Mastery in Advanced Machine Learning & Applied AI™
Unlocking Next-Level AI Solutions with Cutting-Edge Machine Learning Techniques and Real-World Applications
5.00 (2 reviews)
28
students
25 hours
content
Jan 2025
last update
$69.99
regular price
What you will 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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6197817
udemy ID
22/09/2024
course created date
29/11/2024
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