Breaking into artificial intelligence can feel overwhelming for beginners. You’ve likely heard terms like machine learning and deep learning thrown around – but which one should you tackle first? This guide is written by an AI industry veteran with 10+ years of experience to help demystify machine learning vs. deep learning for newcomers. We’ll compare what these fields entail, how difficult they are to learn, and how each can shape your career. Whether you’re looking to switch careers into tech or are an aspiring data scientist, you’ll get clear advice on which AI skill to learn first and why. Along the way, we’ll share practical tips (including insights from Refonte Learning’s programs) so you can confidently start your journey in AI upskilling for beginners.
Understanding the Basics: Machine Learning vs. Deep Learning
Machine Learning (ML) is a broad field of AI where algorithms learn from data to make predictions or decisions. Traditional machine learning includes methods like linear regression, decision trees, support vector machines, clustering algorithms, and so on. These techniques often rely on structured data and usually require human guidance for feature selection. Think of ML as teaching a computer by example: for instance, you might train a model with lots of labeled emails so it can learn to detect spam. In classic ML, human experts often decide which data features (attributes) might be important (e.g., the presence of certain words in an email) and feed those into the algorithm. It’s powerful for many tasks, but it involves a mix of human insight and machine computation.
Deep Learning (DL) is a subset of machine learning that uses neural networks with multiple layers (thus “deep”) to learn from data. These neural networks are loosely inspired by the human brain’s network of neurons. One key difference is that deep learning algorithms can automatically learn features from the raw data, requiring minimal human intervention in feature selection. For example, in deep learning, you can feed a neural network raw images of cats and dogs, and it will learn to distinguish them by itself – it figures out the relevant features (edges, shapes, etc.) in the intermediate layers. Deep learning excels at handling unstructured data like images, audio, and text, where manual feature extraction is difficult. However, it typically needs a lot more data and computational power to work well. In short: all deep learning is machine learning, but it’s the advanced form that automates feature learning and usually achieves higher performance on complex tasks (given sufficient data).
Learning Curve: Which Is Easier for Beginners?
One of the biggest considerations for newbies is the learning curve of deep learning vs. machine learning. Generally, starting with basic machine learning is easier for those with limited coding or math background. Here’s why:
Complexity: Traditional machine learning algorithms are conceptually simpler. You can often understand what a linear regression or decision tree is doing without a ton of math. Deep learning models, on the other hand, are more complex “black boxes” with many parameters, which can be hard to intuitively grasp at first. Learning how a neural network with 10 layers works (with concepts like backpropagation, activation functions, etc.) is a steeper climb than learning how a decision tree splits data. So, from a conceptual standpoint, ML is the gentler introduction.
Prerequisites: Machine learning does require some math (basic statistics, algebra, maybe a bit of calculus for certain algorithms) and programming, but you can get started with high-level libraries pretty quickly. Deep learning, while also accessible via libraries (like TensorFlow/Keras or PyTorch), tends to benefit from a stronger handle on linear algebra and calculus to truly understand what’s happening under the hood. If you’re not comfortable with concepts like matrices or derivatives yet, jumping straight into deep learning theory could be overwhelming. Many experts suggest building a strong foundation in ML – essentially crawling before you run – because deep learning is an extension of those basics.
Tools and Resources: From a practical perspective, machine learning can be practiced on a normal computer with small datasets. You can learn a lot by playing with datasets in Excel or using scikit-learn in Python on your laptop. Deep learning, in contrast, often requires more powerful hardware (GPUs) and big data to really see results, especially for image or language tasks. As a beginner, it might be frustrating if your laptop takes hours to train a neural network (or can’t train a large one at all). Starting with ML allows you to learn on smaller data and simpler models, which is more manageable. (That said, cloud services and free platforms like Google Colab have made it easier to experiment with deep learning these days, even if you don’t own a powerful computer.)
In summary, for most beginners, machine learning is more approachable to learn first. You’ll get quick wins – for example, you can train a small model to predict housing prices or classify iris flower types with just a few lines of code and see results immediately. This builds confidence and understanding. Once you grasp those ML fundamentals, learning deep learning will be far smoother because you’ll understand the core principle that it’s just learning from data, albeit in a more automated and complex way. Refonte Learning’s beginner AI curriculum, for instance, starts with ML basics before introducing deep learning, precisely to ensure students have that solid foundation.
