Artificial Intelligence, Machine Learning, and Deep Learning

• What Is Intelligence?
• Testing Machine Intelligence
• The General Problem Solver
• Strong and Weak Artificial Intelligence
• Artificial Intelligence Planning
• Learning over Memorizing
• Practical Applications of Machine Learning
• Artificial Neural Networks
• The Fall and Rise of the Perceptron
• Big Data Arrives
• Expert System Versus Machine Learning
• Supervised Versus Unsupervised Learning
• Backpropagation of Errors
• Regression Analysis
• Intelligent Robots
• Natural Language Processing
• The Internet of Things
• 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
• How a Machine Learns
• Working with Data
• Applying Machine Learning
• Different Types of Learning

3 LiveLab in this lesson — see the labs panel →

• Supervised Machine Learning
• Unsupervised Machine Learning
• Semi-Supervised Machine Learning
• Reinforcement Learning
• Decision Trees
• k-Nearest Neighbor
• k-Means Clustering
• Regression Analysis
• Näive Bayes
• Fitting the Model to Your Data
• Choosing Algorithms
• Ensemble Modeling
• Deciding on a Machine Learning Approach
• Start Asking Questions
• Don’t Mix Training Data with Test Data
• Don’t Overstate a Model’s Accuracy
• Know Your Algorithms

4 LiveLab in this lesson — see the labs panel →

• Why the Brain Analogy?
• Just Another Amazing Algorithm
• Getting to Know the Perceptron
• Squeezing Down a Sigmoid Neuron
• Feeding Data into the Network
• What Goes on in the Hidden Layers
• Understanding Activation Functions
• Adding Weights
• Adding Bias

1 LiveLab in this lesson — see the labs panel →

• 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
• Solving Classification Problems
• Solving Clustering Problems
• 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

1 LiveLab in this lesson — see the labs panel →

• Extracting Meaning from Text and Speech with NLU
• Delivering Sensible Responses with NLG
• Automating Customer Service
• Reviewing the Top NLP Tools and Resources
• Choosing Natural Language Technologies
• Review the Top Tools for Creating Chatbots and Virtual Agents
• Choosing Between Automated and Intuitive Decision-Making
• Gathering Data in Real Time from IoT Devices
• Reviewing Automated Decision-Making Tools