Library
Cuantum trackFull access

Machine Learning Hero

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

Author
Cuantum Tech.
Chapters
8
Reading time
~ 17h
Level
Professional
Language
English
Edition
2025
Your progress0%
Start reading

Chapters & sections

8 chapters - 45 sections

Chapter 1: Introduction to Machine Learning

0/6
1.1 Introduction to Machine Learning28m1.2 Role of Machine Learning in Modern Software Development21m1.3 AI and Machine Learning Trends in 202420m1.4 Overview of the Python Ecosystem for Machine Learning23mPractical Exercises Chapter 16mChapter 1 Summary3m

Chapter 2: Python and Essential Libraries for Data Science

0/8
2.1 Python Basics for Machine Learning23m2.2 NumPy for High-Performance Computations38m2.3 Pandas for Advanced Data Manipulation52m2.4 Matplotlib, Seaborn, and Plotly for Data Visualization53m2.5 Scikit-learn and Essential Machine Learning Libraries45m2.6 Introduction to Jupyter and Google Colab Notebooks13mPractical Exercises: Chapter 25mChapter 2 Summary3m

Quiz Part 1: Foundations of Machine Learning and Python

0/4
Chapter 1: Introduction to Machine Learning2mChapter 2: Python and Essential Libraries for Data Science2mBonus Question1mAnswers1m

Chapter 3: Data Preprocessing and Feature Engineering

0/8
3.1 Data Cleaning and Handling Missing Data64m3.2 Advanced Feature Engineering47m3.3 Encoding and Handling Categorical Data44m3.4 Data Scaling, Normalization, and Transformation Techniques43m3.5 Train-Test Split and Cross-Validation33m3.6 Data Augmentation for Image and Text Data31mPractical Exercises Chapter 36mChapter 3 Summary3m

Chapter 4: Supervised Learning Techniques

0/6
4.1 Linear and Polynomial Regression22m4.2 Classification Algorithms60m4.3 Advanced Evaluation Metrics (Precision, Recall, AUC-ROC)27m4.4 Hyperparameter Tuning and Model Optimization46mPractical Exercises Chapter 46mSummary Chapter 43m

Chapter 5: Unsupervised Learning Techniques

0/6
5.1 Clustering (K-Means, Hierarchical, DBSCAN)46m5.2 Principal Component Analysis (PCA) and Dimensionality Reduction34m5.3 t-SNE and UMAP for High-Dimensional Data34m5.4 Evaluation Techniques for Unsupervised Learning36mPractical Exercises Chapter 56mSummary Chapter 53m

Chapter 6: Practical Machine Learning Projects

0/3
6.1 Project 1: Feature Engineering for Predictive Analytics25m6.2 Project 2: Predicting Car Prices Using Linear Regression17m6.3 Project 3: Customer Segmentation Using K-Means Clustering15m

Quiz Part 2: Data Preprocessing and Classical Machine Learning

0/4
Chapter 3: Data Preprocessing and Feature Engineering2mChapter 4: Supervised Learning Techniques1mChapter 5: Unsupervised Learning Techniques2mAnswers Section1m