[Udemy 100% Off]-Practical Introduction to Machine Learning - 100% Free Udemy Discount Coupons For Online Courses

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Tuesday, June 11, 2019

[Udemy 100% Off]-Practical Introduction to Machine Learning

[Udemy 100% Off]-Practical Introduction to Machine Learning
Description

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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