"ML, Neural Networks & NLP: Master the Fundamentals of Machine Learning, Neural Networks, and Natural Language Processing" is your comprehensive guide to understanding and mastering the core concepts of these cutting-edge technologies.
Starting with an in-depth exploration of Machine Learning (ML), the book provides a solid foundation by covering topics such as the history of ML, supervised and unsupervised learning, deep learning, and ethical considerations. Each concept is elucidated with clarity and depth, ensuring that even beginners can grasp the fundamentals with ease.
Moving on to Neural Networks (NN), the book delves into the building blocks of NN, including feedforward, convolutional, recurrent, and generative adversarial networks. With step-by-step instructions and practical examples using Python, readers will gain hands-on experience in creating and optimizing neural networks for real-world applications.
Natural Language Processing (NLP) is another crucial area covered extensively in the book. From the basics of linguistics to advanced topics like deep learning for NLP and transformer architecture, readers will learn how to process, analyze, and understand human language effectively. Practical applications such as sentiment analysis, machine translation, and conversational agents are explored in detail, along with ethical considerations and recent advancements in the field.
Throughout the book, readers will find a wealth of resources, hands-on projects, and interview preparation tips to reinforce their learning and prepare them for success in the rapidly evolving fields of ML, NN, and NLP. Whether you're a student, researcher, or industry professional, "ML, Neural Networks & NLP" is your essential companion for mastering these transformative technologies and staying ahead of the curve.
Publisher: Et Tu Code
Year: 2024
Language: English
Pages: 397
Preface
Introduction to Machine Learning
History of Machine Learning
Early Concepts and Foundations
The Emergence of Artificial Intelligence
Statistical Approaches
Connectionism and Neural Networks
Machine Learning Boom in the 20th Century
Rise of Big Data and Computational Power
Deep Learning Revolution
Supervised Learning
Unsupervised Learning
Deep Learning
Feature Engineering
Evaluation Metrics
Model Deployment
Reinforcement Learning
Popular Machine Learning Algorithms
Linear Regression
Logistic Regression
Decision Trees
Random Forest
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
K-Means Clustering
Neural Networks
Gradient Boosting
Principal Component Analysis (PCA)
Recurrent Neural Networks (RNN)
Natural Language Processing (NLP) Algorithms
Popular Tools and Libraries in Machine Learning
NumPy
Pandas
Scikit-Learn
TensorFlow
PyTorch
Keras
Jupyter Notebooks
Matplotlib and Seaborn
SciPy
XGBoost
Ethical Considerations in Machine Learning
Machine Learning in Real-world Applications
Recent Advances and Trends
Introduction to Neural Networks
Building Blocks of Neural Networks
Feedforward Neural Networks
Training Neural Networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Generative Adversarial Networks (GANs)
Transfer Learning
Comparison between Artificial Neural Networks and Human Neurons
Structural Differences
Functionality Variations
Learning Mechanisms
Parallel Processing
Biological Inspiration in AI
Step-by-Step Creation of a Neural Network using Python
Step 1: Importing Libraries
Step 2: Data Preparation
Step 3: Building the Neural Network Architecture
Step 4: Initializing Weights and Biases
Step 5: Choosing the Loss Function and Optimizer
Step 6: Model Training
Step 7: Evaluation and Testing
Step 8: Model Deployment
Popular Examples of Neural Network Usage
Image Classification with CNNs
Sentiment Analysis in Natural Language Processing (NLP)
Handwriting Recognition with RNNs
Speech Recognition using Deep Learning
Autonomous Vehicle Control with Deep Reinforcement Learning
Predictive Maintenance with Time Series Analysis
Fraud Detection using Anomaly Detection Models
Generative Adversarial Networks (GANs) for Image Generation
Neural Network Optimization Techniques
Neural Networks in Real-world Applications
Ethical Considerations in Neural Network Deployment
Neural Network Interpretability
Neuroevolution
Neuromorphic Computing
Introduction to Natural Language Processing
Foundations of Linguistics
Syntax in NLP
Semantics and NLP
Morphology and NLP
Phonetics and NLP
Text Processing Techniques
Statistical Language Models
Machine Learning in NLP
Deep Learning for NLP
Named Entity Recognition (NER)
Sentiment Analysis
Machine Translation
Conversational Agents
Ethical Considerations in NLP
Recent Advancements in NLP
NLP in Large Language Models (LLM)
LLM Architecture
Fine-Tuning for NLP Tasks
Challenges and Opportunities
NLP in LLM Use Cases
Word Embeddings
Attention Mechanism
Transformer Architecture
Multimodal NLP
Explainable AI in NLP
NLP Applications in Industry
Future Trends in NLP
Hands-On Projects
NLP Resources and Tools
Interview Preparation
Conclusion
Glossary
Appendix
Bibliography
