Mastery in Advanced Machine Learning & Applied AI™

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

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.

Requirements
Anyone can learn this class with simplicity end to end

Description
This comprehensive program is designed to transform learners into experts in advanced machine learning and applied AI, covering both supervised and unsupervised learning techniques. The course focuses on the practical application of cutting-edge methods and algorithms, enabling learners to tackle complex real-world problems across various domains.Course Outline1. Introduction to Machine LearningUnderstanding the basics of machine learning and its types: supervised, unsupervised, and reinforcement learning.2. Machine Learning – Reinforcement LearningDive deep into reinforcement learning, covering key concepts such as agents, environments, and rewards.3. Introduction to Supervised LearningLearn the principles of supervised learning, including classification and regression tasks.4. Machine Learning Model Training and EvaluationExplore how to train machine learning models and evaluate their performance using metrics like accuracy, precision, recall, and F1 score.5. Machine Learning Linear RegressionUnderstand the concept of linear regression and its application in predicting continuous values.6. Machine Learning – Evaluating Model FitTechniques for assessing how well a model fits the data, including error metrics and residual analysis.7. Application of Machine Learning – Supervised LearningHands-on application of supervised learning techniques to real-world problems.8. Introduction to Multiple Linear RegressionExplore multiple linear regression and its application when dealing with multiple predictor variables.9. Multiple Linear Regression – Evaluating Model PerformanceLearn how to assess the performance of multiple linear regression models using metrics like R² and Adjusted R².10. Machine Learning Application – Multiple Linear RegressionPractical exercises applying multiple linear regression to complex datasets.11. Machine Learning Logistic RegressionStudy logistic regression for binary classification tasks.12. Machine Learning Feature Engineering – Logistic RegressionTechniques to optimize feature selection and transformation for better model performance in logistic regression.13. Machine Learning Application – Logistic RegressionPractical application of logistic regression to classify data based on binary outcomes.14. Machine Learning Decision TreesLearn the fundamentals of decision trees and how they can be used for both classification and regression tasks.15. Machine Learning – Evaluating Decision Trees PerformanceAssessing decision trees’ performance using criteria such as Gini index and Information Gain.16. Machine Learning Application – Decision TreesApply decision tree algorithms to real-world datasets for classification tasks.17. Machine Learning Random ForestsUnderstand ensemble learning through random forests and their advantages over single decision trees.18. Master Machine Learning Hyperparameter TuningLearn how to fine-tune machine learning models for optimal performance using techniques such as grid search and random search.19. Machine Learning Decision Trees Random ForestApply and compare decision trees and random forests to real-world problems.20. Machine Learning – Support Vector Machines (SVM)Master the theory and application of SVM for classification tasks, including the role of hyperplanes and support vectors.21. Machine Learning – Kernel Functions in Support Vector Machines (SVM)Understand the use of kernel functions to transform non-linear data into a higher-dimensional space for better classification.22. Machine Learning Application – Support Vector Machines (SVM)Practical applications of SVMs in classification tasks.23. Machine Learning K-Nearest Neighbor (KNN) AlgorithmStudy the KNN algorithm, a simple yet powerful method for classification and regression tasks.24. Machine Learning Application – KNN AlgorithmImplement KNN for real-world data analysis.25. Machine Learning Gradient Boosting AlgorithmsMaster advanced ensemble methods like gradient boosting, which combine weak models to create a strong model.26. Master Hyperparameter Tuning in Machine LearningLearn advanced techniques for optimizing model parameters to improve predictive performance.27. Machine Learning Application of Gradient BoostingHands-on experience applying gradient boosting algorithms to complex datasets.28. Machine Learning Model Evaluation MetricsStudy the various evaluation metrics for different types of