Practical Machine Learning : A Beginner's Guide with Ethical Insights / Ally S. Nyamawe, Mohamedi M. Mjahidi, Noe E. Nnko, Salim A. Diwani, Godbless G. Minja, Kulwa Malyango.

Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- About the authors -- Preface -- Acknowledgments -- Glossary -- Chapter 1: Fundamentals of machine learning -- 1.1 What is machine learning? -- 1.2 A brief history of machine learning -- 1.3 Types of machine learning algorithms -- 1.3.1 Supervised learning -- 1.3.1.1 Types of supervised learning -- 1.3.2 Unsupervised learning -- 1.3.2.1 Types of unsupervised learning -- 1.3.2.1.1 Clustering -- 1.3.2.1.2 Association Rules -- 1.3.3 Semi-supervised learning -- 1.3.4 Reinforcement learning -- 1.4 Relationship between machine learning and other computer science disciplines -- 1.4.1 Machine learning and artificial intelligence -- 1.4.2 Machine learning and data science -- 1.4.3 Machine learning and traditional programming -- 1.4.4 Machine learning and deep learning -- 1.4.5 Machine learning and natural language processing -- 1.4.6 Machine learning and computer vision -- 1.4.7 Machine learning and generative AI -- 1.5 The importance of machine learning -- 1.6 When do we need machine learning? -- 1.7 Machine learning skills -- 1.7.1 Essential technical skills for machine learning professionals -- 1.7.2 Essential soft skills for machine learning professionals -- 1.8 What do machine learning professionals do? -- 1.9 Real-world applications of machine learning -- 1.10 Machine learning and ethical concerns -- 1.11 Summary -- Further Reading -- Chapter 2: Mathematics for machine learning -- 2.1 Linear algebra -- 2.1.1 Scalars -- 2.1.2 Vectors -- 2.1.2.1 Vector addition -- 2.1.2.2 Vector subtraction -- 2.1.2.3 Vector multiplication -- 2.1.3 Matrix -- 2.1.3.1 Matrix addition -- 2.1.3.2 Matrix subtraction -- 2.1.3.3 Matrix multiplication -- 2.1.3.4 Matrix transpose -- 2.1.3.5 Square and rectangular matrix -- 2.1.3.6 Triangular matrix -- 2.1.3.7 Diagonal matrix -- 2.1.3.8 Identity matrix.

2.1.3.9 Matrix determinant -- 2.1.3.10 Adjugate of a matrix -- 2.1.3.11 Singular and non-singular matrix -- 2.1.3.12 Matrix inversion -- 2.1.3.13 Eigenvectors and eigenvalues -- 2.2 Statistics concepts -- 2.2.1 Use of statistics in machine learning -- 2.2.2 Types of statistics -- 2.2.2.1 Descriptive statistics -- 2.2.2.2 Inferential statistics -- 2.2.3 Types of data -- 2.2.3.1 Numerical data -- 2.2.3.2 Categorical data -- 2.2.4 Data distribution -- 2.2.4.1 Normal distribution in statistics -- 2.2.4.2 Skewness -- 2.2.4.3 Central limit theorem -- 2.2.5 Applied statistical inference -- 2.2.5.1 Linear regression -- 2.3 Probability theory -- 2.3.1 Sample spaces and events -- 2.3.2 Probability -- 2.3.3 Probability measures -- 2.3.4 Conditional probability -- 2.3.5 Bayes' theorem -- 2.3.6 Random variables -- 2.3.7 Expectation -- 2.3.8 Variance -- 2.3.9 Standard deviation -- 2.3.9.1 Cumulative distribution function -- 2.3.9.2 Probability mass function -- 2.3.9.3 Probability density function -- 2.3.9.4 Discrete distributions -- 2.3.9.5 Bernoulli distribution -- 2.3.9.6 Binomial distribution -- 2.3.9.7 Poisson distribution -- 2.3.9.8 Uniform distribution -- 2.3.9.9 Continuous distributions -- 2.3.9.10 Normal distribution (Gaussian distribution) -- 2.3.9.11 Uniform distribution -- 2.4 Calculus -- 2.4.1 Differentiation -- 2.4.2 Integration -- 2.4.3 Gradient -- 2.4.4 Linear function -- 2.4.5 Quadratic function -- 2.4.6 Sigmoid function -- 2.5 Geometry and trigonometry -- 2.5.1 Geometry in data representation -- 2.5.2 Trigonometric geometry in model optimization -- 2.6 Information theory -- 2.6.1 Entropy and information content -- 2.6.2 Mutual information and feature selection -- 2.6.3 Cross-entropy and model evaluation -- 2.7 Clustering -- 2.7.1 K-Means clustering algorithm -- 2.8 Summary -- Further Reading -- Chapter 3: Data preparation.

