Lesson 1: Introduction to AI and IBM AI Solutions
1.1 Overview of AI and its Applications
1.2 IBM’s Role in AI Development
1.3 Key Components of IBM AI Solutions
1.4 IBM Watson Overview
1.5 Use Cases of IBM AI in Industries
1.6 Ethical Considerations in AI
1.7 AI vs. Machine Learning vs. Deep Learning
1.8 IBM AI Tools and Platforms
1.9 Getting Started with IBM Cloud
1.10 Hands-on: Setting Up IBM Cloud Account
Lesson 2: Fundamentals of Machine Learning
2.1 Introduction to Machine Learning
2.2 Supervised Learning
2.3 Unsupervised Learning
2.4 Reinforcement Learning
2.5 Key Algorithms in Machine Learning
2.6 Feature Engineering
2.7 Model Evaluation Metrics
2.8 Overfitting and Underfitting
2.9 Bias-Variance Tradeoff
2.10 Hands-on: Implementing a Simple ML Model
Lesson 3: Deep Learning Basics
3.1 Introduction to Deep Learning
3.2 Neural Networks Architecture
3.3 Activation Functions
3.4 Loss Functions
3.5 Optimization Algorithms
3.6 Convolutional Neural Networks (CNNs)
3.7 Recurrent Neural Networks (RNNs)
3.8 Long Short-Term Memory (LSTM) Networks
3.9 Transfer Learning
3.10 Hands-on: Building a Basic Neural Network
Lesson 4: Natural Language Processing (NLP) with IBM Watson
4.1 Introduction to NLP
4.2 Text Preprocessing Techniques
4.3 Tokenization and Lemmatization
4.4 Sentiment Analysis
4.5 Named Entity Recognition (NER)
4.6 IBM Watson Natural Language Understanding
4.7 IBM Watson Language Translator
4.8 Building Chatbots with Watson Assistant
4.9 Advanced NLP Techniques
4.10 Hands-on: Creating an NLP Pipeline with Watson
Lesson 5: Computer Vision and Image Processing
5.1 Introduction to Computer Vision
5.2 Image Preprocessing Techniques
5.3 Feature Extraction
5.4 Object Detection and Recognition
5.5 Image Segmentation
5.6 IBM Watson Visual Recognition
5.7 Transfer Learning in Computer Vision
5.8 Advanced Computer Vision Techniques
5.9 Real-world Applications of Computer Vision
5.10 Hands-on: Building an Image Classifier
Lesson 6: Time Series Analysis and Forecasting
6.1 Introduction to Time Series Data
6.2 Stationarity and Differencing
6.3 Autoregressive Integrated Moving Average (ARIMA)
6.4 Seasonal Decomposition
6.5 Long Short-Term Memory (LSTM) for Time Series
6.6 IBM Watson Studio for Time Series Analysis
6.7 Feature Engineering for Time Series
6.8 Model Evaluation for Time Series
6.9 Advanced Time Series Techniques
6.10 Hands-on: Forecasting with ARIMA and LSTM
Lesson 7: Recommender Systems
7.1 Introduction to Recommender Systems
7.2 Collaborative Filtering
7.3 Content-Based Filtering
7.4 Hybrid Recommender Systems
7.5 Matrix Factorization Techniques
7.6 Evaluation Metrics for Recommender Systems
7.7 IBM Watson Personalization
7.8 Building Recommender Systems with IBM Tools
7.9 Real-world Applications of Recommender Systems
7.10 Hands-on: Implementing a Recommender System
Lesson 8: Anomaly Detection
8.1 Introduction to Anomaly Detection
8.2 Types of Anomalies
8.3 Statistical Methods for Anomaly Detection
8.4 Machine Learning Approaches for Anomaly Detection
8.5 IBM Watson Anomaly Detection
8.6 Feature Engineering for Anomaly Detection
8.7 Evaluation Metrics for Anomaly Detection
8.8 Advanced Anomaly Detection Techniques
8.9 Real-world Applications of Anomaly Detection
8.10 Hands-on: Building an Anomaly Detection Model
Lesson 9: Reinforcement Learning
9.1 Introduction to Reinforcement Learning
9.2 Markov Decision Processes (MDPs)
9.3 Q-Learning
9.4 Deep Q-Networks (DQN)
9.5 Policy Gradient Methods
9.6 Actor-Critic Methods
