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The best of the best badass hackers and security experts are using machine learning to break and secure systems. This course has everything you need to join their ranks.
In this one-of-its-kind course, we will be covering all from the fundamentals of cybersecurity data science, to the state of the art. We will be setting up a cybersecurity lab, building classifiers to detect malware, training deep neural networks and even breaking CAPTCHA systems using machine learning.
If you've tried to enter the super hot field of cybersecurity and machine learning, but faced rejection after rejection, needing experience to get experience, feeling hopeless that the demand and pay are so high, but nothing you are doing is letting you in, this is your chance to gain an edge over the competition. This is your chance to get credentials and real experience.
If you are looking to break into the field of cybersecurity data science, pick up on the bleeding edge tools, and become the best in the field of cybersecurity, this course is for you.
We will be using python and scikit learn for majority of our machine learning, and keras, a wrapper for tensorflow, for deep learning. This course is hands on and practical. Consequently, a student is expected to put in the work and not be shy about getting their hands dirty with some malware!
|Learning Style||Self-Paced Learning|
|Course Duration||1 Hour|
Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.
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