Mastering Machine Learning Beginner To Advanced Level(1st Edition)

Book details

Book Price $0 : Mastering Machine Learning Is A Comprehensive Guide Aimed At Helping Readers Develop A Deep Understanding Of Machine Learning Concepts, Techniques, And Applications. This Book Provides An Extensive Overview Of The Machine Learning Landscape, Covering Both Theoretical Aspects And Practical Implementations. Here Is A Summary Of The Key Topics Typically Covered In Such A Book:Introduction To Machine LearningDefinition And Scope: An Introduction To Machine Learning, Its Importance, And Applications In Various Fields.Types Of Machine Learning: Overview Of Supervised, Unsupervised, Semi-supervised, And Reinforcement Learning.Machine Learning Workflow: Steps Involved In Building A Machine Learning Model, From Data Collection To Model Deployment.Data Collection And PreprocessingData Collection: Methods For Collecting Data, Sources Of Data, And Challenges In Data Collection.Data Preprocessing: Techniques For Cleaning And Preparing Data, Including Handling Missing Values, Normalization, And Feature Engineering.Exploratory Data Analysis (EDA): Techniques For Understanding Data Distribution, Identifying Patterns, And Visualizing Data.Supervised LearningRegression: Linear Regression, Polynomial Regression, And Regularization Techniques (Ridge And Lasso).Classification: Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-nearest Neighbors (KNN), And Neural Networks.Model Evaluation: Metrics Such As Accuracy, Precision, Recall, F1 Score, ROC-AUC, And Confusion Matrix.Unsupervised LearningClustering: K-means, Hierarchical Clustering, DBSCAN, And Gaussian Mixture Models.Dimensionality Reduction: Principal Component Analysis (PCA), T-SNE, And Autoencoders.Association Rule Learning: Apriori And Eclat Algorithms.Advanced TopicsEnsemble Methods: Techniques Like Bagging, Boosting (AdaBoost, Gradient Boosting, XGBoost), And Stacking.Semi-Supervised And Self-Supervised Learning: Techniques For Learning From Limited Labeled Data And Leveraging Unlabeled Data.Transfer Learning: Utilizing Pre-trained Models For New Tasks, Common In Image And NLP Tasks.Reinforcement LearningBasics: Key Concepts Including Agents, Environments, States, Actions, Rewards, And Policies.Algorithms: Q-learning, Deep Q-Networks (DQN), And Policy Gradient Methods.Model Deployment And MonitoringModel Deployment: Strategies For Deploying Machine Learning Models In Production Environments.Scalability And Performance Optimization: Techniques For Optimizing Model Performance And Ensuring Scalability.Monitoring And Maintenance: Best Practices For Monitoring Models And Maintaining Performance Over Time.Ethical Considerations And Future TrendsEthical Considerations: Addressing Bias, Fairness, Transparency, And Accountability In Machine Learning.Future Trends: Emerging Trends In Machine Learning, Such As Federated Learning, Automated Machine Learning (AutoML), And Quantum Machine Learning.ConclusionSummary: Recap Of Key Concepts And Techniques Covered In The Book.Further Reading: Recommendations For Further Study And Exploration In Machine Learning. See more