Machine Learning for Absolute Beginners Second Edition by Oliver Theobald

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Machine Learning for Absolute Beginners 2nd Edition

PDF Free Download | Machine Learning for Absolute Beginners Second Edition by Oliver Theobald

Contents of Machine Learning for Absolute Beginners

  • INTRODUCTION
  • WHAT IS MACHINE LEARNING?
  • ML CATEGORIES
  • THE ML TOOLBOX
  • DATA SCRUBBING
  • SETTING UP YOUR DATA
  • REGRESSION ANALYSIS
  • CLUSTERING
  • BIAS & VARIANCE
  • ARTIFICIAL NEURAL NETWORKS
  • DECISION TREES
  • ENSEMBLE MODELING
  • BUILDING A MODEL IN PYTHON
  • MODEL OPTIMIZATION
  • FURTHER RESOURCES
  • DOWNLOADING DATASETS
  • FINAL WORD

Introduction to Machine Learning Book

Machines have come a long way since the Industrial Revolution. They continue to fill factory floors and manufacturing plants,

but now their capabilities extend beyond manual activities to cognitive tasks that, until recently, only humans were capable of performing.

Judging song competitions, driving automobiles, and mopping the floor with professional chess players are three examples of the specific complex tasks machines are now capable of simulating.

But their remarkable feats trigger fear among some observers. Part of this fear nestles on the neck of survivalist insecurities, where it provokes the deep-seated question of what if?

What if intelligent machines turn on us in a struggle of the fittest? What if intelligent machines produce offspring with capabilities that humans never intended to impart to machines?

What if the legend of the singularity is true? The other notable fear is the threat to job security, and if you’re a truck driver or an accountant, there is a valid reason to be worried.

According to the British Broadcasting Company’s (BBC) interactive online resource Will a robot take my job?, professions such as bar worker (77%), waiter (90%),

chartered accountant (95%), receptionist (96%), and taxi driver (57%) each have a high chance of becoming automated by the year 2035.

But research on planned job automation and crystal ball gazing with respect to the future evolution of machines and artificial intelligence (AI) should be read with a pinch of skepticism.

AI technology is moving fast, but broad adoption is still an unchartered path fraught with known and unforeseen challenges. Delays and other obstacles are inevitable.

Nor is machine learning a simple case of flicking a switch and asking the machine to predict the outcome of the Super Bowl and serve you a delicious martini.

Machine learning is far from what you would call an out-of-the-box solution

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