Accredited Expert-Level IBM Data Science Certification Advanced Video Course
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Martha Thomas
Jul 21, 2025, 1:00:02 AM7/21/25
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Visit This Web URL https://masterytrail.com/product/accredited-expert-level-ibm-data-science-certification-advanced-video-course Lesson 1: Introduction to Data Science
1.1 Definition and Scope of Data Science
1.2 The Role of a Data Scientist
1.3 Key Skills Required for Data Science
1.4 Data Science Workflow
1.5 Tools and Technologies in Data Science
1.6 Real-World Applications of Data Science
1.7 Ethical Considerations in Data Science
1.8 Career Paths in Data Science
1.9 Industry Trends in Data Science
1.10 Getting Started with Data Science Projects
Lesson 2: Statistics for Data Science
2.1 Descriptive Statistics
2.2 Probability Theory
2.3 Inferential Statistics
2.4 Hypothesis Testing
2.5 Confidence Intervals
2.6 Correlation and Regression
2.7 Statistical Distributions
2.8 Central Limit Theorem
2.9 Bayesian Statistics
2.10 Statistical Modeling Techniques
Lesson 3: Data Wrangling and Preprocessing
3.1 Data Cleaning Techniques
3.2 Handling Missing Data
3.3 Data Transformation
3.4 Feature Engineering
3.5 Data Normalization and Standardization
3.6 Dealing with Outliers
3.7 Data Aggregation
3.8 Data Merging and Joining
3.9 Working with Time Series Data
3.10 Automating Data Preprocessing
Lesson 4: Exploratory Data Analysis (EDA)
4.1 Understanding Data Distribution
4.2 Visualizing Data with Matplotlib
4.3 Interactive Visualizations with Plotly
4.4 Correlation Analysis
4.5 Dimensionality Reduction Techniques
4.6 Clustering for EDA
4.7 Anomaly Detection
4.8 Feature Importance Analysis
4.9 EDA Reporting and Documentation
4.10 Advanced EDA Techniques
Lesson 5: Machine Learning Fundamentals
5.1 Supervised Learning
5.2 Unsupervised Learning
5.3 Reinforcement Learning
5.4 Model Selection and Evaluation
5.5 Overfitting and Underfitting
5.6 Bias-Variance Tradeoff
5.7 Cross-Validation Techniques
5.8 Hyperparameter Tuning
5.9 Model Interpretability
5.10 Ethical Considerations in Machine Learning
Lesson 6: Supervised Learning Algorithms
6.1 Linear Regression
6.2 Logistic Regression
6.3 Decision Trees
6.4 Random Forests
6.5 Support Vector Machines (SVM)
6.6 K-Nearest Neighbors (KNN)
6.7 Naive Bayes Classifier
6.8 Gradient Boosting Machines
6.9 Neural Networks Basics
6.10 Ensemble Methods
Lesson 7: Unsupervised Learning Algorithms
7.1 K-Means Clustering
7.2 Hierarchical Clustering
7.3 DBSCAN
7.4 Principal Component Analysis (PCA)
7.5 t-SNE for Visualization
7.6 Association Rule Learning
7.7 Anomaly Detection Techniques
7.8 Gaussian Mixture Models
7.9 Self-Organizing Maps (SOM)
7.10 Advanced Clustering Techniques
Lesson 8: Deep Learning Fundamentals
8.1 Introduction to Neural Networks
8.2 Activation Functions
8.3 Loss Functions
8.4 Optimization Algorithms
8.5 Backpropagation
8.6 Convolutional Neural Networks (CNNs)
8.7 Recurrent Neural Networks (RNNs)
8.8 Long Short-Term Memory (LSTM) Networks
8.9 Generative Adversarial Networks (GANs)
8.10 Transfer Learning
Lesson 9: Natural Language Processing (NLP)
9.1 Text Preprocessing Techniques
9.2 Tokenization and Lemmatization
9.3 Bag of Words (BoW) Model
9.4 TF-IDF Vectorization
9.5 Word Embeddings (Word2Vec, GloVe)
9.6 Sentiment Analysis
9.7 Text Classification
9.8 Named Entity Recognition (NER)
9.9 Topic Modeling
9.10 Advanced NLP Techniques
Lesson 10: Time Series Analysis
10.1 Introduction to Time Series Data
10.2 Stationarity and Differencing
10.3 Autocorrelation and Partial Autocorrelation
10.4 ARIMA Models
10.5 Seasonal Decomposition
10.6 Exponential Smoothing Methods
10.7 Forecasting Techniques
10.8 Time Series Anomaly Detection
10.9 Multivariate Time Series Analysis
10.10 Advanced Time Series Models
Lesson 11: Big Data Technologies
11.1 Introduction to Big Data
11.2 Hadoop Ecosystem
11.3 Apache Spark
11.4 Data Lakes and Data Warehouses
