Being a forever student used to be a bad thing, but times change. Nowadays, when not only your University degree, but also your motives and ambitions matter, getting a dream job or building a great career is not possible without lifelong learning: courses, books, conferences, podcasts, and more. Information and industry are constantly changing, and to be a great expert you need to keep up-to-date. This applies to everybody in the industry — teachers, students, scientists, CEOs, etc. Everyone is learning consistently, and so should you.
For starters, you need to figure out what niche you want to explore and grow in; it will help you avoid "informational pollution". For every level of experience, there is a plenty of different options. Data Science and Machine Learning are new fields where everyone can find their own spot and a place for growth and development. Let’s take a look at what this huge market can offer!
During this course, you will master the basics of this widely used open source language, including factors, lists, and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis project.
We have already featured Andrew Ng, the biggest brain in Data Science and Machine Learning industries. He is a top instructor of this course, and they promise to provide a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: Supervised learning: parametric/non-parametric algorithms, support vector machines, kernels, neural networks. Unsupervised learning: clustering, dimensionality reduction, recommender systems, deep learning. Best practices in machine learning: bias/variance theory; innovation process in machine learning and AI. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots, text understanding, computer vision, medical informatics, audio, database mining, and other areas.
During this program, you will gain knowledge to build and train neural network architectures, such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers. Students will also learn how to develop them by using such strategies as Dropout, BatchNorm, Xavier/He initialization, and more. Be prepared to master theoretical concepts and their industry applications using Python and TensorFlow and solve various cases, from speech recognition and music synthesis to chatbots, machine translation, and natural language processing.
This course aims to provide students with comprehensive knowledge of NLP. Students will look into the principles and theories of NLP, as well as into various NLP technologies, including rule-based, statistical, and neural network components. After this course, students will be able to conduct NLP research and develop state-of-the-art NLP systems. If you are a beginner, this course might be a little difficult for you, because you should know basic python programming, college calculus, linear algebra, basic probability & statistics, and foundations of machine learning.
Not just engineers can be interested in AI. This course is created for those professionals who are trying to improve their organizations through AI. The course will explain the common AI terminology, including neural networks, machine learning, deep learning, and data science. You will learn how to spot opportunities to apply AI to problems in your own organization and lots of other useful information bits. Note: Though this course is largely for non-tech folks, engineers can also take it to explore the business aspects of AI.
Today, a life-long education is a must. The AI industry is changing with a crazy speed, and it is hard to stay successful without keeping track of the latest trends. The Data Phoenix team is dedicated to making it easier for you to stay updated; we will prepare lists of trending courses that you can check out and use to nurture yourself. Cheers!
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