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From the Ground Up: Developing Your Artificial Intelligence Career - PROPRIUS


Thanks to big data and machine learning, more sophisticated artificial intelligence systems are around the corner. This means that it is a great time to master your technical skills and learn as much as you can about these emerging fields; jumping into the artificial intelligence career pool now could yield great results. Here are a few tips to help you prepare for your first steps toward a career in artificial intelligence. No matter how advanced you think you are when it comes to artificial intelligence, reviewing the basics will still do you a world of good. Before you dedicate yourself to a specialization like computer vision or natural language processing, master the basics and check to see if you're having a good time.

Natural Language Processing Specialization


Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future.

Practical Data Science Specialization


Goto Practical Data Science Specialization Become a Practical Data Science expert. This Specialization 80,604 recent views Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.

Convolutional Neural Networks


The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.

Natural Language Processing with Sequence Models


In Course 3 of the Natural Language Processing Specialization, offered by, Please make sure that you've completed Course 2 and are familiar with the basics of TensorFlow. If you'd like to prepare additionally, you can take Course 1: Neural Networks and Deep Learning of the Deep Learning Specialization. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning.