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Introduction to AI, Machine Learning & Deep Learning Essentials is an engaging, hands-on training program designed. Fast-growing, critical technologies are currently shaping the future of IT, development, and analytics.

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Unlimited Duration

Last Updated

March 4, 2021

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In this course you learn about:

· The What and Why of AI, Machine Learning & Deep Learning – why is this important and exciting?

· Getting the Basics: High-level skills, vocabulary and terminology

· AI, Machine Learning and Deep Learning – what are the differences and uses?

· Latest trends and research

· Who’s Using It and to What Advantage?

· How to adopt AI, ML and DL

· Hands-on Machine Learning – algorithms, neural networks, natural language processing & more

· Tools and Languages: Python, R, Spark, TensorFlow, Keras

· Deep Learning Essentials

Course Curriculum

    • What is Data Science? 00:00:00
    • New Ways of Thinking about and using Data 00:00:00
    • Challenges of processing 00:00:00
    • Technologies 00:00:00
    • Strategies 00:00:00
    • Where does data science fit in? 00:00:00
    • DS ecosystem – AI, Machine Learning, Deep Learning 00:00:00
    • Data and the Scientific Method 00:00:00
    • Data Science vs. Data Engineering 00:00:00
    • Sharing Results with Colleagues 00:00:00
    • Recording experiments 00:00:00
    • The Data Science Team members 00:00:00
    • Data Science Infrastructure 00:00:00
    • Current Tools, Trends & Technologies 00:00:00
    • Applying Data Science to Your Industry 00:00:00
    • AI – How did we get here? 00:00:00
    • Recent advances in data, hardware 00:00:00
    • Cutting edge research and applications 00:00:00
    • Getting the basics: Core terms and vocabulary 00:00:00
    • Who is leveraging this and why 00:00:00
    • Overview of ML – what’s the difference? 00:00:00
    • Related examples of ML algorithms and applications 00:00:00
    • Surrounding tools and technologies: Python and Spark 00:00:00
    • Supervised vs. Unsupervised 00:00:00
    • Classification 00:00:00
    • Regression 00:00:00
    • Clustering 00:00:00
    • Dimensionality Regression 00:00:00
    • Ensemble Methods 00:00:00
    • What is it, and how is this different than AI and ML? 00:00:00
    • Who’s using Deep Learning and Why 00:00:00
    • Deep Learning algorithms and applications 00:00:00
    • Surrounding tools and technologies: Python, TensorFlow, Keras 00:00:00
    • Rules Systems 00:00:00
    • Feedback loops 00:00:00
    • RETE and beyond 00:00:00
    • Expert Systems in practice 00:00:00
    • Neural Networks 00:00:00
    • Recurrent Neural Networks 00:00:00
    • Long-Short Term Memory Networks 00:00:00
    • Applying Neural Networks 00:00:00
    • Language and Semantic Meaning 00:00:00
    • Bigrams, Trigrams, and n-Grams 00:00:00
    • Root stemming and branching 00:00:00
    • NLP in the world 00:00:00
    • Image processing and Identification 00:00:00
    • Facial Analysis 00:00:00
    • Audio Processing 00:00:00
    • Analyzing Streaming Video 00:00:00
    • Real-world AV processing 00:00:00
    • Sentiment: The beginnings of emotional understanding 00:00:00
    • Sentiment indicators 00:00:00
    • Sentiment Sampling 00:00:00
    • Algorithmic Trading on Sentiment 00:00:00
    • Predicting Elections 00:00:00

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