor is Nathan George, Assistant Professor of Data Science at Regis University. George also uses neural networks to make market predictions in the traditional and cryptocurrency markets.
So, if you're up for a challenge, Machine Learning for Finance in Python might be one you want to pick up. Simply sign up for the DataCamp Premium plan and start learning as soon as today!
What about R?
While this DataCamp machine learning review/guide has focused on Python-based courses, I haven't forgotten R programmers, either. Whether you've skipped right to this section or took an interest in the Python courses, too, I'm going to quickly introduce you to two courses that are compatible if you plan on using machine learning with R in the marketing field.
Machine Learning in the Tidyverse (Enroll HERE)
- Offered by: Dmitriy Gorenshteyn
- Duration: 5 hours
- Price: $25/month
- Certificate: Yes
- Level: Intermediate
- Where to apply? HERE
Similar to how scikit-learn is used by Python programmers, Tidywerse is a collection of open-source packaged for useRs (the term often used to describe R programmers). It follows the principles of tidy data – something that's essential in managing machine learning projects.
The DataCamp Machine Learning in the Tidyverse course will introduce you to this collection and how you can use the available tools to generate machine learning models, explore their results, and evaluate the overall performance. The two primary packages that you'll need for this course are tidyr and purr.
The five-hour course covers four chapters:
- Foundations of "tidy" Machine learning
- Multiple Models with broom
- Build, Tune & Evaluate Regression Models
- Build, Tune & Evaluate Classification Models
Over the course of each chapter, you'll learn how to utilize the List Column Workflow (LCW) to optimize working with multiple models and how the broom package can be used to handle attributes of several models at once. You'll also try your hand at building regression (linear and random forest) and classification models.
Once you've completed your tasks, you'll know how to accurately evaluate your models and measure their performance using R. Your instructor, Dmitriy Gorenshteyn, is the Lead Data Scientist at Memorial Sloan Kettering Cancer Center. He focuses on developing predictive models to improve patient care and has a Ph.D. in Quantitative & Computational Biology.
If you plan on doing your engineering using R, the DataCamp Machine Learning in the Tidyverse course is a great option for you. All you need to access it in full is the Premium plan.
- Easy to use with a learn-by-doing approach
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- Free certificates of completion
- Focused on data science skills
- Flexible learning timetable
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- Suitable for enterprises
- Paid certificates of completion
- A wide range of learning programs
- University-level courses
- Easy to navigate
- University-level courses
- Suitable for enterprises
- Verified certificates of completion
Machine Learning for Marketing Analytics in R (Enroll HERE)
- Offered by: Verena Pflieger
- Duration: 4 hours
- Price: $25/month
- Certificate: Yes
- Level: Intermediate
- Where to apply? HERE
We've already looked at some of the niche possibilities with the DataCamp Machine Learning for Finance in Python and for Marketing in Python courses, so, we'll also spare some time for a similar niche that R programmers can take advantage of – the Machine Learning for Marketing Analytics in R course.
This course will introduce you to statistical models that you can use to handle large quantities of data and provide support to the decision-making process at your company's Marketing department.
This DataCamp machine learning for R course will take you 4 hours to complete and consists of 4 chapters:
- Modeling Customer Lifetime Value with Linear Regression – you'll learn how to evaluate the customer lifetime value (CLV) and use linear regression. You'll also learn about the overall importance of CLV and how it can help optimize your business strategies;
- Logistic Regression for Churn Prevention – "churn" is an event when a customer leaves your business. Naturally, as a marketer, you want to prevent as much churn as possible. This chapter will teach you how you can utilize machine learning models to target and retain your valuable customers;
- Modeling Time to Reorder with Survival Analysis – a customer's journey can continue to their next purchase or end unexpectedly when they leave your business. In this chapter, you'll learn the concept of survival analysis and how it works. You'll also work with model assumptions to determine the survivability of your valuable customers;
- Reducing Dimensionality with Principal Component Analysis – the final chapter focuses on optimizing your customer relationship management (CRM). You'll see how Principal Component Analysis (PCA) is used to sort and reduce data to an optimal number of variables and which principal components are relevant.
This course will help you work on both your R programming and marketing skills, as you will get hands-on experience working on tasks to develop a hypothetical business strategy. Having these skills can help you improve your market performance, retain your current customers and even attract new ones.
Your instructor for this DataCamp machine learning course is Verena Pflieger, a data scientist at INWT Statistics. She is particularly interested in customer relationship management and customer lifetime value. So, the course is going to contain some of her invaluable field expertise.
Does the thought of improving for marketing department with some readily available machine learning skills pique your interest? Then sign up for the DataCamp Machine Learning for Marketing Analytics in R course today!
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Conclusions
We've covered quite the range of DataCamp machine learning courses – from the core information that does not require programming to the ideal skillset for Python developers and even some recommendations for those data engineers that prefer working with R. So, let's do a quick wrap-up.
Machine learning is a lucrative field that holds massive importance in today's internet landscape. From commerce to social media, data is everywhere, and it can be used to develop algorithms, create predictions, and simply test your machine learning models. And DataCamp machine learning Python and R courses are a great place for you to start learning all about it.
From the DataCamp Machine Learning Fundamentals with Python skill track to using R for marketing analysis, there is plenty to choose from. Don't forget that you can find discount codes and special offers for DataCamp here.
So, grab your databases, get your machine learning model ready, and have fun!
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