Who should read this?
- Technical people who wish to revisit machine learning quickly.
- Non-technical people who want an introduction to machine learning but have no idea about where to start with.
- Anybody who thinks machine learning is “hard.”
Why Machine Learning?
Artificial Intelligence will shape our future more powerfully than any other innovation, this century. The rate of acceleration of AI is already astonishing. After two AI winters over the past four decades, rapidly growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage, the game is now changing.
In this post, you will explore core machine learning concepts and algorithms behind all the technology that powers much of our day-to-day lives. By the end of this, you would be able to describe how it functions at the conceptual level and be equipped with the tools to start building similar models and applications yourself.
Prerequisites to start with machine learning?
To understand the concepts presented, it is recommended that one meets the following prerequisites:
- In-depth knowledge of intro-level algebra: One should be comfortable with variables and coefficients, linear equations, calculus and understanding of graphs.
- Proficiency in programming basics, and some experience coding in Python: No prior experience with machine learning is required, but one should feel comfortable reading and writing Python code that contains basic programming constructs, such as function definitions/invocations, lists, dictionaries, loops, and conditional expressions.
- Basic knowledge of the following Python libraries:
The Semantic Tree:
Artificial intelligence is the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals.
Machine learning is a subfield of artificial intelligence. Its goal is to enable computers to learn on their own. A machine’s learning algorithm enables it to identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models.