Our Artificial Intelligence (AI) for Business course offers an in-depth exploration of AI and its practical applications in modern enterprises. You’ll develop an understanding of machine learning (ML), neural networks, and natural language processing (NLP). Key topics include understanding different AI approaches (supervised, unsupervised, and reinforcement learning), building and implementing ML algorithms (decision trees, k-nearest neighbors, regression analysis), and applying AI to real-world challenges such as data analysis, customer service automation, and predictive analytics.
1: Preface
- About This eBook
- Foreword
2: What Is Artificial Intelligence?
- What Is Intelligence?
- Testing Machine Intelligence
- The General Problem Solver
- Strong and Weak Artificial Intelligence
- Artificial Intelligence Planning
- Learning over Memorizing
- Lesson Takeaways
3: The Rise of Machine Learning
- Practical Applications of Machine Learning
- Artificial Neural Networks
- The Fall and Rise of the Perceptron
- Big Data Arrives
- Lesson Takeaways
4: Zeroing in on the Best Approach
- Expert System Versus Machine Learning
- Supervised Versus Unsupervised Learning
- Backpropagation of Errors
- Regression Analysis
- Lesson Takeaways
5: Common AI Applications
- Intelligent Robots
- Natural Language Processing
- The Internet of Things
- Lesson Takeaways
6: Putting AI to Work on Big Data
- Understanding the Concept of Big Data
- Teaming Up with a Data Scientist
- Machine Learning and Data Mining: What’s the Difference?
- Making the Leap from Data Mining to Machine Learning
- Taking the Right Approach
- Lesson Takeaways
7: Weighing Your Options
- Lesson Takeaways
8: What Is Machine Learning?
- How a Machine Learns
- Working with Data
- Applying Machine Learning
- Different Types of Learning
- Lesson Takeaways
9: Different Ways a Machine Learns
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi-Supervised Machine Learning
- Reinforcement Learning
- Lesson Takeaways
10: Popular Machine Learning Algorithms
- Decision Trees
- k-Nearest Neighbor
- k-Means Clustering
- Regression Analysis
- Näive Bayes
- Lesson Takeaways
11: Applying Machine Learning Algorithms
- Fitting the Model to Your Data
- Choosing Algorithms
- Ensemble Modeling
- Deciding on a Machine Learning Approach
- Lesson Takeaways
12: Words of Advice
- Start Asking Questions
- Don’t Mix Training Data with Test Data
- Don’t Overstate a Model’s Accuracy
- Know Your Algorithms
- Lesson Takeaways
13: What Are Artificial Neural Networks?
- Why the Brain Analogy?
- Just Another Amazing Algorithm
- Getting to Know the Perceptron
- Squeezing Down a Sigmoid Neuron
- Adding Bias
- Lesson Takeaways
14: Artificial Neural Networks in Action
- Feeding Data into the Network
- What Goes on in the Hidden Layers
- Understanding Activation Functions
- Adding Weights
- Adding Bias
- Lesson Takeaways
15: Letting Your Network Learn
- Starting with Random Weights and Biases
- Making Your Network Pay for Its Mistakes: The Cost Function
- Combining the Cost Function with Gradient Descent
- Using Backpropagation to Correct for Errors
- Tuning Your Network
- Employing the Chain Rule
- Batching the Data Set with Stochastic Gradient Descent
- Lesson Takeaways
16: Using Neural Networks to Classify or Cluster
- Solving Classification Problems
- Solving Clustering Problems
- Lesson Takeaways
17: Key Challenges
- Obtaining Enough Quality Data
- Keeping Training and Test Data Separate
- Carefully Choosing Your Training Data
- Taking an Exploratory Approach
- Choosing the Right Tool for the Job
- Lesson Takeaways
18: Harnessing the Power of Natural Language Processing
- Extracting Meaning from Text and Speech with NLU
- Delivering Sensible Responses with NLG
- Automating Customer Service
- Reviewing the Top NLP Tools and Resources
- Lesson Takeaways
19: Automating Customer Interactions
- Choosing Natural Language Technologies
- Review the Top Tools for Creating Chatbots and Virtual Agents
- Lesson Takeaways
20: Improving Data-Based Decision-Making
- Choosing Between Automated and Intuitive Decision-Making
- Gathering Data in Real Time from IoT Devices
- Reviewing Automated Decision-Making Tools
- Lesson Takeaways
21: Using Machine Learning to Predict Events and Outcomes
- Machine Learning Is Really about Labeling Data
- Looking at What Machine Learning Can Do
- Use Your Power for Good, Not Evil: Machine Learning Ethics
- Review the Top Machine Learning Tools
- Lesson Takeaways
22: Building Artificial Minds
- Separating Intelligence from Automation
- Adding Layers for Deep Learning
- Considering Applications for Artificial Neural Networks
- Reviewing the Top Deep Learning Tools
- Lesson Takeaways
Hands-on LAB Activities
(Performance Labs)
1: The Rise of Machine Learning
- Analyzing the Artificial Intelligence, Machine Learning, and Deep Learning
- Analyzing the Similarities and Differences Betwe...telligence, Machine Learning, and Deep Learning.
2: Putting AI to Work on Big Data
- Understanding Concepts Used to Automate Decision-Making Processes
3: Weighing Your Options
- Understanding Approaches Used to Automate Computer Decision-Making Processes
4: Popular Machine Learning Algorithms
- Analyzing Algorithms to Parse and Analyze Data
- Identifying Algorithms to Parse and Analyze Data
- Summarizing Algorithms to Parse and Analyze Data
5: Using Neural Networks to Classify or Cluster
- Summarizing Methods Used to Automate Computer Decision-Making Processes
- Understand and implement various ML algorithms
- Prepare data for analysis, including cleaning, normalization, and feature engineering
- Assess the performance of ML models using appropriate metrics
- Optimize model parameters to improve accuracy and efficiency
- Design different types of neural network architectures
- Apply backpropagation to train neural networks
- Use activation functions like ReLU, sigmoid, and tanh to introduce non-linearity
- Employ best practices to avoid overfitting, such as dropout and L1/L2 regularization
- Create numerical representations of text data using bag-of-words and TF-IDF
- Identify positive, negative, and neutral sentiments expressed in the text
- Extract entities like names, organizations, and locations from raw data
- Apply statistical methods and discover patterns and trends in large datasets
- Develop programming skills in Python and libraries like TensorFlow, PyTorch, Scikit-learn, and NLTK
This course provides students with qualified support professionals to guide them through their learning experience. The student support team is available to answer any questions a learner may have including questions on course content, course material, certifications, and registration or enrollment questions. Support advisers also monitor the progress of learners to ensure training retention and program advancement.