9.7 IBM Watson Reinforcement Learning
9.8 Real-world Applications of Reinforcement Learning
9.9 Advanced Reinforcement Learning Techniques
9.10 Hands-on: Implementing a Reinforcement Learning Agent
Lesson 10: AutoML and IBM AutoAI
10.1 Introduction to AutoML
10.2 Benefits of AutoML
10.3 IBM AutoAI Overview
10.4 Automated Feature Engineering
10.5 Automated Model Selection
10.6 Hyperparameter Tuning
10.7 Model Interpretability with AutoAI
10.8 Deploying Models with AutoAI
10.9 Real-world Applications of AutoAI
10.10 Hands-on: Building a Model with IBM AutoAI
Lesson 11: Data Engineering and Pipelines
11.1 Introduction to Data Engineering
11.2 Data Ingestion Techniques
11.3 Data Cleaning and Preprocessing
11.4 Data Transformation
11.5 Data Storage Solutions
11.6 IBM Watson Data Platform
11.7 Building Data Pipelines
11.8 Automating Data Pipelines
11.9 Monitoring and Maintaining Data Pipelines
11.10 Hands-on: Creating a Data Pipeline with IBM Tools
Lesson 12: Model Deployment and Scalability
12.1 Introduction to Model Deployment
12.2 Containerization with Docker
12.3 Orchestration with Kubernetes
12.4 IBM Cloud Functions
12.5 IBM Watson Machine Learning
12.6 Scaling ML Models
12.7 Monitoring Deployed Models
12.8 Updating and Retraining Models
12.9 Real-world Deployment Scenarios
12.10 Hands-on: Deploying a Model on IBM Cloud
Lesson 13: Ethical AI and Bias Mitigation
13.1 Introduction to Ethical AI
13.2 Bias in AI Systems
13.3 Fairness in Machine Learning
13.4 Transparency and Explainability
13.5 IBM AI Fairness 360
13.6 Bias Mitigation Techniques
13.7 Ethical Considerations in Data Collection
13.8 Ethical Considerations in Model Deployment
13.9 Real-world Ethical AI Challenges
13.10 Hands-on: Bias Mitigation with AI Fairness 360
Lesson 14: Advanced Topics in NLP
14.1 Transformer Architectures
14.2 BERT and Its Variants
14.3 Transfer Learning in NLP
14.4 Zero-shot and Few-shot Learning
14.5 IBM Watson Discovery
14.6 Advanced Text Generation Techniques
14.7 Multimodal Learning
14.8 Real-world NLP Applications
14.9 Ethical Considerations in NLP
14.10 Hands-on: Fine-tuning a Transformer Model
Lesson 15: Advanced Topics in Computer Vision
15.1 Generative Adversarial Networks (GANs)
15.2 Object Tracking and Detection
15.3 Semantic Segmentation
15.4 3D Computer Vision
15.5 IBM Watson Visual Insights
15.6 Advanced Image Generation Techniques
15.7 Real-world Computer Vision Applications
15.8 Ethical Considerations in Computer Vision
15.9 Hands-on: Building a GAN Model
Lesson 16: Advanced Topics in Time Series Analysis
16.1 Prophet for Time Series Forecasting
16.2 Multivariate Time Series Analysis
16.3 Anomaly Detection in Time Series
16.4 Change Point Detection
16.5 IBM Watson Time Series Forecasting
16.6 Real-world Time Series Applications
16.7 Ethical Considerations in Time Series Analysis
16.8 Hands-on: Forecasting with Prophet
Lesson 17: Advanced Topics in Recommender Systems
17.1 Collaborative Filtering with Matrix Factorization
17.2 Deep Learning for Recommender Systems
17.3 Contextual Bandits for Recommendations
17.4 IBM Watson Recommendations
17.5 Real-world Recommender System Applications
17.6 Ethical Considerations in Recommender Systems
17.7 Hands-on: Building a Deep Learning Recommender System
Lesson 18: Advanced Topics in Anomaly Detection
18.1 Isolation Forests
18.2 One-Class SVM
18.3 Autoencoders for Anomaly Detection
18.4 IBM Watson Anomaly Detection
18.5 Real-world Anomaly Detection Applications
