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Machine Learning with Python

14 chapters and 56 canonical sections synced from the Cuantum content database.

Author
Cuantum Tech.
Chapters
14
Reading time
~ 11h
Level
Professional
Language
English
Edition
2025
Your progress0%
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Chapters & sections

14 chapters - 56 sections
Chapter 01

Chapter 1: Introduction

0/3
11.1 Introduction to Machine Learning12m21.2 Role of Machine Learning in Software Engineering12m31.3 Overview of Python for Machine Learning12m
Chapter 02

Chapter 2: Python and Essential Libraries

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12.1 Python Crash Course - Part 112m32.2 NumPy for Numerical Computation12m42.3 Pandas for Data Manipulation12m52.4 Matplotlib and Seaborn for Data Visualization12m62.5 Scikit-learn for Machine Learning12m
Chapter 03

Chapter 3: Data Preprocessing

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13.1 Data Cleaning12m23.2 Feature Engineering12m33.3 Handling Categorical Data12m43.4 Data Scaling and Normalization12m53.5 Train-Test Split12m63.6 Practical Exercises12m
Chapter 04

Chapter 4: Supervised Learning

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14.1 Regression Analysis12m24.2 Classification Techniques12m34.3 Evaluation Metrics for Supervised Learning12m44.4 Practical Exercises12m
Chapter 05

Chapter 5: Unsupervised Learning

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15.1 Clustering Techniques12m25.2 Dimensionality Reduction12m35.3 Evaluation Metrics for Unsupervised Learning12m45.4 Practical Exercises12m
Chapter 06

Chapter 6: Introduction to Neural Networks and Deep Learning

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16.1 Perceptron and Multi-Layer Perceptron12m26.2 Backpropagation and Gradient Descent12m36.3 Overfitting, Underfitting, and Regularization12m46.4 Practical Exercises12m
Chapter 07

Chapter 7: Deep Learning with TensorFlow

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17.1 Introduction to TensorFlow12m27.2 Building and Training Neural Networks with TensorFlow12m37.3 Saving and Loading Models in TensorFlow12m47.4 Practical Exercises12m
Chapter 08

Chapter 8: Deep Learning with Keras

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18.1 Introduction to Keras12m28.2 Building and Training Neural Networks with Keras12m38.3 Saving and Loading Models in Keras12m48.4 Practical Exercises12m
Chapter 09

Chapter 9: Deep Learning with PyTorch

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19.1 Introduction to PyTorch12m29.2 Building and Training Neural Networks with PyTorch12m39.3 Saving and Loading Models in PyTorch12m49.4 Practical Exercises12m
Chapter 10

Chapter 10: Convolutional Neural Networks

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110.1 Introduction to CNNs12m210.2 Implementing CNNs with TensorFlow, Keras, and PyTorch12m310.3 Practical Applications of CNNs12m410.4 Practical Exercises12m
Chapter 11

Chapter 11: Recurrent Neural Networks

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111.1 Introduction to RNNs12m211.2 Implementing RNNs with TensorFlow, Keras, and PyTorch12m311.3 Practical Applications of RNNs12m411.4 Practical Exercise12m
Chapter 12

Chapter 12: Advanced Deep Learning Concepts

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112.1 Autoencoders12m212.2 Generative Adversarial Networks (GANs)12m312.3 Practical Exercise12m
Chapter 13

Chapter 13: Practical Machine Learning Projects

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113.1 Project 1: Predicting House Prices with Regression12m213.2 Project 2: Sentiment Analysis with Naive Bayes12m313.3 Project 3: Image Classification with Convolutional Neural Networks12m
Chapter 14

Chapter 14: Future Trends and Ethical Considerations

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114.1 Reinforcement Learning12m214.2 Explainable AI12m314.3 Ethical Considerations in Machine Learning12m414.4 Future Trends in Machine Learning for Software Engineering12m