Manralai-Ai Career Master: Less Theory and more Python Implementation — The apply first lern from experts tutorial book covering all implementations.
Statistics Frameworks 一Problem Solving Frameworks for Data Science 一 Required Python — Data Structures(Strings,Array) and Algorithms in Python 一 Data Analysis & Chart Selection Frameworks 一 Machine Learning Frameworks — Data Preprocessing Pipelines — Feature Engineering Pipelines — Feature Selection Frameworks 一MLOP's — Model in Production — Monitoring and Testing Deployments 一 Deep Learning Basics — TensorFlow2 & PyTorch — Computer Vision Frameworks — Image Classification, Object Detection Frameworks — NLP Frameworks and many more...

Manralai-Ai Career Master: Less Theory and more Python Implementation is a apply first learn from experts tutorial book for Ai world, with explanation of theory using Python implementations.

• Theory: Starting from a uniform mathematical framework, this book derives the theory and algorithms for learning Ai, including all major algorithms of Ai world, such as Machine Learning, Computer Vision and NLP algorithms.
• Practice: Every chapter is accompanied by high quality implementation based on Python 3, Scikit-learn, TensorFlow and other required libs, fully compatible with Windows, MacOS, and Linux.

Version

• First English version of this book is being written by Mukesh Manral, and live on git.

• Free Version

Source Codes

• Source Codes(GitHub)

— The first Ai Problem Solving book for Data Science
Cracking complex Business Problems into Data Science Problems

Ai Problem Solving Frameworks is an apply first learn from experts book for Data Science and Ai. It covers the fundamental theory of problem solving in Data Science and Ai, helping companies to achive results leveraging data.

• Theory: Without preparatory knowledge of advanced mathmatics, this book leverages the core Ai concept to efficiently crack complex business problems.
• Practice: This book leads you to solve any complex business problem, for your application in a super easy way.

Version

• Simplified English version of this book is being written by Mukesh Manral, and live on git.

• Free Version

Source Codes

• SOURCE CODES(GITHUB)

Data Preprocessing Frameworks with apply first learn from experts approach is a guide for beginner to intermediate-level Machine Learning developers looking to take the next leap forward in Ai field: realtime.
With Realtime Machine Learning pipelines, you'll be able to quickly get up to speed on how Data Preprocessing pielines are being done in industry, how it is going to affect the future projects.
Only lightly touching on the heavy theory and instead focusing on a practical approach, Realtime Data Preprocessing Frameworks will guide you through building and refining your first ML Project. After your initial Frameworks given in the book, you'll immediately jump into the process of creating some realtime Industry grade Data Preprocessing pipelines.
The future of the Ai is realtime. Grab your hoverboard.

Versions

• Simplified English version of this book is being written by Mukesh Manral, and live on git.

• Free Version

Source Codes

• SOURCE CODES(GITHUB)

Feature Engineering Frameworks: Secrets to Building Better Machine Learning Models is designed by keeping in mind both new bee and professionals. This text takes a logical approach to the presentation of core points, moving step-by-step from the basics to more advanced approaches, with proper industry grade frameworks being introduced at the appropriate time. The book is divided into three parts:

• Part One covers the basics of Features say : Features Type, Features Characteristics, Feature Imputations, Multivariate Imputations, Categorical Features Encoding.
• Part Two introduces to Feature Simple Transformations, Feature Mathematical Transformations, Discretisation, Outliers, Feature Scaling, Mixed Features Operations, DateTime Features.
• Part Three explores key aspects of Text Features, Transactions & Time Series Features, Feature Combinations, Industry Grage End to End Feature Engineering pipelines.

Versions

• Simplified English version of this book is being written by Mukesh Manral, and live on git.

• Free Version

Source Codes

• Source Codes(GitHub)

Feature Selection Frameworks: Guide to Boosting Machine Learning Model Performance with Images is designed by keeping in mind both new bee and professionals. This text takes a logical approach to the presentation of core points, moving step-by-step from the basics to more advanced approaches, with proper industry grade frameworks being introduced at the appropriate time. The book is divided into four parts:

• Part One covers the Filter Methods say Mutual Information, Chi-Squre Distribution, ANOVA, Method used in KDD competition - 2009, Basic Selection Methods + Statistical Methods Pipelines.
• Part Two covers the Wrapper Methods say Forward Feature Selection, Backward Feature Selection, Exhaustive Feature Selection.
• Part Three covers the Embeddd Methods say Linear Model Coefficients(Logistic-Linear Regression Coefficients), Effect of Regularization on Coefficients, Selection Methods + Correlation + Embedded Pipelines, Selection Methods + Correlation + Tree Importance Pipelines.
• Part Four covers the Hybrid Methods say Feature Shuffling, Recursive Elimination, Recursive Feature Addition.

Versions

• Simplified English version of this book is being written by Mukesh Manral, and live on git.

• Free Version

Source Codes

• Source Codes(GitHub)