The Machine Learning Handbook, A Practical Guide, To Data-Driven Innovation, Vanita B.

The Machine Learning Handbook, A Practical Guide, To Data-Driven Innovation, Vanita B.
     
Фрагмент из книги.
The future of machine learning is marked by exciting advancements and emerging trends that promise to revolutionize the field. Explainable AI (XAI) aims to make models more transparent and interpretable, fostering trust and accountability. Automated Machine Learning (AutoML) democratizes machine learning by automating complex tasks and making it accessible to nonexperts. Federated learning enables collaborative model training while preserving data privacy and security. Quantum machine learning leverages the power of quantum computing to solve complex problems more efficiently. By staying informed about these future trends, you can leverage cutting-edge technologies to drive innovation and create impactful solutions in the evolving landscape of machine learning.




Time Series Analysis and Forecasting.
Introduction to Time Series Analysis.
Time series analysis involves analyzing data points collected or recorded at specific time intervals. This type of analysis is crucial for understanding trends, patterns, and seasonal variations in data over time. Time series data is widely used in various fields, including finance, economics, healthcare, and environmental science.

Time Series Data and Components.
Time series data consists of observations taken sequentially over time. Understanding the components of time series data is essential for effective analysis and forecasting.

Contents.
1. Chapter 1: Introduction to Machine Learning.
What is Machine Learning?.
History and Evolution of Machine Learning.
Types of Machine Learning: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.
Key Concepts and Terminology.
2. Chapter 2: Data Collection and Preparation.
Sources of Data: Internal and External.
Data Quality and Integrity.
Data Cleaning and Preprocessing.
Exploratory Data Analysis (EDA).
3. Chapter 3: Feature Engineering and Selection.
Understanding Features and Their Importance.
Creating New Features.
Feature Scaling and Normalization.
Feature Selection Techniques.
4. Chapter 4: Supervised Learning Techniques.
Regression Models: Linear Regression, Polynomial Regression, Ridge Regression.
Classification Models: Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM).
Model Evaluation and Metrics.
5. Chapter 5: Unsupervised Learning Techniques.
Clustering Algorithms: К-Means, Hierarchical Clustering, DB-SCAN.
Dimensionality Reduction: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE).
Anomaly Detection: Isolation Forest, One-Class SVM.
6. Chapter 6: Neural Networks and Deep Learning.
Introduction to Neural Networks: Perceptrons, Activation Functions, Backpropagation.
Deep Learning Architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM).
Transfer Learning and Pre-trained Models.
7. Chapter 7: Natural Language Processing (NLP).
Text Preprocessing: Tokenization, Lemmatization, Stemming.
Feature Extraction: TF-IDF, Word2Vec, GloVe.
NLP Models: Recurrent Neural Networks (RNNs), Transformers, BERT, GPT.
8. Chapter 8: Time Series Analysis and Forecasting.
Time Series Data and Components.
Forecasting Methods: ARIMA, SARIMA, Exponential Smoothing.
Seasonal Decomposition and Trend Analysis.
9. Chapter 9: Real-World Data Science Projects.
Project 1: Predicting House Prices.
Project 2: Sentiment Analysis of Social Media Posts.
Project 3: Customer Segmentation for Marketing.
Project 4: Anomaly Detection in IoT Data.
10. Chapter 10: Model Deployment and Monitoring.
Saving and Loading Models.
Deploying Models in Production.
Model Monitoring and Maintenance.
11. Chapter 11: Ethical Considerations in Machine Learning.
Fairness and Bias in Machine Learning.
Privacy and Data Security.
Transparency and Explainability.
Accountability in AI Systems.
12. Chapter 12: Future Trends in Machine Learning.
Explainable AI (XAI).
Automated Machine Learning (AutoML).
Federated Learning.
Quantum Machine Learning.



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