11.5 Distributed File Systems
11.6 MapReduce Programming
11.7 Real-Time Data Processing
11.8 Big Data Storage Solutions
11.9 Big Data Analytics Tools
11.10 Case Studies in Big Data
Lesson 12: Cloud Computing for Data Science
12.1 Introduction to Cloud Computing
12.2 AWS for Data Science
12.3 Google Cloud Platform (GCP) for Data Science
12.4 Microsoft Azure for Data Science
12.5 Cloud Storage Solutions
12.6 Cloud Computing Services
12.7 Scalable Data Processing
12.8 Cloud-Based Machine Learning
12.9 Cost Management in Cloud Computing
12.10 Security and Compliance in Cloud Computing
Lesson 13: Data Visualization Techniques
13.1 Principles of Effective Data Visualization
13.2 Advanced Matplotlib Techniques
13.3 Seaborn for Statistical Visualizations
13.4 Interactive Dashboards with Dash
13.5 Geospatial Data Visualization
13.6 Network Graph Visualization
13.7 Visualizing High-Dimensional Data
13.8 Storytelling with Data
13.9 Data Visualization Tools
13.10 Best Practices in Data Visualization
Lesson 14: Advanced Machine Learning Techniques
14.1 Model Ensembling Techniques
14.2 Stacking and Blending
14.3 Feature Selection Techniques
14.4 Regularization Techniques
14.5 Imbalanced Data Handling
14.6 Advanced Hyperparameter Tuning
14.7 Model Interpretability Techniques
14.8 Explainable AI (XAI)
14.9 AutoML Frameworks
14.10 Advanced Model Evaluation Metrics
Lesson 15: Reinforcement Learning
15.1 Introduction to Reinforcement Learning
15.2 Markov Decision Processes (MDPs)
15.3 Q-Learning
15.4 Deep Q-Networks (DQN)
15.5 Policy Gradient Methods
15.6 Actor-Critic Methods
15.7 Multi-Agent Reinforcement Learning
15.8 Reinforcement Learning Applications
15.9 Challenges in Reinforcement Learning
15.10 Advanced Reinforcement Learning Techniques
Lesson 16: Data Engineering
16.1 Data Pipeline Design
16.2 ETL Processes
16.3 Data Warehousing Solutions
16.4 Data Lake Architecture
16.5 Stream Processing with Apache Kafka
16.6 Batch Processing with Apache Spark
16.7 Data Governance and Quality
16.8 Data Lineage and Metadata Management
16.9 Data Engineering Tools
16.10 Best Practices in Data Engineering
Lesson 17: Advanced NLP Techniques
17.1 Transformer Architecture
17.2 BERT and Its Variants
17.3 Sequence-to-Sequence Models
17.4 Attention Mechanisms
17.5 Text Generation with RNNs
17.6 Sentiment Analysis with Deep Learning
17.7 Advanced Topic Modeling
17.8 Multilingual NLP
17.9 NLP in Conversational AI
17.10 Ethical Considerations in NLP
Lesson 18: Advanced Time Series Analysis
18.1 Prophet for Time Series Forecasting
18.2 LSTM for Time Series Prediction
18.3 ARIMA with Exogenous Variables
18.4 Change Point Detection
18.5 Seasonal-Trend Decomposition
18.6 Multivariate Time Series Models
18.7 Time Series Clustering
18.8 Anomaly Detection in Time Series
18.9 Time Series Feature Engineering
18.10 Advanced Forecasting Techniques
Lesson 19: MLOps and Model Deployment
19.1 Introduction to MLOps
19.2 Model Versioning and Tracking
19.3 Continuous Integration/Continuous Deployment (CI/CD)
19.4 Model Serving with Flask and FastAPI
19.5 Containerization with Docker
19.6 Orchestration with Kubernetes
19.7 Monitoring and Logging
19.8 A/B Testing and Canary Deployments
19.9 Scalable Model Deployment
19.10 Best Practices in MLOps
Lesson 20: Ethical AI and Fairness in Machine Learning
20.1 Bias in Machine Learning
20.2 Fairness Metrics
20.3 Bias Mitigation Techniques
20.4 Ethical Considerations in Data Collection
20.5 Privacy-Preserving Machine Learning
20.6 Differential Privacy
20.7 Explainable AI (XAI) for Fairness
20.8 Regulatory Compliance in AI
20.9 Case Studies in Ethical AI
20.10 Best Practices for Ethical AI
Lesson 21: Advanced Deep Learning Techniques
21.1 Transfer Learning with Pre-trained Models
21.2 Fine-Tuning Techniques
21.3 Advanced CNN Architectures
21.4 Advanced RNN Architectures
21.5 Attention Mechanisms in Deep Learning
21.6 Transformer Models for NLP
21.7 Generative Models (GANs, VAEs)
