uCertify

Artificial Intelligence, Machine Learning, and Deep Learning

(UOP-DSC520.AP1)
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1

Thinking Machines: An Overview of Artificial Intelligence

  • 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
2

Machine Learning and Algorithms

  • 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
3

AI Neural Networks in Action

  • 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
4

Letting Networks Learn, Classify and Cluster

  • 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
5

Putting Artificial Intelligence to Work

  • 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
6

Building Artificial Minds and Using ML to Predict 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
  • Separating Intelligence from Automation
  • Adding Layers for Deep Learning
  • Considering Applications for Artificial Neural Networks
  • Reviewing the Top Deep Learning Tools

1

Thinking Machines: An Overview of Artificial Intelligence

  • Analyzing the Artificial Intelligence, Machine Learning, and Deep Learning
  • Understanding Concepts Used to Automate Decision-Making Processes
  • Understanding Approaches Used to Automate Computer Decision-Making Processes
2

Machine Learning and Algorithms

  • Performing the K-Means Clustering
  • Analyzing Algorithms to Parse and Analyze Data
  • Identifying Algorithms to Parse and Analyze Data
  • Summarizing Algorithms to Parse and Analyze Data
3

AI Neural Networks in Action

  • Using Step Functions
4

Letting Networks Learn, Classify and Cluster

  • Summarizing Methods Used to Automate Computer Decision-Making Processes
5

Building Artificial Minds and Using ML to Predict Outcomes

  • Using Amazon SageMaker
  • Performing PCA in SageMaker
  • Creating Machine Learning Model using AWS SageMaker

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