Machine Learning in 30 Days, The Complete Beginner’s Guide, Jain A., 2025

Machine Learning in 30 Days, The Complete Beginner’s Guide, Jain A., 2025.
     
   Mastering machine learning requires a structured approach to ensure consistent progress and deep comprehension of concepts. This book provides a 30-day roadmap, guiding you from the basics to advanced ML techniques with step-by-step explanations, practical examples, and realworld applications.




What is Machine Learning?
Machine Learning (ML) is a subset of artificial intelligence (AI) that enables computers to learn from vast amounts of data and progressively improve their performance without requiring explicit programming. Unlike traditional programming, where a developer must define every rule and logic explicitly, ML models autonomously identify patterns, recognize relationships, and derive data-driven insights. This ability allows machines to make increasingly accurate predictions, classifications, and recommendations as they process more information.

The paradigm shift introduced by ML has led to groundbreaking advancements in numerous industries. For instance, in healthcare, ML assists in diagnosing diseases, analyzing medical images, and recommending personalized treatments. In finance, ML models detect fraudulent transactions and optimize investment strategies. Similarly, ML has transformed e-commerce, transportation, robotics, and numerous other fields by driving automation and intelligent decision-making.

CONTENTS.
Introduction.
Day 1: Introduction to Machine Learning.
Day 2: Setting Up Your ML Environment.
Day 3: Understanding Data and Data Preprocessing.
Day 4: Exploratory Data Analysis (EDA).
Day 5: Introduction to Linear Regression.
Day 6: Multiple Linear Regression and Feature Selection.
Day 7: Logistic Regression for Classification.
Day 8: Decision Trees and Random Forests.
Day 9: Understanding Support Vector Machines (SVMs).
Day 10: К-Nearest Neighbors (KNN) Algorithm.
Day 11: Introduction to Clustering.
Day 12: Principal Component Analysis (PCA) and Dimensionality Reduction.
Day 13: Introduction to Naive Bayes Classifier.
Day 14: Working with Real-World Datasets.
Day 15: Introduction to Model Evaluation and Hyperparameter Tuning.
Day 16: Introduction to Artificial Neural Networks (ANNs).
Day 17: Deep Learning with TensorFlow and Keras.
Day 18: Introduction to Convolutional Neural Networks (CNNs).
Day 19: Recurrent Neural Networks (RNNs) and Time-Series Forecasting.
Day 20: Introduction to Reinforcement Learning.
Day 21: Natural Language Processing (NLP) with Python.
Day 22: Introduction to Generative AI and Transformers.
Day 23: Working with Real-World ML Projects.
Day 24: Introduction to MLOps and Model Deployment.
Day 25: Debugging and Optimizing ML Models.
Day 26: AutoML and No-Code ML Tools.
Day 27: Trends in AI and Emerging Technologies.
Day 28: Final Project - Implementing an End-to-End ML Application.
Day 29: Deploying ML Models at Scale.
Day 30: Wrapping Up & Next Steps.
Appendix.



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