Career Paths and Practical Applications
Both ML and DL skills are highly valued, but let’s talk about how they play out in real-world careers and applications:
Machine Learning Career Path: With machine learning know-how, you can aim for roles such as Data Analyst, Data Scientist, or Machine Learning Engineer. These positions often involve working with structured data (like databases, spreadsheets) and applying a range of algorithms to solve business problems. For example, as a data scientist at a retail company, you might use machine learning to analyze purchase data and predict customer churn or optimize inventory. These roles frequently use algorithms like regression, classification (maybe using random forests or gradient boosting), or clustering to find insights. A machine learning career path can span many industries – finance (credit scoring models), healthcare (predictive models for patient risk), marketing (customer segmentation), etc. The key is that you’re leveraging data to inform decisions or automate processes. Many traditional ML roles also involve a fair bit of data preprocessing, cleaning, and feature engineering. Domain knowledge can be important too (knowing something about the industry you’re in to choose the right features or models).
Deep Learning and Specialized AI Roles: Deep learning expertise opens the door to more specialized (and often cutting-edge) projects. If you’re interested in roles like Computer Vision Engineer, NLP (Natural Language Processing) Engineer, AI Research Scientist, or even AI Specialist in fields like autonomous vehicles or robotics, deep learning is usually essential. For example, computer vision engineers build models that can interpret images or video – think facial recognition, medical image diagnostics, or self-driving car vision systems – almost all of which rely on convolutional neural networks (a deep learning technique). NLP engineers develop systems like language translators, speech recognition (like Siri/Alexa), or chatbot intelligence – often using deep learning models like transformers or recurrent neural networks. These areas have seen huge breakthroughs thanks to deep learning. In terms of applications, deep learning is what’s behind many “AI” products we see: it’s how Netflix recommends shows (DL models analyzing your watch history), how smartphones unlock with your face, and how breakthrough technologies like GPT-4 (a large language model) are possible.
Job Market Demand: It’s worth noting that in job listings, you’ll often see “machine learning” as a required skill broadly. In fact, one analysis showed that machine learning appears in about 69% of data science and AI job postings. Deep learning is a bit more specialized but is mentioned in around one-third of AI job postings (particularly those focusing on AI research, vision, or language tasks). Essentially, if you know machine learning, there are many entry points in the job market. If you also know deep learning, you become eligible for an even wider range of roles, including the most cutting-edge projects. Many roles will expect both: a job title might be “Machine Learning Engineer,” but the description asks for experience with deep learning frameworks. The good news is that ML and DL skills complement each other – learning one helps with the other, and having both makes you a versatile AI professional. Refonte Learning’s AI programs train students in core ML first and then DL precisely to meet this blended demand.
Career Growth and Evolution: Starting with machine learning might land you a role like a junior data scientist working on business analytics. As you gather experience and perhaps introduce deep learning into your toolkit, you might move into more advanced projects (like building a computer vision solution for your company). On the flip side, starting with deep learning could land you a niche role early (say, as a neural network specialist in a research lab), but you might find you need to understand broader ML concepts when the project calls for a simpler solution or when communicating with a broader team. Long-term, careers in AI often involve continuous learning. Today’s hot framework or algorithm might be eclipsed by a new one in a few years, so be prepared to keep updating your knowledge. The fundamentals of ML/DL, however, will remain valuable despite tooling changes.
In summary, machine learning skills can take you into a wide array of industries and roles – it’s the backbone of data-driven decision making in many companies. Deep learning skills are crucial for pushing the state-of-the-art in AI applications and tackling problems that classical ML can’t easily handle (like image and speech recognition at high accuracy). Ideally, you want to have both in your toolkit eventually. But how to proceed first? That depends on your goals, which we’ll discuss next.
