Top Machine Learning Certificates For 2024 (ML Roadmap)

– This is School of Machine Learning’s complete roadmap for learning ML for 2024.

This year has been an incredible year for the world of machine learning and AI in large. We saw the rise of Large Language Models completely taking over the world. From small startups to the biggest companies in the world, everyone is talking about it. In fact, this year might just have been the biggest inflection point towards seeing how a Artificial General Intelligence (AGI) might look like. Well, if you want to know the intricacies of how all of this actually works. We have the right machine learning certifications for you.

Why Should I Learn Machine Learning?

One of the most important question that you should be asking yourself, before even looking at these courses/certifications, is why should I even learn machine learning? The answer is not as straightforward as you might think. Here are some of the most important reasons on why you should learn and also why these might not be for you.

Learn ML:

  1. Boost Your Career with In-Demand Skills: Machine learning is transforming the tech field, directly linking to better job prospects and higher salaries. By mastering it, you’re not just adding a skill, you’re making yourself a top candidate in numerous job markets and setting yourself up for rapid career advancement.
  2. Staying Relevant in the Industry: The tech industry is ever-evolving, and machine learning represents one of its most significant leaps forward. By learning ML, you ensure you’re not left behind in this technological revolution, staying relevant and competitive in your field.
  3. Transitioning to Diverse Roles: Machine learning isn’t confined to one industry or sector. It’s ubiquitous, influencing sectors like finance, healthcare, automotive, and more. Learning ML offers flexibility and a diverse range of career paths.

Don’t learn ML:

  1. Seeking Instant Gratification: Machine learning requires deep study and understanding, often demanding a substantial investment of time and effort. If you’re interested in quick wins or overnight success, this field might frustrate your expectations.
  2. Not Intrigued by Data and Complex Challenges: Machine learning revolves around analyzing data and solving intricate problems. If you’re not passionate about diving into analytics or tackling complex technological issues, ML might not be the right fit for you.
  3. Expecting a Non-Technical Role: While there are various roles within the field, a core understanding of algorithms, data structures, and computations is essential. If you’re envisioning a transition away from technical work, ML might not be the escape you’re imagining.

Now you have a basic understanding of why you should or should not learn ML. If you are willing to go the distance, let’s take a deep dive into the roadmap you should be following for learning ML for 2024.

Machine Learning Roadmap 2024

Even though the title mentions certifications for ML, this is going to be a roadmap to follow to becoming a machine learning engineer. Which basically means that you should attempt these courses in the order they are written below. Unless you already have prior machine learning experience, then you can jump to any one of the courses you see fit.

At the end of the roadmap you will have gained multiple certificates for ML, which you can display on LinkedIn or write in your resume/CV. But the biggest thing is that you will have the knowledge on how machine learning actually works. Which means applying for some of the highest paying jobs in the tech world. But before attempting the courses please read the important notes.

Important notes:

  1. For some of these specializations you can earn degree credits, if your institution is part of the program.
  2. Almost all of these specialization contain multiple courses. And some of them do require prior programming experience.
  3. Most of the courses are taught in Python. If you want to brush your Python skills pretty quickly, check out the LearnXinYminutes python page.
  4. All the courses are free but if you want to get the certificate or do assignments/exams, there is a fee.

Important update:

We at School of Machine Learning have published our first ever project-based course ‘Build Talking AI Chatbot App’. It is aimed at an audience who is completely new to the world of AI/ML and teaches how to use the latest ML models for building real world applications. Make sure to check it out.

1. Machine Learning Basics

The first course you should be looking at, especially if you are new to the world of machine learning is the basic one. Machine Learning Specialization consists of three courses, which are all aimed at beginners.

This specialization consists of the following courses:

  1. Supervised Machine Learning: Regression and Classification.
  2. Advanced Learning Algorithm.
  3. Unsupervised Learning, Recommenders, Reinforcement Learning.

The basic machine learning course is pretty much the rite of passage for almost everyone who is interested in ML. This course is provided by Stanford with partnership and is being taught by Andrew Ng.

Overview and Relevance: This specialization starts with the basics of building an ML model with NumPy & scikit-learn. Followed by building and training a neural network with TensorFlow.

Prerequisites: Basic coding skills (loops, functions and if/else statements) and high school-level math (algebra, arithmetic).

Time to Complete: Approximate time is about 100 hours or around two months at 10 hours a week.

Skills Gained: Neural Networks, Logistic Regression, Linear Regression, Reinforcement Learning and more.

Learning format: Videos with quizzes and assignments.