3.1 Overview of machine learning process -- 3.2 Business problem identification -- 3.3 Success criteria definition -- 3.4 Data collection -- 3.4.1 Nature of data -- 3.4.2 Data sources -- 3.4.3 Data curation -- 3.4.4 Data labeling -- 3.4.5 Ethical considerations in data collection -- 3.5 Data preprocessing -- 3.5.1 Data cleaning -- 3.5.1.1 Removing duplicate or irrelevant values -- 3.5.1.2 Fixing structural errors -- 3.5.1.3 Detecting and removing outliers -- 3.5.1.4 Handling missing values -- 3.5.1.5 Validation -- 3.5.2 Data Transformation -- 3.5.2.1 Binning -- 3.5.2.2 Encoding -- 3.5.2.3 Data normalization -- 3.5.2.4 Standardization -- 3.5.3 Exploratory data analysis -- 3.5.3.1 Data summarization -- 3.5.3.2 Data visualization -- 3.5.4 Types of exploratory data analysis -- 3.5.4.1 Univariate -- 3.5.4.2 Bivariate -- 3.5.5 Multivariate -- 3.5.6 Dimensionality reduction -- 3.5.6.1 Feature selection -- 3.5.6.1.1 Backward feature elimination -- 3.5.6.1.2 Forward feature selection -- 3.5.6.2 Feature extraction -- 3.5.7 Data balancing -- 3.6 Summary -- Further Reading -- Chapter 4: Machine learning operations -- 4.1 Model development -- 4.1.1 Dataset splitting -- 4.1.1.1 Hold-out -- 4.1.1.2 Cross-validation -- 4.1.2 Choosing an algorithm -- 4.1.2.1 Problem understanding -- 4.1.2.2 Algorithm capabilities -- 4.1.2.3 Computational resources -- 4.1.3 Model training -- 4.1.4 Model evaluation -- 4.1.5 Overfitting and underfitting -- 4.1.6 Model optimization -- 4.1.6.1 Exhaustive search -- 4.1.6.2 Gradient descent -- 4.1.6.3 Stochastic gradient descent -- 4.1.6.4 Evolutionary optimization algorithms -- 4.2 Model deployment -- 4.3 Model monitoring -- 4.4 Ethical considerations in machine learning operations (MLOps) -- 4.5 Summary -- Further Reading -- Chapter 5: Machine learning software and hardware requirements -- 5.1 Programming languages.