Which Should You Learn First? (It Depends on Your Goals)
So, deep learning vs. machine learning – which should you learn first? The answer really depends on your background and what you aim to do:
For most people starting from scratch, learning machine learning fundamentals first is the way to go. Machine learning provides the essential building blocks for understanding AI. When you learn ML, you cover topics like how to split data for training and testing, how to evaluate models (accuracy, precision/recall, etc.), how to avoid overfitting, and the general mindset of letting data drive improvements. These concepts are absolutely transferable to deep learning. If you jump straight to deep learning without this foundation, you might end up using powerful neural network libraries without understanding these underlying principles, which can lead to mistakes or confusion.
However, consider a few scenarios:
Scenario 1: “I have limited coding/math experience.” In this case, start with ML. You’ll benefit from the more straightforward algorithms and the wealth of beginner tutorials available. For example, you might follow a tutorial to predict house prices with a linear regression in Python. You’ll learn how to handle data and interpret results, and nothing will be too much “magic” – you can understand the output fairly easily. Once you get comfortable with a few ML projects, you can then venture into writing your first neural network. Refonte Learning’s beginner tracks follow this approach: build up your Python and ML skills first in a manageable way.
Scenario 2: “I’m already a software developer or have a strong math background, and I’m interested in a specific AI area (like vision or NLP).” If you have a solid grounding in programming and are not intimidated by more abstract concepts, you could begin with a deep learning course that targets your interest. For instance, someone proficient in Python and calculus might dive into a fast deep learning course to quickly get hands-on with building an image classifier. This can be motivating because you jump into impressive projects early. Just be aware that you’ll still need to learn a lot of the ML basics along the way. Often these intensive DL courses will include a crash course on ML concepts, but sometimes it can feel like learning backwards. It’s doable – especially if you’re the type who learns best by diving into the deep end – but plan to fill any gaps in foundational knowledge. Many experts still advise learning ML first even for talented folks, because it ensures no fundamental holes in understanding. (Even in Refonte Learning’s advanced courses, we do a quick ML refresher to make sure everyone’s on the same page.)
Scenario 3: “I want results fast for a specific project.” Perhaps you have a personal project or a work task that would clearly benefit from deep learning (say, you want to build a prototype voice assistant, which needs deep learning for speech recognition). If the timeline is tight, you might directly use a deep learning API or pre-trained model to get the job done. In this practical case, you’re not fully “learning deep learning from scratch,” but rather leveraging existing models. This is fine and often practical – you don’t need to reinvent the wheel. Just treat it as a stop-gap and later go back to learn systematically. For example, you use TensorFlow’s pre-trained model to recognize spoken commands now, but later you study how such models are trained.
Scenario 4: “I’m mainly interested in data analysis/business analytics.” Not everyone aiming to learn AI wants to build neural networks. If your goal is to enhance your data analysis skills for business (like becoming a better analyst or using ML to make business decisions), then deep learning might not even be necessary initially. You’d be better served mastering classical machine learning and statistics. These will help you make sense of data and build interpretable models. Deep learning in business analytics is less common unless you’re dealing with huge datasets and very complex patterns. Also, deep learning models are often less interpretable, which can be a drawback in business settings that require explaining to stakeholders why the model made a certain prediction. So for a career in data analytics or traditional data science, ML first (and maybe ML only, for a while) is the right approach.
In all cases, remember that deep learning is essentially an advanced form of machine learning. It’s like learning to drive a high-performance sports car versus a regular car – you still need to know the basic rules of the road. Many people find that once they have ML experience, picking up deep learning is much easier. Concepts like training vs. test data, model generalization, or even simpler algorithmic thinking will carry you through the complex parts of deep learning.
On the other hand, don’t be scared of deep learning as some mythical beast. The availability of high-level frameworks means that, practically, you can experiment with neural networks without having a Ph.D. in math. The key is context. If you’ve never built a simple model, a 100-layer neural net example might just seem like magic. But if you have, say, built a model to classify handwritten digits using logistic regression, you’ll appreciate what extra accuracy or capability the neural network is giving you when you try it – and you’ll have a reference point.
The bottom line: most beginners should start with ML and then move to DL. This path ensures you cover all your bases. However, if you have a compelling reason or sufficient background to start with deep learning, it’s okay to do so – just be prepared to fill in foundational knowledge as needed. Many learning programs (including Refonte Learning’s AI courses) will guide you through ML basics before deep learning, which naturally sets you up for success.
