Python Machine Learning: Learn Python in a Week and Master It, 7 Days Crash Course, Book 2
- Length: 212 pages
- Edition: 1
- Language: English
- Publisher: Wiomy Ltd
- Publication Date: 2020-11-10
- ISBN-10: 1914185099
- ISBN-13: 9781914185090
- Sales Rank: #0 (See Top 100 Books)
Would you like to learn how to use Python to generate machine learning models but you think it would be too difficult? Or perhaps you want to automate simple things with your computer but you don’t know how to do it?
Here’s the deal… As a beginner you might think that programming is complex… Learning artificial intelligence coding can take months, and the possibility to give up before mastering it could be high. So, if you have a project to develop you could think on hiring a professional developer to shorten the time. This may seem like a good solution but it is certainly very expensive and if the developer you chose doesn’t perform a proper job you still have to pay for it.
The best solution is to follow a complete programming manual with hands-on projects and practical exercises. Computer Programming Academy structured this guide as a course with seven chapters for seven days and studied special exercises for each section to apply what you have learned step-by-step. This protocol, tested on both total beginners and people who were already familiar with coding, takes advantage of the principle of diving, concentrating learning in one week. The result of this method has been one for both categories of students: the content of the course was learned faster and remembered longer respect the average.
Inside this book, you will go through a first section in which fundamental and basic notions of deep learning are discussed, to get to the next chapters crafted specifically to help you learn advanced coding concepts required to develop training data sets for the production of successful machine learning models.
In the detail, you will learn:
- Why Python is considered the fundamental tool for machine learning
- Deep understanding of the significance of machine learning in our daily lives and why you cannot ignored its importance in 2020
- 12 machine learning models that you must study as a beginner
- The most common mistakes to avoid when you start building machine learning models with Python
- Step-by-step instructions to install required packages to set up a machine learning coding environment
- The algorithms that will make your life easier while coding artificial intelligence
- A proven strategy to process raw data to generate high quality training data sets
- A simple method to build the desired machine learning model in less than 7 days
- The 2 main libraries you need implementing to develop a neural network
- Exercises and quizzes at the end of every chapter to review immediately what you’ve learned
- Extra content that you will appreciate as curious technology enthusiast
Why is this book different? Most of the books on the market only take a brief look into machine learning, showing some of the topics but never going deep concretely. The best way to learn machine learning with Python is by doing and with this manual you will work through applicable projects in order to solidify your knowledge and obtain a huge sense of achievement.
This is what this guide offers to you, even if you’re completely new to programming in 2020 or you are just looking to widen your skills as programmer.
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Introduction Day 1: Introduction to Machine Learning Core concepts of machine learning Basic machine learning terminologies Importance of machine learning Review Quiz Day 2: Machine Learning Models Rule-based systems Case-Based Reasoning Artificial Neural Networks (ANN) Genetic Algorithms Cellular Automata Fuzzy Systems Multi-Agent Systems Swarm Intelligence Reinforcement Learning Hybrid systems Statistics Probabilistic Programming Review Quiz Day 3: Supervised Machine Learning Algorithms Regression Classification Ensemble Methods Review Quiz Day 4: Unsupervised Machine Learning Algorithms Clustering Dimensionality Reduction Transfer Learning Natural Language Processing Word Embedding Apriori machine learning algorithm Review Quiz Day 5: Data Pre-processing and Creation of Training Dataset Overview Data Preprocessing Steps of Data Pre-processing Day 6: Machine Learning Libraries Prerequisites for application of Scikit-Learn library Application of machine learning using Scikit-Learn library Creating Training and Test subsets Building the Machine Learning Model Day 7: Neural Network Training With Tensorflow Fundamentals of Neural Network Training a Neural Network using TensorFlow Extra content Python Programming Python Data Science Conclusion
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