18.6 Ethical Considerations in Anomaly Detection
18.7 Hands-on: Building an Autoencoder for Anomaly Detection
Lesson 19: Advanced Topics in Reinforcement Learning
19.1 Multi-Agent Reinforcement Learning
19.2 Hierarchical Reinforcement Learning
19.3 Inverse Reinforcement Learning
19.4 IBM Watson Reinforcement Learning
19.5 Real-world Reinforcement Learning Applications
19.6 Ethical Considerations in Reinforcement Learning
19.7 Hands-on: Implementing a Multi-Agent RL System
Lesson 20: Advanced Topics in AutoML
20.1 Ensemble Learning in AutoML
20.2 Meta-Learning for AutoML
20.3 IBM AutoAI Advanced Features
20.4 Real-world AutoML Applications
20.5 Ethical Considerations in AutoML
20.6 Hands-on: Building an Ensemble Model with AutoAI
Lesson 21: Advanced Data Engineering Techniques
21.1 Data Lakes and Data Warehouses
21.2 Stream Processing with Apache Kafka
21.3 IBM Watson Data Stage
21.4 Real-world Data Engineering Applications
21.5 Ethical Considerations in Data Engineering
21.6 Hands-on: Building a Stream Processing Pipeline
Lesson 22: Advanced Model Deployment Techniques
22.1 Serverless Architectures for ML
22.2 Edge Computing for ML
22.3 IBM Watson Edge Application Manager
22.4 Real-world Model Deployment Scenarios
22.5 Ethical Considerations in Model Deployment
22.6 Hands-on: Deploying a Model on Edge Devices
Lesson 23: AI in Healthcare
23.1 Overview of AI in Healthcare
23.2 Medical Image Analysis
23.3 Predictive Analytics in Healthcare
23.4 IBM Watson for Health
23.5 Real-world Healthcare AI Applications
23.6 Ethical Considerations in Healthcare AI
23.7 Hands-on: Building a Medical Image Classifier
Lesson 24: AI in Finance
24.1 Overview of AI in Finance
24.2 Fraud Detection
24.3 Algorithmic Trading
24.4 IBM Watson for Financial Services
24.5 Real-world Finance AI Applications
24.6 Ethical Considerations in Finance AI
24.7 Hands-on: Building a Fraud Detection Model
Lesson 25: AI in Retail
25.1 Overview of AI in Retail
25.2 Inventory Management
25.3 Customer Segmentation
25.4 IBM Watson for Retail
25.5 Real-world Retail AI Applications
25.6 Ethical Considerations in Retail AI
25.7 Hands-on: Building a Customer Segmentation Model
Lesson 26: AI in Manufacturing
26.1 Overview of AI in Manufacturing
26.2 Predictive Maintenance
26.3 Quality Control
26.4 IBM Watson for Manufacturing
26.5 Real-world Manufacturing AI Applications
26.6 Ethical Considerations in Manufacturing AI
26.7 Hands-on: Building a Predictive Maintenance Model
Lesson 27: AI in Transportation
27.1 Overview of AI in Transportation
27.2 Autonomous Vehicles
27.3 Traffic Prediction
27.4 IBM Watson for Transportation
27.5 Real-world Transportation AI Applications
27.6 Ethical Considerations in Transportation AI
27.7 Hands-on: Building a Traffic Prediction Model
Lesson 28: AI in Energy
28.1 Overview of AI in Energy
28.2 Energy Consumption Forecasting
28.3 Renewable Energy Integration
28.4 IBM Watson for Energy
28.5 Real-world Energy AI Applications
28.6 Ethical Considerations in Energy AI
28.7 Hands-on: Building an Energy Forecasting Model
Lesson 29: AI in Agriculture
29.1 Overview of AI in Agriculture
29.2 Crop Yield Prediction
29.3 Disease Detection in Plants
29.4 IBM Watson for Agriculture
29.5 Real-world Agriculture AI Applications
29.6 Ethical Considerations in Agriculture AI
29.7 Hands-on: Building a Crop Yield Prediction Model
Lesson 30: AI in Education
30.1 Overview of AI in Education
30.2 Personalized Learning
30.3 Student Performance Prediction