Actionable Tips for AI Beginners (Machine Learning & Deep Learning)
Master the Basics of Python and Math: Both ML and DL typically rely on Python (it’s the most common language in the AI field). If you’re new to programming, spend time learning Python fundamentals first – understand syntax, data structures (lists, dictionaries), and how to write simple programs. Simultaneously, brush up on basic math: ensure you’re comfortable with algebra (e.g., solving equations), statistics (mean, variance, probability basics), and have a very basic understanding of calculus (the concept of a derivative). You don’t need to be a math expert to start, but familiarity helps. Refonte Learning’s introductory AI courses, for example, include modules on essential math and Python to get students up to speed.
Start with a Simple ML Project: Nothing solidifies learning like doing a project. Pick a simple, classic machine learning problem as your first project. For instance, try the famous “Iris” flower dataset (predict the species of a flower from its measurements) or make a linear regression model on some data (like predicting house prices from size, number of rooms, etc.). You can follow step-by-step tutorials for these. Use a library like scikit-learn, which makes implementing algorithms straightforward. This will teach you how to load data, preprocess it, train a model, and evaluate results. Keep it small and manageable – the goal is to go through the whole process end-to-end and understand the workflow.
Leverage Online Courses and Tutorials: Structure can be very helpful when you’re beginning. Consider enrolling in a well-reviewed online course that covers machine learning fundamentals. Courses on Coursera (such as Andrew Ng’s Machine Learning course) or platforms like DataCamp and Udacity are geared towards beginners. These often give you hands-on assignments which are invaluable. Refonte Learning also offers a structured path for beginners, starting from ML and moving to DL, which might suit you if you prefer a guided program with mentorship. Additionally, don’t overlook free resources: there are excellent YouTube series and blogs that teach ML concepts in an accessible way (e.g., StatQuest on YouTube for stats and ML, or blogs like MachineLearningMastery).
Practice Coding and Use Libraries: As you learn concepts, practice coding them. For example, if you learn about how a decision tree works, try using scikit-learn to fit a decision tree model, and also try to implement a simple version of it from scratch (even if just to understand it conceptually). When you move to deep learning, get familiar with a library like Keras (which runs on TensorFlow) or PyTorch. Start by running example codes provided in tutorials or documentation. For instance, a common beginner deep learning project is classifying handwritten digits using the MNIST dataset – there are many ready examples for this. Don’t worry if you don’t fully understand how the neural network works internally at first; by playing with the code (changing parameters, seeing what happens if you alter the architecture slightly), you’ll build intuition.
Join Communities and Share Progress: Learning alone can be tough. It helps to join communities where you can ask questions and see what others are doing. Websites like Stack Overflow are great for technical questions. Reddit has communities like r/learnmachinelearning or r/datascience for discussion. Kaggle (a platform for data science competitions) has forums and also hosts many datasets – they even have “Getting Started” competitions perfect for beginners. Try participating in a beginner-friendly Kaggle competition; even if you don’t rank high, you’ll learn a ton by doing and by reading others’ solutions. Also, consider writing about your learning (maybe a small blog on what you learned in your first project) – teaching or summarizing insights helps reinforce your knowledge. Refonte Learning often encourages learners to present their project results or write reflections, as it cements what you’ve learned.
Gradually Transition to Deep Learning Projects: Once you’ve done a few ML projects and perhaps a basic deep learning example, gradually take on more DL projects. A good next step is to use pre-trained models. For instance, use a pre-trained image recognition model (like ResNet or VGG, readily available in libraries) and apply it to a problem (perhaps classify your own set of images). This teaches you about concepts like transfer learning, which is very practical (you don’t always need to train from scratch). Then try creating and training a simple neural network of your own on a dataset. Start maybe with something like a two-layer fully connected network on a small dataset to predict numeric values, just to see that you can set it up and it trains. From there, you can explore specialized architectures: convolutional networks for images, recurrent networks for sequences, etc., as your interest dictates. Always keep track of the basics while you do this – for example, when your network isn’t training well, recall the basic concept of overfitting or data quality from your ML knowledge.