21.8 Reinforcement Learning with Deep Learning
21.9 Neural Architecture Search (NAS)
21.10 Advanced Optimization Techniques
Lesson 22: Graph Neural Networks (GNNs)
22.1 Introduction to Graph Neural Networks
22.2 Graph Convolutional Networks (GCNs)
22.3 Graph Attention Networks (GATs)
22.4 GraphSAGE
22.5 Node Classification with GNNs
22.6 Link Prediction with GNNs
22.7 Graph Embeddings
22.8 Applications of GNNs
22.9 Challenges in Graph Neural Networks
22.10 Advanced GNN Techniques
Lesson 23: Federated Learning
23.1 Introduction to Federated Learning
23.2 Federated Averaging Algorithm
23.3 Privacy and Security in Federated Learning
23.4 Federated Learning Architectures
23.5 Federated Learning with PySyft
23.6 Federated Learning with TensorFlow Federated
23.7 Challenges in Federated Learning
23.8 Applications of Federated Learning
23.9 Advanced Federated Learning Techniques
23.10 Future Directions in Federated Learning
Lesson 24: Advanced Data Visualization Techniques
24.1 Interactive Visualizations with Bokeh
24.2 Advanced Plotly Techniques
24.3 Geospatial Data Visualization with Folium
24.4 Network Graph Visualization with NetworkX
24.5 Visualizing High-Dimensional Data with t-SNE
24.6 Visualizing Clustering Results
24.7 Visualizing Time Series Data
24.8 Storytelling with Data Visualization
24.9 Data Visualization Tools and Libraries
24.10 Best Practices in Advanced Data Visualization
Lesson 25: Advanced Feature Engineering Techniques
25.1 Feature Selection Methods
25.2 Feature Extraction Techniques
25.3 Dimensionality Reduction Techniques
25.4 Feature Scaling and Normalization
25.5 Handling Categorical Data
25.6 Time-Based Feature Engineering
25.7 Domain-Specific Feature Engineering
25.8 Automated Feature Engineering
25.9 Feature Engineering for NLP
25.10 Feature Engineering for Time Series Data
Lesson 26: Advanced Ensemble Methods
26.1 Bagging Techniques
26.2 Boosting Techniques
26.3 Stacking Ensemble Methods
26.4 Blending Ensemble Methods
26.5 Voting Classifiers
26.6 Ensemble Methods for Regression
26.7 Ensemble Methods for Classification
26.8 Ensemble Methods for Anomaly Detection
26.9 Ensemble Methods for Time Series Forecasting
26.10 Advanced Ensemble Techniques
Lesson 27: Advanced Reinforcement Learning Techniques
27.1 Deep Reinforcement Learning
27.2 Policy Gradient Methods
27.3 Actor-Critic Methods
27.4 Multi-Agent Reinforcement Learning
27.5 Reinforcement Learning with Function Approximation
27.6 Reinforcement Learning with Neural Networks
27.7 Reinforcement Learning Applications
27.8 Challenges in Reinforcement Learning
27.9 Advanced Reinforcement Learning Algorithms
27.10 Future Directions in Reinforcement Learning
Lesson 28: Advanced Topics in NLP
28.1 Transformer Architecture for NLP
28.2 BERT and Its Variants
28.3 Sequence-to-Sequence Models
28.4 Attention Mechanisms in NLP
28.5 Text Generation with RNNs
28.6 Sentiment Analysis with Deep Learning
28.7 Advanced Topic Modeling
28.8 Multilingual NLP
28.9 NLP in Conversational AI
28.10 Ethical Considerations in NLP
Lesson 29: Advanced Topics in Time Series Analysis
29.1 Prophet for Time Series Forecasting
29.2 LSTM for Time Series Prediction
29.3 ARIMA with Exogenous Variables
29.4 Change Point Detection
29.5 Seasonal-Trend Decomposition
29.6 Multivariate Time Series Models
29.7 Time Series Clustering
29.8 Anomaly Detection in Time Series
29.9 Time Series Feature Engineering
29.10 Advanced Forecasting Techniques
Lesson 30: Advanced MLOps and Model Deployment
30.1 Introduction to MLOps
30.2 Model Versioning and Tracking
30.3 Continuous Integration/Continuous Deployment (CI/CD)
30.4 Model Serving with Flask and FastAPI
30.5 Containerization with Docker
30.6 Orchestration with Kubernetes
30.7 Monitoring and Logging
30.8 A/B Testing and Canary Deployments
30.9 Scalable Model Deployment
30.10 Best Practices in MLOps
Lesson 31: Advanced Ethical AI and Fairness in Machine Learning
31.1 Bias in Machine Learning