5.1.1 Python programming language -- 5.1.1.1 Python code editors and IDEs -- 5.1.1.2 Python libraries -- 5.1.2 R programming language -- 5.1.2.1 R programming code editors and IDEs -- 5.1.2.2 R programming libraries -- 5.1.3 MATLAB -- 5.1.3.1 MATLAB code editors and IDEs -- 5.1.3.2 MATLAB libraries -- 5.1.4 Other programming languages -- 5.1.4.1 Java programming -- 5.1.5 Java programming code editors and IDEs -- 5.1.6 Java ML libraries -- 5.1.6.1 C++ programming -- 5.1.7 C++ programming code editors and IDEs -- 5.1.8 C++ programming libraries -- 5.1.9 Criteria for choosing programming language for machine learning -- 5.1.9.1 Library and framework support -- 5.1.9.2 Robust and extensive community support -- 5.1.9.3 Ease of learning and use -- 5.1.9.4 Flexibility, scalability, and efficiency -- 5.1.9.5 Integration with other tools and software -- 5.1.9.6 Industry adoption -- 5.2 No-code tools -- 5.3 Experiment tracking tools -- 5.4 Pre-trained models repositories -- 5.5 Datasets and model tracking tools -- 5.6 AutoML hyperparameter optimization tools -- 5.7 Machine learning life cycle tools -- 5.8 User interface development tools -- 5.9 Explainable AI tools -- 5.10 Version control systems -- 5.11 Machine learning hardware requirements -- 5.12 Operating systems requirements -- 5.13 Processor and memory requirements -- 5.13.1 CPU -- 5.13.2 GPU -- 5.13.3 TPU -- 5.13.4 RAM -- 5.13.5 Storage -- 5.14 Cloud computing services for machine learning -- 5.15 Summary -- Further Reading -- Chapter 6: Responsible AI and explainable AI -- 6.1 Responsible AI -- 6.2 Explainable AI -- 6.3 Privacy concerns in machine learning -- 6.4 Ethical implications of machine learning -- 6.5 Accountability and trust in AI -- 6.6 Global case studies on AI governance and regulation -- 6.6.1 Formulation of AI strategies and guidelines in Africa -- 6.6.2 European Union AI Act.

6.6.3 Global partnership on AI -- 6.6.4 China AI ethics guidelines -- 6.7 Human-centric artificial intelligence -- 6.8 Responsible AI best practices -- 6.9 AI impact assessment case studies -- 6.10 Artificial intelligence sovereignty -- 6.11 Summary -- Further Reading -- Chapter 7: Artificial general intelligence -- 7.1 Categories of artificial intelligence -- 7.2 What makes an intelligence general? -- 7.3 Approaches for developing AGI -- 7.4 Philosophy of mind -- 7.5 Challenges of artificial general intelligence -- 7.6 Potential benefits and risks of artificial general intelligence -- 7.7 Indicators of the presence of artificial general intelligence -- 7.8 Robotics and embodied intelligence -- 7.9 Artificial super intelligence -- 7.10 Summary -- Further Reading -- Chapter 8: Machine learning step-by-step practical examples -- 8.1 Case study 1: Classification problem -- 8.1.1 Problem definition -- 8.1.1.1 Description of the dataset -- 8.1.2 Loading libraries -- 8.1.3 Loading dataset -- 8.1.4 Data summary -- 8.1.4.1 Descriptive statistics -- 8.1.4.2 Data visualization -- 8.1.5 Data preprocessing -- 8.1.5.1 Data cleaning -- 8.1.5.1.1 Outliers -- 8.1.5.1.2 Missing values -- 8.1.5.2 Data standardization -- 8.1.6 Split-out the dataset -- 8.1.7 Choosing classification algorithm -- 8.1.8 Training the model -- 8.1.8.1 Model evaluation -- 8.1.8.2 Saving the model -- 8.1.8.3 Inferencing -- 8.2 Case study 2: Regression problem -- 8.2.1 Problem definition -- 8.2.1.1 Description of the dataset -- 8.2.2 Loading libraries -- 8.2.3 Loading dataset -- 8.2.4 Data summary -- 8.2.4.1 Descriptive statistics -- 8.2.4.2 Data visualization -- 8.2.5 Data preprocessing -- 8.2.5.1 Data cleaning -- 8.2.5.1.1 Outliers -- 8.2.5.1.2 Missing values -- 8.2.5.2 Feature selection -- 8.2.5.3 Data transformation -- 8.2.6 Choosing regression algorithm -- 8.2.7 Training the model.

8.2.7.1 Model equation.

This is a core resource for students and instructors of machine learning and data science looking for beginner-friendly material which offers real-world applications and takes ethical discussions into account.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2025. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.