30.4 IBM Watson for Education
30.5 Real-world Education AI Applications
30.6 Ethical Considerations in Education AI
30.7 Hands-on: Building a Student Performance Prediction Model
Lesson 31: AI in Customer Service
31.1 Overview of AI in Customer Service
31.2 Chatbots and Virtual Assistants
31.3 Sentiment Analysis in Customer Feedback
31.4 IBM Watson for Customer Service
31.5 Real-world Customer Service AI Applications
31.6 Ethical Considerations in Customer Service AI
31.7 Hands-on: Building a Customer Service Chatbot
Lesson 32: AI in Human Resources
32.1 Overview of AI in Human Resources
32.2 Candidate Screening
32.3 Employee Performance Prediction
32.4 IBM Watson for Human Resources
32.5 Real-world HR AI Applications
32.6 Ethical Considerations in HR AI
32.7 Hands-on: Building an Employee Performance Prediction Model
Lesson 33: AI in Marketing
33.1 Overview of AI in Marketing
33.2 Customer Segmentation
33.3 Predictive Analytics in Marketing
33.4 IBM Watson for Marketing
33.5 Real-world Marketing AI Applications
33.6 Ethical Considerations in Marketing AI
33.7 Hands-on: Building a Marketing Campaign Optimization Model
Lesson 34: AI in Cybersecurity
34.1 Overview of AI in Cybersecurity
34.2 Intrusion Detection Systems
34.3 Malware Detection
34.4 IBM Watson for Cybersecurity
34.5 Real-world Cybersecurity AI Applications
34.6 Ethical Considerations in Cybersecurity AI
34.7 Hands-on: Building an Intrusion Detection Model
Lesson 35: AI in Supply Chain Management
35.1 Overview of AI in Supply Chain Management
35.2 Demand Forecasting
35.3 Inventory Optimization
35.4 IBM Watson for Supply Chain
35.5 Real-world Supply Chain AI Applications
35.6 Ethical Considerations in Supply Chain AI
35.7 Hands-on: Building a Demand Forecasting Model
Lesson 36: AI in Environmental Monitoring
36.1 Overview of AI in Environmental Monitoring
36.2 Air Quality Prediction
36.3 Wildlife Conservation
36.4 IBM Watson for Environmental Monitoring
36.5 Real-world Environmental Monitoring AI Applications
36.6 Ethical Considerations in Environmental Monitoring AI
36.7 Hands-on: Building an Air Quality Prediction Model
Lesson 37: AI in Robotics
37.1 Overview of AI in Robotics
37.2 Robotic Process Automation (RPA)
37.3 Autonomous Robots
37.4 IBM Watson for Robotics
37.5 Real-world Robotics AI Applications
37.6 Ethical Considerations in Robotics AI
37.7 Hands-on: Building an Autonomous Robot Controller
Lesson 38: AI in Entertainment
38.1 Overview of AI in Entertainment
38.2 Content Recommendation Systems
38.3 Virtual Reality and AI
38.4 IBM Watson for Entertainment
38.5 Real-world Entertainment AI Applications
38.6 Ethical Considerations in Entertainment AI
38.7 Hands-on: Building a Content Recommendation System
Lesson 39: AI in Smart Cities
39.1 Overview of AI in Smart Cities
39.2 Traffic Management
39.3 Waste Management
39.4 IBM Watson for Smart Cities
39.5 Real-world Smart City AI Applications
39.6 Ethical Considerations in Smart City AI
39.7 Hands-on: Building a Smart City Traffic Management System
Lesson 40: Future Trends in AI
40.1 Emerging AI Technologies
40.2 Quantum Computing and AI
40.3 Explainable AI (XAI)
40.4 Federated Learning
40.5 IBM’s Vision for the Future of AI
40.6 Ethical Considerations in Future AI
40.7 Real-world Future AI Applications
40.8 Preparing for Future AI Trends
40.9 Hands-on: Exploring an Emerging AI Technology
40.10 Capstone Project: Integrating Multiple AI Technologies
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