31.2 Fairness Metrics
31.3 Bias Mitigation Techniques
31.4 Ethical Considerations in Data Collection
31.5 Privacy-Preserving Machine Learning
31.6 Differential Privacy
31.7 Explainable AI (XAI) for Fairness
31.8 Regulatory Compliance in AI
31.9 Case Studies in Ethical AI
31.10 Best Practices for Ethical AI
Lesson 32: Advanced Deep Learning Techniques
32.1 Transfer Learning with Pre-trained Models
32.2 Fine-Tuning Techniques
32.3 Advanced CNN Architectures
32.4 Advanced RNN Architectures
32.5 Attention Mechanisms in Deep Learning
32.6 Transformer Models for NLP
32.7 Generative Models (GANs, VAEs)
32.8 Reinforcement Learning with Deep Learning
32.9 Neural Architecture Search (NAS)
32.10 Advanced Optimization Techniques
Lesson 33: Advanced Graph Neural Networks (GNNs)
33.1 Introduction to Graph Neural Networks
33.2 Graph Convolutional Networks (GCNs)
33.3 Graph Attention Networks (GATs)
33.4 GraphSAGE
33.5 Node Classification with GNNs
33.6 Link Prediction with GNNs
33.7 Graph Embeddings
33.8 Applications of GNNs
33.9 Challenges in Graph Neural Networks
33.10 Advanced GNN Techniques
Lesson 34: Advanced Federated Learning
34.1 Introduction to Federated Learning
34.2 Federated Averaging Algorithm
34.3 Privacy and Security in Federated Learning
34.4 Federated Learning Architectures
34.5 Federated Learning with PySyft
34.6 Federated Learning with TensorFlow Federated
34.7 Challenges in Federated Learning
34.8 Applications of Federated Learning
34.9 Advanced Federated Learning Techniques
34.10 Future Directions in Federated Learning
Lesson 35: Advanced Data Visualization Techniques
35.1 Interactive Visualizations with Bokeh
35.2 Advanced Plotly Techniques
35.3 Geospatial Data Visualization with Folium
35.4 Network Graph Visualization with NetworkX
35.5 Visualizing High-Dimensional Data with t-SNE
35.6 Visualizing Clustering Results
35.7 Visualizing Time Series Data
35.8 Storytelling with Data Visualization
35.9 Data Visualization Tools and Libraries
35.10 Best Practices in Advanced Data Visualization
Lesson 36: Advanced Feature Engineering Techniques
36.1 Feature Selection Methods
36.2 Feature Extraction Techniques
36.3 Dimensionality Reduction Techniques
36.4 Feature Scaling and Normalization
36.5 Handling Categorical Data
36.6 Time-Based Feature Engineering
36.7 Domain-Specific Feature Engineering
36.8 Automated Feature Engineering
36.9 Feature Engineering for NLP
36.10 Feature Engineering for Time Series Data
Lesson 37: Advanced Ensemble Methods
37.1 Bagging Techniques
37.2 Boosting Techniques
37.3 Stacking Ensemble Methods
37.4 Blending Ensemble Methods
37.5 Voting Classifiers
37.6 Ensemble Methods for Regression
37.7 Ensemble Methods for Classification
37.8 Ensemble Methods for Anomaly Detection
37.9 Ensemble Methods for Time Series Forecasting
37.10 Advanced Ensemble Techniques
Lesson 38: Advanced Reinforcement Learning Techniques
38.1 Deep Reinforcement Learning
38.2 Policy Gradient Methods
38.3 Actor-Critic Methods
38.4 Multi-Agent Reinforcement Learning
38.5 Reinforcement Learning with Function Approximation
38.6 Reinforcement Learning with Neural Networks
38.7 Reinforcement Learning Applications
38.8 Challenges in Reinforcement Learning
38.9 Advanced Reinforcement Learning Algorithms
38.10 Future Directions in Reinforcement Learning
Lesson 39: Advanced Topics in NLP
39.1 Transformer Architecture for NLP
39.2 BERT and Its Variants
39.3 Sequence-to-Sequence Models
39.4 Attention Mechanisms in NLP
39.5 Text Generation with RNNs
39.6 Sentiment Analysis with Deep Learning
39.7 Advanced Topic Modeling
39.8 Multilingual NLP
39.9 NLP in Conversational AI
39.10 Ethical Considerations in NLP
Lesson 40: Capstone Project and Certification
40.1 Capstone Project Overview
40.2 Project Planning and Design
40.3 Data Collection and Preprocessing
40.4 Exploratory Data Analysis
40.5 Model Selection and Training
40.6 Model Evaluation and Tuning
40.7 Deployment and Scalability
40.8 Project Documentation and Reporting
