Practical Introduction to Machine Learning -

Practical Introduction to Machine Learning

IT & Software


Machine learning is the technology behind self driving cars, smart speakers, recommendations, and sophisticated predictions. Recent advances in algorithms, technology, and the availability of vast amounts of data allow machines to solve problems that were once considered out of reach. Machine learning is an exciting and rapidly growing field full of possibilities, but it can be intimidating at first.

If you want to learn how machine learning can be applied in your organization without lots of math or code, then this course is for you. There's more to a successful ML project that just creating models and writing code. Identifying suitable problems, collecting, preparing and curating data sets, validating results, and maintaining quality over time are just as important as writing code. These challenges require a variety of skills, many of which are not technical.

Whether you're a manager, business analyst, software architect, or someone looking to change careers, there's a place for you in a machine learning project. This course is aimed at giving you the knowledge you need to be productive in a changing economy where machines are climbing the corporate ladder.

Course Content

  • Introduction to Artificial Intelligence and Machine Learning

    • What is it? Why now?

    • Applications of machine learning

    • AI timeline

    • Human learning

    • How machines learn from data

  • Machine Learning Models

    • Classical and Deep Learning Models

    • Feature engineering

    • Neural networks and backpropagation

    • Neural network breakthroughs

    • Ultimate accuracy

    • Expert performance

  • Learning Style

    • Supervised, Unsupervised, Reinforcement, and Transfer Learning

    • Amount of training data required

  • Practical examples

    • Natural language text

    • Sentiment analysis

    • Amazon Comprehend

    • Clustering

    • Image recognition

    • Speech to text and text to speech

    • Language translation

    • Amazon Transcribe, Polly, and Translate

  • Development process

    • Data collection and preparation

    • Choosing a model

    • Bias and variance

    • GPU training with Google Colaboratory

    • CPUs, GPUs, and FPGAs

    • Retraining and feedback loops

  • Next steps

    • For managers

    • For business analysts

    • For software architects and developers

    • Economics of machine learning

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