Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition) (Learn AI & Python for Beginners)

by Oliver Theobald

According to joint research from the Office for National Statistics and Deloitte

UK

published by the BBC in 2015, job professions including bar worker (77%), waiter (90%), chartered accountant (95%), receptionist (96%), and taxi driver (57%) have a hi...

machine learning relies on statistical algorithms managed and overseen by skilled individuals called data scientists and machine learning engineers.

Coding is the other indispensable part of machine learning, which includes managing and manipulating large amounts of data.

machine learning requires Python, C++, R or another programming language.

introduced machine learning as a subfield of computer science that gives computers the ability to learn without being explicitly programmed.

key characteristic of machine learning is the concept of self-learning.

This refers to the application of statistical modeling to detect patterns and improve performance based on data and empirical information; all without direct programming commands.

machine learning is heavily dependent on code input.

machines can perform a set task using input data rather than relying on a direct input command.

Comparison of Input Command vs Input Data

where outputs or decisions are pre-defined by the programmer,

machine learning uses data as input to build a decision model.

Decisions are generated by deciphering relationships and patterns in the data using probabilistic reasoning, trial and error, and other...

This means that the output of the decision model is determined by the contents of the input data rather than any pre-set r...

The human programmer is still responsible for feeding the data into the model, selecting an appropriate algorithm and tweaking its settings (called hyperparameters) in a bid to reduce prediction error, but ultimately the machine and develop...

decoding complex patterns in the input data, the model uses machine learning to find connections without human help.

machine learning is the ability to improve predictions based on experience.

machine learning utilizes exposure to data to improve its decision making.

insufficient input data restricts the model’s ability to deconstruct underlying patterns in the data and limits its capacity to respond to potential variance and random phenomena found in live data.

Exposure to input data thereby deepens the model’s understanding of patterns, including the significance of changes in the data, and to construct an effective self-learning model.

as more data is analyzed, the model might also find exceptions and incorrect assumptions that render the model susceptible to bad predictions.

While data is used to source the self-learning process, more data doesn’t always equate to better decisions; the input data must be relevant.

The Hidden Battles to Collect Your Data and Control Your World,

adding irrelevant data can be counter-productive to achieving a desired result.

the amount of input data should be compatible with the processing resources and time that is available.

In machine learning, the input data is typically split into training data and test data.

training data, which is the initial reserve of data used to develop the model.

the model can be trained to automatically detect these errors

without direct human interference.

After you have developed a model based on patterns extracted from the training data and you are satisfied with the accuracy of its predictions, you can test the model ...

Machine learning, data mining, artificial intelligence, and computer programming all fall under the umbrella of computer science, which encompasses everything related to the design and use of computers.

data science comprises methods and systems to extract knowledge and insights from data with the aid of computers.

AI is driving the development of machines capable of simulating cognitive abilities.

AI spans numerous subfields that are popular and newsworthy today. These subfields include search and planning, reasoning and knowledge representation, perception, natural language processing (NLP), and of course, machine learning.

machine learning overlaps with data mining—a sister discipline based on discovering and unearthing patterns in large datasets.

Data mining: Practical machine learning tools and techniques with Java is said to have originally been titled Practical machine learning, but for marketing reasons “data mining” was later appended to the title.

seeks out patterns and relationships that are yet to be mined and is, thus, well-suited for understanding large datasets with complex patterns.

Data Mining: Concepts and Techniques, data mining developed as a result of advances in data collection and database management beginning in the early 1980s[8] and an urgent need to make sense of progressively larger and complicated datasets.[9]

data mining focuses on analyzing input variables to predict a new output, machine learning extends to analyzing ...

supervised learning techniques that compare known combinations of input and output variables to discern patterns and make predictions, and reinforcement learning which randomly trials a massive n...

unsupervised learning, generates predictions based on the analysis of input variables wi...