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Uncover beneficial machine studying methods you possibly can perceive and apply utilizing simply high-school math.
In Grokking Machine Studying you’ll be taught:
Supervised algorithms for classifying and splitting information
Strategies for cleansing and simplifying information
Machine studying packages and instruments
Neural networks and ensemble strategies for complicated datasets
Grokking Machine Studying teaches you tips on how to apply ML to your tasks utilizing solely commonplace Python code and excessive school-level math. No specialist information is required to sort out the hands-on workout routines utilizing Python and available machine studying instruments. Full of easy-to-follow Python-based workout routines and mini-projects, this guide units you on the trail to changing into a machine studying professional.
Buy of the print guide features a free eBook in PDF, Kindle, and ePub codecs from Manning Publications.
In regards to the know-how
Uncover highly effective machine studying methods you possibly can perceive and apply utilizing solely highschool math! Put merely, machine studying is a set of methods for information evaluation primarily based on algorithms that ship higher outcomes as you give them extra information. ML powers many cutting-edge applied sciences, similar to advice methods, facial recognition software program, sensible audio system, and even self-driving automobiles. This distinctive guide introduces the core ideas of machine studying, utilizing relatable examples, participating workout routines, and crisp illustrations.
In regards to the guide
Grokking Machine Studying presents machine studying algorithms and methods in a means that anybody can perceive. This guide skips the confused tutorial jargon and gives clear explanations that require solely fundamental algebra. As you go, you’ll construct fascinating tasks with Python, together with fashions for spam detection and picture recognition. You’ll additionally choose up sensible expertise for cleansing and making ready information.
What’s inside
Supervised algorithms for classifying and splitting information
Strategies for cleansing and simplifying information
Machine studying packages and instruments
Neural networks and ensemble strategies for complicated datasets
In regards to the reader
For readers who know fundamental Python. No machine studying information mandatory.
In regards to the writer
Luis G. Serrano is a analysis scientist in quantum synthetic intelligence. Beforehand, he was a Machine Studying Engineer at Google and Lead Synthetic Intelligence Educator at Apple.
Desk of Contents
1 What’s machine studying? It’s common sense, besides accomplished by a pc
2 Sorts of machine studying
3 Drawing a line near our factors: Linear regression
4 Optimizing the coaching course of: Underfitting, overfitting, testing, and regularization
5 Utilizing strains to separate our factors: The perceptron algorithm
6 A steady strategy to splitting factors: Logistic classifiers
7 How do you measure classification fashions? Accuracy and its mates
8 Utilizing likelihood to its most: The naive Bayes mannequin
9 Splitting information by asking questions: Choice bushes
10 Combining constructing blocks to achieve extra energy: Neural networks
11 Discovering boundaries with type: Help vector machines and the kernel technique
12 Combining fashions to maximise outcomes: Ensemble studying
13 Placing all of it in observe: An actual-life instance of knowledge engineering and machine studying
Writer : Manning
Publication date : Dec 14 2021
Language : English
Print size : 512 pages
ISBN-10 : 1617295914
ISBN-13 : 978-1617295911
Merchandise weight : 907 g
Dimensions : 18.73 x 3.3 x 23.5 cm