40.9 Presentation and Defense
40.10 Certification and Next Steps
1.1 Definition and Scope of Data Science
1.2 The Role of a Data Scientist
1.3 Key Skills Required for Data Science
1.4 Data Science Workflow
1.5 Tools and Technologies in Data Science
1.6 Real-World Applications of Data Science
1.7 Ethical Considerations in Data Science
1.8 Career Paths in Data Science
1.9 Industry Trends in Data Science
1.10 Getting Started with Data Science Projects
Lesson 2: Statistics for Data Science
2.1 Descriptive Statistics
2.2 Probability Theory
2.3 Inferential Statistics
2.4 Hypothesis Testing
2.5 Confidence Intervals
2.6 Correlation and Regression
2.7 Statistical Distributions
2.8 Central Limit Theorem
2.9 Bayesian Statistics
2.10 Statistical Modeling Techniques
Lesson 3: Data Wrangling and Preprocessing
3.1 Data Cleaning Techniques
3.2 Handling Missing Data
3.3 Data Transformation
3.4 Feature Engineering
3.5 Data Normalization and Standardization
3.6 Dealing with Outliers
3.7 Data Aggregation
3.8 Data Merging and Joining
3.9 Working with Time Series Data
3.10 Automating Data Preprocessing
Lesson 4: Exploratory Data Analysis (EDA)
4.1 Understanding Data Distribution
4.2 Visualizing Data with Matplotlib
4.3 Interactive Visualizations with Plotly
4.4 Correlation Analysis
4.5 Dimensionality Reduction Techniques
4.6 Clustering for EDA
4.7 Anomaly Detection
4.8 Feature Importance Analysis
4.9 EDA Reporting and Documentation
4.10 Advanced EDA Techniques
Lesson 5: Machine Learning Fundamentals
5.1 Supervised Learning
5.2 Unsupervised Learning
5.3 Reinforcement Learning
5.4 Model Selection and Evaluation
5.5 Overfitting and Underfitting
5.6 Bias-Variance Tradeoff
5.7 Cross-Validation Techniques
5.8 Hyperparameter Tuning
5.9 Model Interpretability
5.10 Ethical Considerations in Machine Learning
Lesson 6: Supervised Learning Algorithms
6.1 Linear Regression
6.2 Logistic Regression
6.3 Decision Trees
6.4 Random Forests
6.5 Support Vector Machines (SVM)
6.6 K-Nearest Neighbors (KNN)
6.7 Naive Bayes Classifier
6.8 Gradient Boosting Machines
6.9 Neural Networks Basics
6.10 Ensemble Methods
Lesson 7: Unsupervised Learning Algorithms
7.1 K-Means Clustering
7.2 Hierarchical Clustering
7.3 DBSCAN
7.4 Principal Component Analysis (PCA)
7.5 t-SNE for Visualization
7.6 Association Rule Learning
7.7 Anomaly Detection Techniques
7.8 Gaussian Mixture Models
7.9 Self-Organizing Maps (SOM)
7.10 Advanced Clustering Techniques
Lesson 8: Deep Learning Fundamentals
8.1 Introduction to Neural Networks
8.2 Activation Functions
8.3 Loss Functions
8.4 Optimization Algorithms
8.5 Backpropagation
8.6 Convolutional Neural Networks (CNNs)
8.7 Recurrent Neural Networks (RNNs)
8.8 Long Short-Term Memory (LSTM) Networks
8.9 Generative Adversarial Networks (GANs)
8.10 Transfer Learning
Lesson 9: Natural Language Processing (NLP)
9.1 Text Preprocessing Techniques
9.2 Tokenization and Lemmatization
9.3 Bag of Words (BoW) Model
9.4 TF-IDF Vectorization
9.5 Word Embeddings (Word2Vec, GloVe)
9.6 Sentiment Analysis
9.7 Text Classification
9.8 Named Entity Recognition (NER)
9.9 Topic Modeling
9.10 Advanced NLP Techniques
Lesson 10: Time Series Analysis
10.1 Introduction to Time Series Data
10.2 Stationarity and Differencing
10.3 Autocorrelation and Partial Autocorrelation
10.4 ARIMA Models
10.5 Seasonal Decomposition
10.6 Exponential Smoothing Methods
10.7 Forecasting Techniques
10.8 Time Series Anomaly Detection
10.9 Multivariate Time Series Analysis
10.10 Advanced Time Series Models
Lesson 11: Big Data Technologies
11.1 Introduction to Big Data
11.2 Hadoop Ecosystem
11.3 Apache Spark
11.4 Data Lakes and Data Warehouses
11.5 Distributed File Systems
11.6 MapReduce Programming
11.7 Real-Time Data Processing
11.8 Big Data Storage Solutions
11.9 Big Data Analytics Tools
11.10 Case Studies in Big Data
Lesson 12: Cloud Computing for Data Science
12.1 Introduction to Cloud Computing
12.2 AWS for Data Science