Keep Learning and Stay Curious: AI is a fast-evolving field. What’s cutting-edge today might be outdated in a few years. Develop a habit of continuous learning. This could mean reading AI news or research summaries (there are newsletters that summarize latest AI research in layman terms), following AI influencers on social media who share tips, or taking advanced courses as you progress. One great way to stay sharp is to challenge yourself with small problems regularly – e.g., participate in online hackathons or try to replicate results from interesting research papers (many papers publish their code). Also, don’t hesitate to revisit fundamentals. Many professionals periodically revisit basic courses or textbooks; each time, you may understand something at a deeper level. And whenever you hit a roadblock, remember that resources like Refonte Learning and its community are there – be it through advanced workshops, mentorship, or forums – to support your upskilling journey.
Conclusion
Deciding between deep learning and machine learning as your first step comes down to building a strong foundation. For most beginners, starting with machine learning makes the journey into AI more approachable – it’s like learning to walk before you run. Deep learning vs. machine learning isn’t an either/or choice in the long run; you’ll likely want to know both as you advance in your AI career. The key is to get started in a way that matches your current skills and goals. With patience and practice, you can progress from simple models to sophisticated neural networks. Along the way, make use of learning resources (from free tutorials to comprehensive programs like those at Refonte Learning) to support your growth. AI is a dynamic, exciting field – choosing the right starting point will set you up for success in your upskilling journey and ensure you enjoy the process of becoming an AI practitioner.
FAQ
Q: What’s the difference between machine learning and deep learning?
A: Machine learning is a broad term for algorithms that enable computers to learn from data and improve over time. It includes many types of models (decision trees, linear regression, etc.). Deep learning is a subset of machine learning that uses many-layered neural networks to learn from large amounts of data. The main difference is that deep learning can automatically learn complex features from raw data, especially unstructured data like images or text, while traditional machine learning often relies on human-crafted features and works better with structured data. In essence: all deep learning is machine learning, but it’s the more advanced, neural network-based approach.
Q: Is deep learning more difficult to learn than machine learning?
A: Deep learning can be more challenging for beginners, yes. It involves more complex concepts (like neural network architectures, backpropagation algorithms, etc.) and typically requires understanding a bit more math (especially linear algebra and calculus). Machine learning basics (e.g., understanding simpler models like decision trees or logistic regression) are generally easier to grasp initially and to visualize. However, modern libraries have made both ML and DL more accessible. Many beginners successfully learn deep learning with the help of high-level frameworks that handle a lot of the complexity. Still, we usually recommend learning some ML first because it makes learning DL much smoother. Think of it like learning to ride a bicycle before a motorbike – it’s not absolutely required, but it helps a lot.
Q: Do I need a strong math background to start learning ML or DL?
A: You need some math, but you don’t need to be a mathematician to start. At the beginning, a grasp of high school math – algebra (how equations work), basics of probability, and maybe familiarity with the concept of functions – is enough. As you progress:
For machine learning, understanding statistics and linear algebra will greatly help (e.g., knowing what a matrix is, or what standard deviation means).
For deep learning, eventually learning about calculus (derivatives, gradients) and more linear algebra (vectors, matrices, eigenvalues) becomes important to really understand how learning is happening under the hood.
Many courses (including beginner-friendly ones from Refonte Learning) introduce the math concepts alongside the coding, so you learn them as needed. Also, there are excellent resources that teach the math for ML/DL in intuitive ways. So don’t be scared – you can start with minimal math and learn more as you go. The key is to not skip learning the math entirely; just tackle it gradually. Often, implementing a simple algorithm and seeing it work will motivate you to understand the equations behind it.
Q: Which skill has better career prospects or pays more – machine learning or deep learning?
