Introduction to Artificial Intelligence

About Course

This Introduction to Artificial Intelligence (AI) is designed to help learners decode the mystery of artificial intelligence and its business applications. This AI for beginners course provides an overview of AI concepts and workflows, machine learning, deep learning, and performance metrics.

This Introduction to AI course provides an overview of AI concepts and workflows, machine learning, deep learning, and performance metrics. You’ll learn the difference between supervised, unsupervised, and reinforcement learning; be exposed to use cases, and see how clustering and classification algorithms help identify AI business applications.

Course Content

1. Decoding Artificial Intelligence

1.1 Decoding Artificial Intelligence

00:20
1.2 Meaning, Scope, and Stages Of Artificial Intelligence

00:47
1.3 Three Stages of Artificial Intelligence

02:20
1.4 Applications of Artificial Intelligence

01:06
1.5 Image Recognition

01:27
1.6 Applications of Artificial Intelligence – Examples

01:07
1.7 Effects of Artificial Intelligence on Society

03:55
1.8 Supervises Learning for Telemedicine

03:11
1.9 Solves Complex Social Problems

03:18
1.10 Benefits Multiple Industries

02:39
1.11 Key Takeaways

01:08

2. Fundamentals Of Machine Learning and Deep Learning

2.1 Fundamentals Of Machine Learning and Deep Learning

00:00
2.2 Meaning of Machine Learning

00:00
2.3 Relationship between Machine Learning and Statistical Analysis

00:00
2.4 Process of Machine Learning

00:00
2.5 Types of Machine Learning

00:00
2.6 Meaning of Unsupervised Learning

00:00
2.7 Meaning of Semi-supervised Learning

00:00
2.8 Algorithms of Machine Learning

00:00
2.9 Regression

00:00
2.10 Naive Bayes

00:00
2.11 Naive Bayes Classification

00:00
2.12 Machine Learning Algorithms

00:00
2.13 Deep Learning

00:00
2.14 Artificial Neural Network Definition

00:00
2.15 Definition of Perceptron

00:00
2.16 Online and Batch Learning

00:00
2.17 Key Takeaways

00:00

3. Machine Learning Workflow

3.1 Learning Objective

00:00
3.2 Machine Learning Workflow

00:00
3.3 Get more data

00:00
3.4 Ask a Sharp Question

00:00
3.5 Add Data to the Table

00:00
3.6 Check for Quality

00:00
3.7 Transform Features

00:00
3.8 Answer the Questions

00:00
3.9 Use the Answer

00:00
3.10 Key takeaways

00:00

4. Performance Metrics

4.1 Performance Metrics

00:00
4.2 Need For Performance Metrics

00:00
4.3 Key Methods Of Performance Metrics

00:00
4.4 Confusion Matrix Example

00:00
4.5 Terms Of Confusion Matrix

00:00
4.6 Minimize False Cases

00:00
4.7 Minimize False Positive Example

00:00
4.8 Accuracy

00:00
4.9 Precision

00:00
4.10 Recall Or Sensitivity

00:00
4.11 Specificity

00:00
4.12 F1 Score

00:00
4.13 Key takeaways

00:00

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About Course

What Will You Learn?

Course Content

1. Decoding Artificial Intelligence

1.1 Decoding Artificial Intelligence

1.2 Meaning, Scope, and Stages Of Artificial Intelligence

1.3 Three Stages of Artificial Intelligence

1.4 Applications of Artificial Intelligence

1.5 Image Recognition

1.6 Applications of Artificial Intelligence – Examples

1.7 Effects of Artificial Intelligence on Society

1.8 Supervises Learning for Telemedicine

1.9 Solves Complex Social Problems

1.10 Benefits Multiple Industries

1.11 Key Takeaways

2. Fundamentals Of Machine Learning and Deep Learning

2.1 Fundamentals Of Machine Learning and Deep Learning

2.2 Meaning of Machine Learning

2.3 Relationship between Machine Learning and Statistical Analysis

2.4 Process of Machine Learning

2.5 Types of Machine Learning

2.6 Meaning of Unsupervised Learning

2.7 Meaning of Semi-supervised Learning

2.8 Algorithms of Machine Learning

2.9 Regression

2.10 Naive Bayes

2.11 Naive Bayes Classification

2.12 Machine Learning Algorithms

2.13 Deep Learning

2.14 Artificial Neural Network Definition

2.15 Definition of Perceptron

2.16 Online and Batch Learning

2.17 Key Takeaways

3. Machine Learning Workflow

3.1 Learning Objective

3.2 Machine Learning Workflow

3.3 Get more data

3.4 Ask a Sharp Question

3.5 Add Data to the Table

3.6 Check for Quality

3.7 Transform Features

3.8 Answer the Questions

3.9 Use the Answer

3.10 Key takeaways

4. Performance Metrics

4.1 Performance Metrics

4.2 Need For Performance Metrics

4.3 Key Methods Of Performance Metrics

4.4 Confusion Matrix Example

4.5 Terms Of Confusion Matrix

4.6 Minimize False Cases

4.7 Minimize False Positive Example

4.8 Accuracy

4.9 Precision

4.10 Recall Or Sensitivity

4.11 Specificity

4.12 F1 Score

4.13 Key takeaways

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