12.3 Google Cloud Platform (GCP) for Data Science
12.4 Microsoft Azure for Data Science
12.5 Cloud Storage Solutions
12.6 Cloud Computing Services
12.7 Scalable Data Processing
12.8 Cloud-Based Machine Learning
12.9 Cost Management in Cloud Computing
12.10 Security and Compliance in Cloud Computing
Lesson 13: Data Visualization Techniques
13.1 Principles of Effective Data Visualization
13.2 Advanced Matplotlib Techniques
13.3 Seaborn for Statistical Visualizations
13.4 Interactive Dashboards with Dash
13.5 Geospatial Data Visualization
13.6 Network Graph Visualization
13.7 Visualizing High-Dimensional Data
13.8 Storytelling with Data
13.9 Data Visualization Tools
13.10 Best Practices in Data Visualization
Lesson 14: Advanced Machine Learning Techniques
14.1 Model Ensembling Techniques
14.2 Stacking and Blending
14.3 Feature Selection Techniques
14.4 Regularization Techniques
14.5 Imbalanced Data Handling
14.6 Advanced Hyperparameter Tuning
14.7 Model Interpretability Techniques
14.8 Explainable AI (XAI)
14.9 AutoML Frameworks
14.10 Advanced Model Evaluation Metrics
Lesson 15: Reinforcement Learning
15.1 Introduction to Reinforcement Learning
15.2 Markov Decision Processes (MDPs)
15.3 Q-Learning
15.4 Deep Q-Networks (DQN)
15.5 Policy Gradient Methods
15.6 Actor-Critic Methods
15.7 Multi-Agent Reinforcement Learning
15.8 Reinforcement Learning Applications
15.9 Challenges in Reinforcement Learning
15.10 Advanced Reinforcement Learning Techniques
Lesson 16: Data Engineering
16.1 Data Pipeline Design
16.2 ETL Processes
16.3 Data Warehousing Solutions
16.4 Data Lake Architecture
16.5 Stream Processing with Apache Kafka
16.6 Batch Processing with Apache Spark
16.7 Data Governance and Quality
16.8 Data Lineage and Metadata Management
16.9 Data Engineering Tools
16.10 Best Practices in Data Engineering
Lesson 17: Advanced NLP Techniques
17.1 Transformer Architecture
17.2 BERT and Its Variants
17.3 Sequence-to-Sequence Models
17.4 Attention Mechanisms
17.5 Text Generation with RNNs
17.6 Sentiment Analysis with Deep Learning
17.7 Advanced Topic Modeling
17.8 Multilingual NLP
17.9 NLP in Conversational AI
17.10 Ethical Considerations in NLP
Lesson 18: Advanced Time Series Analysis
18.1 Prophet for Time Series Forecasting
18.2 LSTM for Time Series Prediction
18.3 ARIMA with Exogenous Variables
18.4 Change Point Detection
18.5 Seasonal-Trend Decomposition
18.6 Multivariate Time Series Models
18.7 Time Series Clustering
18.8 Anomaly Detection in Time Series
18.9 Time Series Feature Engineering
18.10 Advanced Forecasting Techniques
Lesson 19: MLOps and Model Deployment
19.1 Introduction to MLOps
19.2 Model Versioning and Tracking
19.3 Continuous Integration/Continuous Deployment (CI/CD)
19.4 Model Serving with Flask and FastAPI
19.5 Containerization with Docker
19.6 Orchestration with Kubernetes
19.7 Monitoring and Logging
19.8 A/B Testing and Canary Deployments
19.9 Scalable Model Deployment
19.10 Best Practices in MLOps
Lesson 20: Ethical AI and Fairness in Machine Learning
20.1 Bias in Machine Learning
20.2 Fairness Metrics
20.3 Bias Mitigation Techniques
20.4 Ethical Considerations in Data Collection
20.5 Privacy-Preserving Machine Learning
20.6 Differential Privacy
20.7 Explainable AI (XAI) for Fairness
20.8 Regulatory Compliance in AI
20.9 Case Studies in Ethical AI
20.10 Best Practices for Ethical AI
Lesson 21: Advanced Deep Learning Techniques
21.1 Transfer Learning with Pre-trained Models
21.2 Fine-Tuning Techniques
21.3 Advanced CNN Architectures
21.4 Advanced RNN Architectures
21.5 Attention Mechanisms in Deep Learning
21.6 Transformer Models for NLP
21.7 Generative Models (GANs, VAEs)
21.8 Reinforcement Learning with Deep Learning
21.9 Neural Architecture Search (NAS)
21.10 Advanced Optimization Techniques
Lesson 22: Graph Neural Networks (GNNs)
22.1 Introduction to Graph Neural Networks
22.2 Graph Convolutional Networks (GCNs)
22.3 Graph Attention Networks (GATs)
22.4 GraphSAGE
