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If you're a tech professional or looking to enter the industry, what you should be thinking about now is being the best you can be in a specific area. You want to be seen as a specialized professional, someone who knows what he's doing, the ins and outs, etc.
Naturally, we are given broad knowledge and not how to specialize in a specific field.
This is where this article comes in to help you hone your skills, develop your knowledge, and upgrade your title to specialized professional.
Specialization in machine learning
Link: Machine Learning Specialization
Are you a data analyst looking to improve your technology and data management skills to get into artificial intelligence and machine learning? Look no further. This Specialization in Machine Learning 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 like logistic regression. You'll also learn how to build and train a neural network with TensorFlow, apply best practices for ML development, and create recommendation systems and deep reinforcement learning models.
Go from data analyst to machine learning engineer!
MLOps Specialization
Link: MLOps Specialization
Want to dig a little deeper when it comes to machine learning? How about the operations side?
This MLOps specialization 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 an end-to-end machine learning production system: from project scope to deployment requirements. You will also establish a model baseline, address concept drift, implement, and learn how to continually improve your machine learning application. It doesn't stop there, you'll also learn how to create data pipelines, establish the data lifecycle, and keep a production system running continuously.
Specialization in deep learning
Link: Deep Learning Specialization
Or maybe you want to dive into deep learning? This Specialization in Deep Learning 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 architectural parameters, as well as be able to train test sets, analyze variations for DL applications, and use a variety of optimization techniques and algorithms. It doesn't end there, you will also learn how to build a CNN/RNN and more.
Specialization in natural language processing
Link: Specialization in Natural Language Processing
Want to learn the fundamentals behind great language models like ChatGPT and Claude?
Now you can with the Specialization in Natural Language Processing, 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. Dig deeper and learn about recurrent neural networks, LSTM, GRU, and Siamese networks, as well as how to use encoder-decoder, causal, and self-attention to automatically translate full sentences, summarize text, create chatbots, and more.
TensorFlow: Data and Implementation Specialization
Link: TensorFlow: Data and Implementation Specialization
If you looked at the previous courses and saw that TensorFlow is mentioned but you don't need to learn about the rest except TensorFlow, check out this specialization.
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 to mobile devices using TensorFlow Lite. You'll also learn how to more easily access, organize, and process training data using TensorFlow Data Services while exploring more advanced deployment scenarios using TensorFlow Serving, TensorFlow Hub, and TensorBoard.
Wrapping it up
And so, you have a variety of courses that you can use to improve your skills, gain more knowledge and become a specialist in a specific sector of the technology industry.
If you want to be a jack of all trades and be highly competitive, you can choose more than one of these to broaden your horizons!
nisha arya is a data scientist, freelance technical writer, and KDnuggets editor and community manager. She is particularly interested in providing professional data science advice or tutorials and theory-based insights into data science. Nisha covers a wide range of topics and wants to explore the different ways in which artificial intelligence can benefit the longevity of human life. Nisha, a great student, seeks to expand her technological knowledge and her writing skills, while she helps mentor others.