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Machine Learning Hero

8 chapters and 45 canonical sections synced from the Cuantum content database.

Author
Cuantum Tech.
Chapters
8
Reading time
~ 9h
Level
Professional
Language
English
Edition
2025
Your progress0%
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Chapters & sections

8 chapters - 45 sections
Chapter 01

Chapter 1: Introduction to Machine Learning

0/6
11.1 Introduction to Machine Learning12m21.2 Role of Machine Learning in Modern Software Development12m31.3 AI and Machine Learning Trends in 202412m41.4 Overview of the Python Ecosystem for Machine Learning12m6Chapter 1 Summary12m5Practical Exercises Chapter 112m
Chapter 02

Chapter 2: Python and Essential Libraries for Data Science

0/8
12.1 Python Basics for Machine Learning12m22.2 NumPy for High-Performance Computations12m32.3 Pandas for Advanced Data Manipulation12m42.4 Matplotlib, Seaborn, and Plotly for Data Visualization12m52.5 Scikit-learn and Essential Machine Learning Libraries12m62.6 Introduction to Jupyter and Google Colab Notebooks12m8Chapter 2 Summary12m7Practical Exercises: Chapter 212m
Chapter 03

Quiz Part 1: Foundations of Machine Learning and Python

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4Answers12m3Bonus Question12m1Chapter 1: Introduction to Machine Learning12m2Chapter 2: Python and Essential Libraries for Data Science12m
Chapter 04

Chapter 3: Data Preprocessing and Feature Engineering

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13.1 Data Cleaning and Handling Missing Data12m23.2 Advanced Feature Engineering12m33.3 Encoding and Handling Categorical Data12m43.4 Data Scaling, Normalization, and Transformation Techniques12m53.5 Train-Test Split and Cross-Validation12m63.6 Data Augmentation for Image and Text Data12m8Chapter 3 Summary12m7Practical Exercises Chapter 312m
Chapter 05

Chapter 4: Supervised Learning Techniques

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14.1 Linear and Polynomial Regression12m24.2 Classification Algorithms12m34.3 Advanced Evaluation Metrics (Precision, Recall, AUC-ROC)12m44.4 Hyperparameter Tuning and Model Optimization12m5Practical Exercises Chapter 412m6Summary Chapter 412m
Chapter 06

Chapter 5: Unsupervised Learning Techniques

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15.1 Clustering (K-Means, Hierarchical, DBSCAN)12m25.2 Principal Component Analysis (PCA) and Dimensionality Reduction12m35.3 t-SNE and UMAP for High-Dimensional Data12m45.4 Evaluation Techniques for Unsupervised Learning12m5Practical Exercises Chapter 512m6Summary Chapter 512m
Chapter 07

Chapter 6: Practical Machine Learning Projects

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16.1 Project 1: Feature Engineering for Predictive Analytics12m26.2 Project 2: Predicting Car Prices Using Linear Regression12m36.3 Project 3: Customer Segmentation Using K-Means Clustering12m
Chapter 08

Quiz Part 2: Data Preprocessing and Classical Machine Learning

0/4
4Answers Section12m1Chapter 3: Data Preprocessing and Feature Engineering12m2Chapter 4: Supervised Learning Techniques12m3Chapter 5: Unsupervised Learning Techniques12m