22.5 Node Classification with GNNs
22.6 Link Prediction with GNNs
22.7 Graph Embeddings
22.8 Applications of GNNs
22.9 Challenges in Graph Neural Networks
22.10 Advanced GNN Techniques
Lesson 23: Federated Learning
23.1 Introduction to Federated Learning
23.2 Federated Averaging Algorithm
23.3 Privacy and Security in Federated Learning
23.4 Federated Learning Architectures
23.5 Federated Learning with PySyft
23.6 Federated Learning with TensorFlow Federated
23.7 Challenges in Federated Learning
23.8 Applications of Federated Learning
23.9 Advanced Federated Learning Techniques
23.10 Future Directions in Federated Learning
Lesson 24: Advanced Data Visualization Techniques
24.1 Interactive Visualizations with Bokeh
24.2 Advanced Plotly Techniques
24.3 Geospatial Data Visualization with Folium
24.4 Network Graph Visualization with NetworkX
24.5 Visualizing High-Dimensional Data with t-SNE
24.6 Visualizing Clustering Results
24.7 Visualizing Time Series Data
24.8 Storytelling with Data Visualization
24.9 Data Visualization Tools and Libraries
24.10 Best Practices in Advanced Data Visualization
Lesson 25: Advanced Feature Engineering Techniques
25.1 Feature Selection Methods
25.2 Feature Extraction Techniques
25.3 Dimensionality Reduction Techniques
25.4 Feature Scaling and Normalization
25.5 Handling Categorical Data
25.6 Time-Based Feature Engineering
25.7 Domain-Specific Feature Engineering
25.8 Automated Feature Engineering
25.9 Feature Engineering for NLP
25.10 Feature Engineering for Time Series Data
Lesson 26: Advanced Ensemble Methods
26.1 Bagging Techniques
26.2 Boosting Techniques
26.3 Stacking Ensemble Methods
26.4 Blending Ensemble Methods
26.5 Voting Classifiers
26.6 Ensemble Methods for Regression
26.7 Ensemble Methods for Classification
26.8 Ensemble Methods for Anomaly Detection
26.9 Ensemble Methods for Time Series Forecasting
26.10 Advanced Ensemble Techniques
Lesson 27: Advanced Reinforcement Learning Techniques
27.1 Deep Reinforcement Learning
27.2 Policy Gradient Methods
27.3 Actor-Critic Methods
27.4 Multi-Agent Reinforcement Learning
27.5 Reinforcement Learning with Function Approximation
27.6 Reinforcement Learning with Neural Networks
27.7 Reinforcement Learning Applications
27.8 Challenges in Reinforcement Learning
27.9 Advanced Reinforcement Learning Algorithms
27.10 Future Directions in Reinforcement Learning
Lesson 28: Advanced Topics in NLP
28.1 Transformer Architecture for NLP
28.2 BERT and Its Variants
28.3 Sequence-to-Sequence Models
28.4 Attention Mechanisms in NLP
28.5 Text Generation with RNNs
28.6 Sentiment Analysis with Deep Learning
28.7 Advanced Topic Modeling
28.8 Multilingual NLP
28.9 NLP in Conversational AI
28.10 Ethical Considerations in NLP
Lesson 29: Advanced Topics in Time Series Analysis
29.1 Prophet for Time Series Forecasting
29.2 LSTM for Time Series Prediction
29.3 ARIMA with Exogenous Variables
29.4 Change Point Detection
29.5 Seasonal-Trend Decomposition
29.6 Multivariate Time Series Models
29.7 Time Series Clustering
29.8 Anomaly Detection in Time Series
29.9 Time Series Feature Engineering
29.10 Advanced Forecasting Techniques
Lesson 30: Advanced MLOps and Model Deployment
30.1 Introduction to MLOps
30.2 Model Versioning and Tracking
30.3 Continuous Integration/Continuous Deployment (CI/CD)
30.4 Model Serving with Flask and FastAPI
30.5 Containerization with Docker
30.6 Orchestration with Kubernetes
30.7 Monitoring and Logging
30.8 A/B Testing and Canary Deployments
30.9 Scalable Model Deployment
30.10 Best Practices in MLOps
Lesson 31: Advanced Ethical AI and Fairness in Machine Learning
31.1 Bias in Machine Learning
31.2 Fairness Metrics
31.3 Bias Mitigation Techniques
31.4 Ethical Considerations in Data Collection
31.5 Privacy-Preserving Machine Learning
31.6 Differential Privacy
31.7 Explainable AI (XAI) for Fairness
31.8 Regulatory Compliance in AI
31.9 Case Studies in Ethical AI
31.10 Best Practices for Ethical AI