machine learning models, such as precision, recall, F1 score, and confusion matrix.29. Machine Learning ROC Curve and AUC ExplainedLearn how to use the ROC curve and AUC to assess the performance of classification models.30. Unsupervised Learning Explained | Clustering & Dimensionality ReductionAn introduction to unsupervised learning techniques such as clustering and dimensionality reduction.31. Unsupervised Learning Explained – Anomaly DetectionStudy anomaly detection techniques to identify outliers and abnormal patterns in data.32. Mastering K-Means Clustering in Unsupervised LearningUnderstand the K-Means algorithm and its application in clustering data.33. Iterating K-Means Clustering Algorithm in Unsupervised LearningLearn how to refine and optimize K-Means clustering for better results.34. Application of K-Means Clustering Algorithm in Unsupervised LearningHands-on experience applying K-Means clustering to real-world problems.35. Mastering Hierarchical Clustering in Unsupervised LearningUnderstand hierarchical clustering techniques and their applications in unsupervised learning.36. Unsupervised Learning Dendrogram VisualizationVisualize hierarchical clustering results using dendrograms to better understand data structures.37. Application Hierarchical Clustering Explained – Master Unsupervised LearningApply hierarchical clustering to solve practical unsupervised learning tasks.38. Advanced Clustering Techniques Unsupervised Learning with DBSCANStudy DBSCAN, an advanced clustering algorithm that handles noise and non-spherical clusters.39. Advanced Clustering Techniques – Unsupervised Learning with DBSCAN AdvantagesLearn the advantages of DBSCAN over traditional clustering techniques like K-Means.40. Introduction to Principal Component Analysis (PCA)Understand PCA, a dimensionality reduction technique that simplifies high-dimensional data.41. Selecting Principal Component Analysis (PCA)Learn how to select the most important principal components to reduce data dimensionality effectively.42. Application of Principal Components in PCAHands-on application of PCA to reduce dimensionality and improve model performance.43. Unsupervised Learning with Linear Discriminant Analysis (LDA)Learn LDA, a dimensionality reduction technique commonly used in classification tasks.44. PCA vs LDA | Machine Learning Dimensionality ReductionCompare PCA and LDA to understand their differences and appropriate use cases.45. Application of LDA | Machine Learning Dimensionality ReductionApply LDA for dimensionality reduction in supervised learning tasks.46. Unsupervised Learning with t-SNEStudy t-SNE (t-Distributed Stochastic Neighbor Embedding) for nonlinear dimensionality reduction.47. Unsupervised Learning – How t-SNE Works – Mastering Dimensionality ReductionUnderstand how t-SNE works and how it can be applied to visualize high-dimensional data.48. Application of t-SNE – Mastering Dimensionality ReductionApply t-SNE to explore data patterns and visualize complex datasets in lower dimensions.49. Unsupervised Learning Model Evaluation Metrics – A Complete GuideLearn about evaluation metrics used to assess the performance of unsupervised learning models.50. Dimensionality Reduction Evaluation MetricsStudy the metrics used to evaluate the effectiveness of dimensionality reduction techniques.51. Unsupervised Learning HyperparameterExplore hyperparameter tuning in unsupervised learning to optimize model performance.52. Unsupervised Learning with Bayesian Optimization – A Complete GuideLearn Bayesian Optimization and its applications in improving the performance of unsupervised learning algorithms.53. Introduction to Association RuleUnderstand association rule mining and its application in market basket analysis.54. Association Rule Mining – Confidence & Support ExplainedDive into confidence and support metrics used to evaluate association rules.55. Apriori Algorithm Association Rule Mining & Market Basket AnalysisStudy the Apriori algorithm and its application to market basket analysis for uncovering product relationships.56. Apriori Algorithm Step-by-Step ExplainedA detailed explanation of the Apriori algorithm and how to apply it to real-world data.This course equips students with the tools and knowledge to excel in machine learning, from foundational concepts to advanced applications, making it ideal for those looking to master the field of AI and machine learning.

Who this course is for
Anyone who wants to learn future skills and become Data Scientist, Sr. Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.

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