Cost: Free to audit but have to pay for the certificate which starts at $49 USD/month until you complete specialization.

Next Steps: Completing this specialization lays a strong foundation of machine learning. This is the stepping stone into the world of ML.

2. Mathematics for Machine Learning

If the earlier Machine Learning Specialization series was laying a strong foundation of ML. This Mathematics for ML is pouring the concrete into the foundation to cement your strong understanding of ML.

The heart of machine learning is mathematics and this series of courses focuses exactly on that. Vectors, linear algebra, calculus and more are the critical elements of what makes ML. These series of courses are taught by Imperial College London.

This specialization consists of the following courses:

  1. Mathematics for Machine Learning: Linear Algebra.
  2. Mathematics for Machine Learning: Multivariate Calculus.
  3. Mathematics for Machine Learning: PCA.

Overview and Relevance: This specialization focuses on the prerequisite mathematics for applications in machine learning. It lays the foundation of deep learning and other advanced topics within the world of ML.

Prerequisites: Basic math.

Time to Complete: 45 hours or 10 hours a week for one month.

Skills Gained: Principal Component Analysis (PCA), Multivariable Calculus, Linear Algebra, Eigenvalues and Eigenvectors.

Learning format: Video, quizzes and assignments.

Cost: Free to audit but certification starts at $49 USD/month. It’s also included as part of Coursera Plus.

Next Steps: Once you have an understanding of mathematics that are used within ML, move on to the next specialization.

3. Deep Learning Specialization

Deep learning is where things start getting serious. If you truly want to work within the world of machine learning, knowing deep learning is a must. This is where you go from building a simple neural network in earlier courses to building and training deep neural networks. This specialization consists of five courses.

This specialization consists of the following courses:

  1. Neural Networks and Deep Learning.
  2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimisation.
  3. Structuring Machine Learning Projects.
  4. Convolutional Neural Networks.
  5. Sequence Modes.

Overview and Relevance: The Deep Learning Specialization starts with learning about general deep neural networks, followed by hyperparameter tuning, and optimization. This is further followed by building convolutional neural networks (CNN) for image processing and then sequence models for tasks such as Natural Language Processing (NLP) and machine translation. Deep learning is a critical part of all fields within ML. Without knowing DL, it is almost impossible to build and train models for production. This specialization also earns degree credits for multiple universities/colleges.

Prerequisites: This is the first intermediate level courses you might take in this 2024 machine learning roadmap. This requires intermediate level python and basic understanding of linear algebra and machine learning.

Time to Complete: 125 hours or three months at 10 hours a week.

Skills Gained: Recurrent Neural Network, Convolutional Neural Network, Transformers, Hyperparameter Tuning, Machine Translation.

Learning Format: Video, quizzes, assignments/labs.

Cost: Free to audit but certificate requires $49/month USD until completion.

Next Steps: This is going to be the final general specialization that you complete in the machine learning roadmap. The only steps from now on are going to specialise in certain areas within the world of machine learning. The choice is yours.

4. Natural Language Processing Specialization

Since the past one year, the world of ML has been heavily focused on Large Language Models (LLMs). The launch of ChatGPT kicked off a massive race for building the state-of-the-art models for LLMs. If you have wondered how to build and train one; the Natural Language Processing (NLP) specialization teaches that.

NLP specialization is an optional path an individual can take if they want to get experience or want jobs related to NLP. If you are looking for image processing than there is no need to go this route. NLP specialization consists of four courses.

NLP specialization consists of the following four courses:

  1. Natural Language Processing with Classification and Vector Spaces.
  2. Natural Language Processing with Probabilistic Models.
  3. Natural Language Processing with Sequence Models.
  4. Natural Language Processing with Attention Models.

Overview and Relevance: This specialization consists of extensive NLP courses that teach sentiment analysis, text generation, named entity recognition, hidden Markov models, transformer models and many more. By the end of this you will have enough knowledge to understand most open-source LLMs. If you are seeking jobs or want to move into research in NLP, this specialization is going to be highly helpful.

Prerequisites: Even though Coursera puts it at an intermediate level. I personally feel this might be the hardest specialisation in this roadmap. It requires good knowledge of machine learning, intermediate Python experience including DL frameworks and proficiency in calculus, linear algebra and statistics.

Time to Complete: 121 hours or three months at 10 hours a week.

Skills Gained: Transformer, Word2Vec, Machine Translation, Sentiment Analysis, Text Generation, Attention Models.

Learning Format: Video, quizzes, assignments/labs.

Cost: Free to audit but certificate requires $49/month USD until completion.

Next Steps: Time to build your own models.

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