Lesson 32: Advanced Deep Learning Techniques
32.1 Transfer Learning with Pre-trained Models
32.2 Fine-Tuning Techniques
32.3 Advanced CNN Architectures
32.4 Advanced RNN Architectures
32.5 Attention Mechanisms in Deep Learning
32.6 Transformer Models for NLP
32.7 Generative Models (GANs, VAEs)
32.8 Reinforcement Learning with Deep Learning
32.9 Neural Architecture Search (NAS)
32.10 Advanced Optimization Techniques
Lesson 33: Advanced Graph Neural Networks (GNNs)
33.1 Introduction to Graph Neural Networks
33.2 Graph Convolutional Networks (GCNs)
33.3 Graph Attention Networks (GATs)
33.4 GraphSAGE
33.5 Node Classification with GNNs
33.6 Link Prediction with GNNs
33.7 Graph Embeddings
33.8 Applications of GNNs
33.9 Challenges in Graph Neural Networks
33.10 Advanced GNN Techniques
Lesson 34: Advanced Federated Learning
34.1 Introduction to Federated Learning
34.2 Federated Averaging Algorithm
34.3 Privacy and Security in Federated Learning
34.4 Federated Learning Architectures
34.5 Federated Learning with PySyft
34.6 Federated Learning with TensorFlow Federated
34.7 Challenges in Federated Learning
34.8 Applications of Federated Learning
34.9 Advanced Federated Learning Techniques
34.10 Future Directions in Federated Learning
Lesson 35: Advanced Data Visualization Techniques
35.1 Interactive Visualizations with Bokeh
35.2 Advanced Plotly Techniques
35.3 Geospatial Data Visualization with Folium
35.4 Network Graph Visualization with NetworkX
35.5 Visualizing High-Dimensional Data with t-SNE
35.6 Visualizing Clustering Results
35.7 Visualizing Time Series Data
35.8 Storytelling with Data Visualization
35.9 Data Visualization Tools and Libraries
35.10 Best Practices in Advanced Data Visualization
Lesson 36: Advanced Feature Engineering Techniques
36.1 Feature Selection Methods
36.2 Feature Extraction Techniques
36.3 Dimensionality Reduction Techniques
36.4 Feature Scaling and Normalization
36.5 Handling Categorical Data
36.6 Time-Based Feature Engineering
36.7 Domain-Specific Feature Engineering
36.8 Automated Feature Engineering
36.9 Feature Engineering for NLP
36.10 Feature Engineering for Time Series Data
Lesson 37: Advanced Ensemble Methods
37.1 Bagging Techniques
37.2 Boosting Techniques
37.3 Stacking Ensemble Methods
37.4 Blending Ensemble Methods
37.5 Voting Classifiers
37.6 Ensemble Methods for Regression
37.7 Ensemble Methods for Classification
37.8 Ensemble Methods for Anomaly Detection
37.9 Ensemble Methods for Time Series Forecasting
37.10 Advanced Ensemble Techniques
Lesson 38: Advanced Reinforcement Learning Techniques
38.1 Deep Reinforcement Learning
38.2 Policy Gradient Methods
38.3 Actor-Critic Methods
38.4 Multi-Agent Reinforcement Learning
38.5 Reinforcement Learning with Function Approximation
38.6 Reinforcement Learning with Neural Networks
38.7 Reinforcement Learning Applications
38.8 Challenges in Reinforcement Learning
38.9 Advanced Reinforcement Learning Algorithms
38.10 Future Directions in Reinforcement Learning
Lesson 39: Advanced Topics in NLP
39.1 Transformer Architecture for NLP
39.2 BERT and Its Variants
39.3 Sequence-to-Sequence Models
39.4 Attention Mechanisms in NLP
39.5 Text Generation with RNNs
39.6 Sentiment Analysis with Deep Learning
39.7 Advanced Topic Modeling
39.8 Multilingual NLP
39.9 NLP in Conversational AI
39.10 Ethical Considerations in NLP
Lesson 40: Capstone Project and Certification
40.1 Capstone Project Overview
40.2 Project Planning and Design
40.3 Data Collection and Preprocessing
40.4 Exploratory Data Analysis
40.5 Model Selection and Training
40.6 Model Evaluation and Tuning
40.7 Deployment and Scalability
40.8 Project Documentation and Reporting
40.9 Presentation and Defense
40.10 Certification and Next Steps
Quality Thoughts
Jul 21, 2025, 6:48:35 AM7/21/25
to Masterytrail
Quality Thought – The Best Data Science Training Course Institute in Hyderabad with Live Intensive Internship Program
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