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Balancing Bias and Variance for Peak Model Accuracy

Duration: 4m 49s

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1. Course Kickoff & Orientation
1m 39s

1. Getting Started with AI - Core Essentials
2m 15s

2. AI vs. Generative AI - Key Distinctions
1m 37s

3. The Rising Importance of AI Skills
2m 55s

4. AI Evolution - From Origins to Today
7m 15s

5. The AI Spectrum - Understanding Its Forms
4m 58s

6. AI at Work - Practical Applications
8m 30s

1. Perceptron Fundamentals
5m 1s

2. AI Reality Check – Dispelling Common Misconceptions
4m 56s

3. Strategic Overview of AI Domains
1m 53s

4. Data Foundations – Mastering Exploratory Data Analysis
7m 36s

5. AI Project Framework
5m 45s

1. Supervised Learning Foundations
2m 2s

2. Unsupervised Learning Essentials
1m 47s

3. Reinforcement Learning Fundamentals
20m

4. Regression and Classification Explained
1m 17s

5. Shaping Data for Smarter Models
20m

6. Cross-Validation Techniques
2m 22s

7. Hyperparameter Optimization
1m

8. Balancing Bias and Variance for Peak Model Accuracy
4m 49s

1. Introduction to Deep Learning
2m 31s

2. Core Concepts of Neural Networks
1m 31s

3. Convolutional Neural Networks Explained
1m 36s

4. Recurrent Networks and Transformer Models
1m 30s

5. Building and Training Neural Networks
1m 55s

1. Understanding the Confusion Matrix
2m 32s

2. Key Performance Metrics
3m 8s

3. Advanced Model Evaluation Metrics
6m 38s

4. Practical Exercise with Teachable Machines
5m 10s

1. AI Applications in Healthcare during COVID-19
6m 2s

2. AI Transforming Sports Analytics
3m 5s

3. AI in Cybersecurity and Threat Detection
7m 7s

4. Challenges in Real-World AI Implementation
11m 19s

1. Foundations of AI Ethics
6m 47s

2. Introduction to Prompt Engineering and LLMs
10m 46s

1. Course Summary and Next Steps
1m 22s

1. Project – Build & Deploy Your Own AI Solution
10m 2s

2. Professional Competency Quiz
20m