MACHINE LEARNING FOR BEGINNERS: A Complete Beginners Guide To The Concepts, Tools, & Techniques To Build Intelligent Systems With Machine Learning And Artificial Intelligence From Scratch

The first mathematical model of neural networks was published in the 1943 scientific article "A logical calculus of the ideas immanent in nervous activity" by Walter Pitts and Warren McCulloch, marking the beginning of machine learning.

This was followed in 1949 by the publication of Donald Hebb's book, The Organization of Behavior. One of the seminal foundations of machine learning, the book proposed hypotheses about the relationship between behavior, neural networks, and brain activity.

Machine learning is a branch of artificial intelligence (AI) that focuses on building systems that can learn from and make decisions based on data. For beginners, it can be helpful to understand some of the fundamental concepts and techniques used in the field.

This easy-to-understand manual is specially made for both beginners and seniors who want to effectively master MACHINE LEARNING without stress.

This comprehensive manual presents all you need to know about the MACHINE LEARNING in simple, illustrative, and straightforward terms.

Here Is A Preview Of What You Will Learn In This Book:

• What Is Machine Learning
• How Does Machine Learning Work
• Why Should We Learn Machine Learning
• How To Get Started With Machine Learning
• Which Language Is Best For Machine Learning
• Types Of Machine Learning
• Machine Learning Algorithms
• Application Of Machine Learning
• Practical Applications Of Machine Learning
• The Long-Term Prospects Of Ai
• What Is The Statistics And Machine Learning Toolbox
• Tools For Statistics And Machine Learning And Their Features
• How To Use Statistics And Machine Learning Toolbox
• What Are The Best Ways To Integrate SMLT With Other MATLAB Features?
• What Is Data Cleaning
• What Is The Difference Between Data Cleaning And Data Transformation
• Criteria For High-Quality Data
• Efficient Data Cleansing Software And Tools
• What Is Data Preparation For Machine Learning
• Data Preparation And Its Importance For Machine Learning
• Procedures For Preparing Data For Machine Learning Initiatives
• Tools For Preparing Data For Machine Learning
• How To Prepare Data For Machine Learning?
• What Is Machine Learning Regression
• What Are Regression Models Used For
• What Is Simple Linear Regression
• What Is Multiple Linear Regression
• What Is Logistic Regression
• Implementing Machine Learning For Any Company
• What Is KNN (K-Nearest Neighbor) Algorithm
• How Can We Apply The KNN Algorithm?
• How Does The KNN Algorithm Work
• Implementation In Python From Scratch
• Comparing Our Model With SCIKIT-Learn
• Implementation Of KNN In R
• Examining The "Class" Library And Our KNN Predictor Function
• What Is Clustering In Machine Learning And How Does It Work
• Types Of Clustering Algorithms
• Fraud And Detection Application
• What Is Bias In Machine Learning
• How Does Machine Learning Deal With Variance?
• How To Build A Machine Learning Model
• The Six-Step Process For Creating An Ml Model
• How To Improve Machine Learning
• Tips And Tricks