BOOKS - Programming Machine Learning Machine Learning Basics Concepts + Artificial In...
Programming Machine Learning Machine Learning Basics Concepts + Artificial Intelligence + Python Programming + Python Machine Learning - Kavishankar Panchtilak 2024 PDF Kavis Web Designer BOOKS
ECO~19 kg CO²

2 TON

Views
13592

Telegram
 
Programming Machine Learning Machine Learning Basics Concepts + Artificial Intelligence + Python Programming + Python Machine Learning
Author: Kavishankar Panchtilak
Year: 2024
Pages: 558
Format: PDF
File size: 37.0 MB
Language: ENG



Pay with Telegram STARS
The book "Programming Machine Learning Machine Learning Basics Concepts + Artificial Intelligence + Python Programming + Python Machine Learning" is a comprehensive guide that provides readers with a deep understanding of machine learning concepts, artificial intelligence, and Python programming. The book covers the basics of machine learning, including supervised and unsupervised learning, neural networks, and deep learning, as well as the practical applications of these techniques in real-world scenarios. The first chapter of the book introduces the concept of machine learning and its importance in today's technology landscape. The author explains how machine learning has revolutionized the way we approach problem-solving and decision-making, and how it has enabled us to automate complex tasks with ease. The chapter also covers the history of machine learning, from its early beginnings to the current state-of-the-art techniques used in industry and academia. The second chapter delves into the fundamentals of artificial intelligence, exploring the different types of AI, including narrow or weak AI, general or strong AI, and the various subfields of AI such as natural language processing, computer vision, and robotics. The chapter also discusses the ethical implications of AI, such as privacy concerns, bias, and job displacement.
''

You may also be interested in:

Explainable Machine Learning Models and Architectures
Machine Learning for Physicists A hands-on approach
Tkinter, Data Science, And Machine Learning
Machine Learning, revised and updated edition
Metaheuristics for Machine Learning Algorithms and Applications
IBM Watson Solutions for Machine Learning
Data Protection The Wake of AI and Machine Learning
Secrets of Machine Learning How It Works and What It Means for You
Practical Machine Learning Innovations in Recommendation
Machine Learning by Tutorials (1st Edition)
Blockchain and Machine Learning for IoT Security
Building Machine Learning Pipelines (First Edition)
Machine Learning for Neuroscience: A Systematic Approach
Probabilistic Machine Learning Advanced Topics
Machine Learning Techniques and Industry Applications
Blockchain and Machine Learning for e-Healthcare Systems
Probabilistic Numerics: Computation as Machine Learning
Blockchain and Machine Learning for IoT Security
Machine Learning Approaches in Financial Analytics
Machine Learning with Python: Master Pandas
Machine Learning for Complex and Unmanned Systems
Machine and Deep Learning Algorithms and Applications
Supervised Machine Learning for Text Analysis in R
Pathways to Machine Learning and Soft Computing
Probability and Statistics for Machine Learning: A Textbook
Machine Learning for Factor Investing: R Version
Probability and Statistics for Machine Learning A Textbook
Image Processing and Machine Learning, Vol 2
Distributed Machine Learning Patterns (MEAP v7)
Mathematical Analysis of Machine Learning Algorithms
Informatics and Machine Learning From Martingales to Metaheuristics
Machine Learning with Python Theory and Applications
Practical Machine Learning Illustrated with KNIME
Machine Learning and Optimization for Engineering Design
Practical Machine Learning in R (2021 Update)
Regression and Machine Learning for Education Sciences Using R
Machine Learning for Emotion Analysis in Python
Machine Learning Fundamentals A Concise Introduction
Machine Learning with Python A Comprehensive Guide
Random Matrix Methods for Machine Learning