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If you’re a tech professional or looking to enter the industry, what you should be thinking about right now is being the best you can be in a specific area. You want to be seen as a specialised professional, someone who knows their stuff, the ins and outs, etc.
Naturally, we are given broad knowledge and not how to become specialised in a specific field.
This is where this article comes in to help you refine your skills, build your knowledge and change your title to being a specialised professional.
Machine Learning Specialization
Link: Machine Learning Specialization
Are you a data analyst and you’re looking to advance your tech and data handling skills to break into AI and machine learning? Look no further. This Machine Learning Specialization consists of 3 courses:
- Supervised Machine Learning: Regression and Classification
- Advanced Learning Algorithms
- Unsupervised Learning, Recommenders, and Reinforcement Learning.
In these 3 courses, you will learn how to build machine learning models using NumPy and Scikit-learn, for example, supervised models such as logistic regression. You will also learn how to build & train a neural network with TensorFlow, apply best practices for ML development, and build recommender systems and deep reinforcement learning models.
Go from being a data analyst to a machine learning engineer!
MLOps Specialization
Link: MLOps Specialization
Want to dive a little deeper when it comes to machine learning? How about the operations side of it?
This MLOps Specialisation consists of 5 courses:
- Introduction to Machine Learning in Production
- Machine Learning Data Lifecycle in Production
- Machine Learning Modeling Pipelines in Production
- Deploying Machine Learning Models in Production
In these courses, you will learn how to design a machine learning production system end-to-end: from project scoping to deployment requirements. You will also establish a model baseline, address concept drift, deploy, and learn how to continuously improve the ML application. Doesn’t stop there, you will also learn how to build data pipelines, establish data lifecycle and maintain a continuously operating production system.
Deep Learning Specialization
Link: Deep Learning Specialization
Or maybe you want to dive into deep learning? This Deep Learning Specialization consists of 5 courses:
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
In these courses, you will learn how to build and train deep neural networks, identify key architecture parameters, as well as be able to train test sets, analyze variance for DL applications, and use a variety of techniques and optimization algorithms. It doesn’t stop there, you will also learn how to build a CNN/RNN and more.
Natural Language Processing Specialization
Link: Natural Language Processing Specialization
Want to learn the foundations behind large language models such as ChatGPT and Claude?
You can now with the Natural Language Processing Specialization which consists of 4 courses:
- Natural Language Processing with Classification and Vector Spaces
- Natural Language Processing with Probabilistic Models
- Natural Language Processing with Sequence Models
- Natural Language Processing with Attention Models
In these 4 courses, you will learn about logistic regression, naïve Bayes, sentiment analysis, word embeddings and more. Dive further and learn about recurrent neural networks, LSTMs, GRUs & Siamese networks as well as how to use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots and more.
TensorFlow: Data and Deployment Specialization
Link: TensorFlow: Data and Deployment Specialization
If you have looked at the above courses and saw TensorFlow being mentioned but do not need to learn about the rest but TensorFlow – check this specialisation out.
This TensoreFlow: Data and Deployment Specialization consists of 4 courses:
- Browser-based Models with TensorFlow.js
- Device-based Models with TensorFlow Lite
- Data Pipelines with TensorFlow Data Services
- Advanced Deployment Scenarios with TensorFlow
In these 4 courses, you will learn how to run models using TensorFlow.js, and prepare and deploy models on mobile devices using TensorFlow Lite. You will also learn how to access, organize, and process training data more easily using TensorFlow Data Services whilst exploring more advanced deployment scenarios using TensorFlow Serving, TensorFlow Hub, and TensorBoard.
Wrapping it Up
And just like that you have a variety of courses that you can use to elevate your skills, become more knowledgable and a specialist in a specific sector of the tech industry.
If you wanted to be a jack of all trades and become highly competitive, you can take more than one of these to broaden your horizons!
Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.
