Mastery in Advanced Machine Learning & Applied AI™

Mastery in Advanced Machine Learning & Applied AI™

Unlocking Next-Level AI Solutions with Cutting-Edge Machine Learning Techniques and Real-World Applications

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What you'll learn

• Introduction to the foundational concepts of Machine Learning.
• Understanding Reinforcement Learning and its applications in decision-making.
• Introduction to Supervised Learning and its role in predictive modeling.
• Techniques for training and evaluating Machine Learning models effectively.
• In-depth exploration of Linear Regression and its application in predictive tasks.
• Evaluating the fit of machine learning models for better accuracy.
• Applying Supervised Learning techniques in real-world data scenarios.
• Introduction to Multiple Linear Regression for modeling multiple variables.
• Evaluating the performance of Multiple Linear Regression models.
• Practical applications of Multiple Linear Regression in solving business problems.
• Mastery of Logistic Regression and its use in classification tasks.
• Feature engineering techniques to improve Logistic Regression models.
• Application of Logistic Regression for classification and prediction.
• Understanding Decision Trees and their use in machine learning.
• Evaluating the performance of Decision Trees for optimal predictions.
• Applying Decision Trees to real-world problems in various industries.
• Mastering Random Forests and their advantages for predictive tasks.
• Techniques for Hyperparameter Tuning to optimize machine learning models.
• Combining Decision Trees and Random Forests for enhanced predictive power.
• Mastering Support Vector Machines (SVM) for classification tasks.
• Understanding Kernel Functions in SVM to handle non-linear data.
• Real-world applications of Support Vector Machines for classification problems.
• Implementing K-Nearest Neighbor (KNN) algorithm for supervised learning.
• Practical applications of KNN algorithm for classification and prediction.
• Understanding Gradient Boosting algorithms and their power in predictive tasks.
• Mastering Hyperparameter Tuning to improve Gradient Boosting models.
• Application of Gradient Boosting in various machine learning problems.
• Mastering evaluation metrics to assess the performance of machine learning models.
• Understanding and using ROC Curve and AUC for model performance assessment.
• Introduction to Unsupervised Learning concepts, focusing on clustering and dimensionality reduction.
• Mastering Anomaly Detection techniques for identifying outliers in data.
• Advanced techniques in K-Means Clustering for unsupervised learning tasks.
• Iterating the K-Means algorithm to improve clustering results.
• Practical applications of K-Means Clustering in real-world scenarios.
• Mastering Hierarchical Clustering techniques for data segmentation.
• Visualizing Hierarchical Clustering using Dendrograms for clear insights.
• Applying PCA in real-world problems to reduce data dimensions.
• Understanding Linear Discriminant Analysis (LDA) and its role in unsupervised learning.
• Comparing PCA vs LDA for dimensionality reduction techniques.
• Applying LDA for dimensionality reduction and classification in machine learning.
• Mastering t-SNE for advanced dimensionality reduction and visualization.
• Understanding how t-SNE works and using it to visualize high-dimensional data.
• Understanding and applying dimensionality reduction evaluation metrics.
• Hyperparameter tuning techniques for optimizing unsupervised learning models.
• Using Bayesian Optimization for improving the performance of unsupervised models.
• Introduction to Association Rule Mining for extracting patterns from data.
• Understanding Confidence and Support in Association Rule Mining for actionable insights.
• Using the Apriori Algorithm in Association Rule Mining for Market Basket Analysis.
• Step-by-step explanation and application of the Apriori Algorithm in real-world analysis.

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