A: In practice, the two skills are intertwined and most good jobs in AI will expect you to have at least some knowledge of both. Machine learning (in the broad sense) is listed in a vast number of job descriptions across industries – everything from finance to healthcare is using ML for data-driven decisions, so the career prospects are excellent. Deep learning is a bit more niche in application but is at the core of the most exciting AI developments (autonomous vehicles, advanced AI assistants, etc.), so specialists in deep learning are highly sought after and often command high salaries too. If we speak generally, a “machine learning engineer” or “data scientist” role might be more common and could have a slightly lower barrier to entry. A “deep learning researcher” role might be more specialized (often requiring an advanced degree or very strong portfolio) but could be at the forefront of tech with potentially higher pay in research-heavy organizations. That said, many ML engineer roles also involve deep learning these days. Salary-wise, both ML and DL experts tend to be well-paid. In the US, for example, entry-level data scientists/ML engineers might make around $90k-$110k, while those with a few years of experience or specializing in DL could see significantly higher (well into six figures). Globally, AI roles are among the top-paying in tech in many countries. The best strategy for career and pay is: gain competence in machine learning first (to land a job and get experience), and simultaneously or subsequently deepen your deep learning expertise – this combination makes you very versatile and valuable.
Q: Can I learn deep learning without learning machine learning first?
A: It’s possible, especially now that there are some courses aimed at beginners that go straight into building neural networks (like some high-level courses that emphasize coding first). Some learners with strong programming skills do jump directly into deep learning frameworks and manage to build projects. However, skipping ML basics can lead to gaps in understanding. You might be able to train a neural network by following a recipe, but not understand why it’s behaving a certain way or how to troubleshoot when things go wrong. Learning ML first gives you a framework for thinking about model training and evaluation that applies to DL too. In short: you can start with deep learning, but be prepared to circle back to ML concepts anyway. Most people find it more logical to do ML then DL, but if you’re very motivated by, say, building an AI for a specific task, you can start there and learn fundamentals concurrently. Just make sure you don’t ignore the fundamentals – they’ll catch up to you eventually. Many training programs (like Refonte Learning’s) deliberately start with ML precisely because it makes learners stronger when they move to DL.
Q: What programming language and tools should I use to learn ML/DL?
A: Python is the dominant language in both machine learning and deep learning. It has a rich ecosystem of libraries: for ML, libraries like scikit-learn, pandas (for data manipulation), and Matplotlib/Seaborn (for visualization) are commonly used. For deep learning, the main libraries are TensorFlow (often used via the Keras API for ease of use) and PyTorch. Both are excellent – TensorFlow/Keras is known for production deployment and a beginner-friendly interface (Keras), while PyTorch is praised for its developer-friendly design and is widely used in research. As a beginner, it might not matter which deep learning library you start with; both have lots of tutorials and community support. Many beginners find Keras (within TensorFlow) slightly more straightforward to get a model up and running with minimal code, whereas PyTorch offers flexibility when you want to tinker more under the hood. Besides Python, R is another language used especially for statistical machine learning and data analysis. If you come from an R background or are interested in statistical modeling, R can be fine for ML (it has packages like caret and mlr). However, R is not commonly used for deep learning (though it can call TensorFlow). Given industry trends, learning Python will give you the most opportunities and access to the majority of learning resources. Jupyter Notebooks are a great tool to write and run Python code in an interactive way, and many courses (and Refonte Learning’s exercises) utilize notebooks. So, in summary: use Python, start with libraries like scikit-learn for ML, and TensorFlow or PyTorch for DL as you progress.
Q: How long does it take to learn machine learning or deep learning?
A: This varies a lot depending on how much time you can dedicate and your background. To get the basics of machine learning (enough to do simple projects), it might take a few months of consistent learning and practice. For example, many online ML courses are around 8-12 weeks long for a beginner level. After completing such a course and doing 1-2 projects, you’ll be at a point where you “know what you don’t know” (which is a good place to be to continue learning on your own). Deep learning might take another few months to grasp basics after that. Realistically, to be job-ready (for an entry-level ML engineer or data scientist role), you might spend 6 months to a year learning and building projects. But remember, learning AI is a continuous journey – even professionals with years of experience keep learning new things every year. One strategy is to learn in public: as you learn and build things, share your projects on GitHub or a personal blog. This way, after some months, you have something to show for your learning which can help in job applications. If you’re in an intensive program (like a bootcamp or a full-time course through Refonte Learning or university), the timelines can be faster due to immersion. But outside of structured timelines, don’t rush too much – focus on understanding concepts well, and the speed will come. Even a modest pace, if consistent, will compound knowledge over time. After a year of steady learning, you’ll be amazed at how much you know.