Machine Learning for Absolute Beginners
A Step-by-Step Introduction to AI and Data Science (No Coding Required – Learn the Basics of Algorithms and Predictive Analytics)By Jonathan K. Hari
Length2h 25m
About this audiobook
Machine Learning for Absolute Beginners
Learn AI and Data Science Step by Step
Step into the world of AI with this beginner-friendly guide to machine learning. No prior coding or math skills required—just curiosity and a desire to learn.
Here's a Preview of What You'll Learn...
The difference between AI, machine learning, and deep learning
Supervised, unsupervised, and reinforcement learning
Key concepts: data, features, labels, and models
Algorithms from linear regression to neural networks
Data preparation and cleaning essentials
Introduction to Python and libraries like Scikit-Learn, TensorFlow, PyTorch
Real-world applications and ethical considerations
Build a solid foundation and explore the exciting possibilities of machine learning today.
Audiobook details
GenreTechnology, Business and Economics
Length2 hrs 25 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateApr 25, 2016
LanguageEnglish
Table of contents
1Introduction
2What is Machine Learning? An Introduction
3The Difference Between AI, Machine Learning, and Deep Learning
4Types of Machine Learning: Supervised, Unsupervised & Reinforcement Learning
5Key Concepts: Data, Features, Labels, and Models
Show all chaptersShow less
6Understanding Algorithms: From Linear Regression to Neural Networks
7Getting Started with Python for Machine Learning
8Exploring Popular Libraries: Scikit-Learn, TensorFlow, and PyTorch
9Preparing Data: Cleaning, Normalization, and Feature Engineering
10Training and Testing: How to Build a Model
11Overfitting, Underfitting, and Model Evaluation
12Real-World Applications of Machine Learning
13Ethical Considerations: Bias, Fairness, and Privacy
14Tuning Your Model: Hyperparameters and Optimization
15Deploying a Machine Learning Model
16The Future of Machine Learning and Next Steps for Beginners
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