The Exploring AI & Machine Learning for the Enterprise Overview is an introductory-level, lecture, demonstration and group discussion style course that explores the foundations of AI and how AI can be practically exploited in the modern business sense. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice.
This course introduces AI from a practical applied business perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will explore:
Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional AI, machine learning, data science, programming, Python/R and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.
This course introduces AI from a practical applied business perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will explore:
Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional AI, machine learning, data science, programming, Python/R and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.
This course is ideally suited for a wide variety of technical learners just getting started with AI or machine Learning, seeking a primer-level overview of these technologies, skills and related tools. Attendees might include:
Students attending this class should have a grounding in Enterprise computing. While there’s no particular class to offer as a prerequisite, students attending this course should be familiar with Enterprise IT, have a general (high-level) understanding of systems architecture, as well as some knowledge of the business drivers that might be able to take advantage of applying AI. Please see the Related Courses tab for recommended course pre-requisites, alternative suggested courses and follow on events.
Session: The AI Landscape
Lesson: Overview: Data Science to Deep Learning
Data Science versus Machine Learning
Types of AI
Machine Learning
Deep Learning
Lesson: Diving into Machine Learning
Importance of Data
Supervised Learning Explained
Classification
Regression
Unsupervised Training
Clustering
Dimensionality Reduction
Lesson: Real-World Use Cases for ML and AI
Retail
Financial
Healthcare
Manufacturing
Self-Driving Cars
Lesson: Real-World Expectations for ML and DL
Challenging Machine Learning
Simplicity and Data
Structured vs. Unstructured Data
Challenging Deep Learning
Today’s Limit on Image Recognition
Today’s Limit in Natural Language Processing
Data, Data, and More Data
Session: Machine Learning Projects
Lesson: Step 1: Plan
ML Project Workflow
Selecting a Business Need
Data Science vs Machine Learning
ML Planning Specifics
ML Considerations
Lesson: Step 2: Data Management
Acquiring and Analyzing Data
Pre-Processing Data
Data Transformations
Unstructured to Structured
Dimensionality Reduction
Lesson: Step 3: Feature Selection and Engineering
Steps May Iterate and Change Order
Defining Feature Selection
Examples to Consider
Raw Data to Algorithm Inputs
Lesson: Step 3: Feature Selection and Engineering
Data Drivers for Selection
Structured and Unstructured
Supervised and Unsupervised
Goal Drivers for Selection
Classification and Regression
Clustering and Dimensionality Reduction
ML Algorithms
Lesson: Step 4: Model Training and Validation
Importance of Getting to Supervised Training
Training and Testing Datasets
Training the Model
Testing the Model
Lesson: Step 5: Implementation to Production and Monitoring
Consuming the Model Results
Considerations for Deploying the Model
Monitoring to Make Further Improvements
Session: Setting the Stage
Lesson: Current Tools of the Trade
Python and Its Libraries
ML Libraries Including SciKit-learn
DL Libraries Including TensorFlow and Keras
Hardware From GPUs to TPUs
Open Source Datasets
Resources to Experiment With
Lesson: Deep Learning: A Primer
Neural Networks (NN) for Handling Tougher Problems
Basic NN for Demand Prediction
DL and Unstructured Data
Advances in Image Processing
Convoluted NN
Advances in NLP
Recurrent NN
Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable).
Live scheduled classes are listed below or browse our full course catalog anytime
Check out custom training solutions planned around your unique needs and skills.
Exclusive materials, ongoing support and a free live course refresh with every class.
Please see the current upcoming available open enrollment course dates posted below. Please feel free to Register Online below, or call 844-475-4559 toll free to connect with our Registrar for assistance. If you need additional date options, please contact us for scheduling.
Course Title | Days | Date | Time | Price | |
---|---|---|---|---|---|
Exploring AI / Artificial Intelligence & Machine Learning Overview | 1 Day | Jul 20 | 10:00 AM to 06:00 PM EST | $895.00 | Enroll |
Exploring AI / Artificial Intelligence & Machine Learning Overview | 1 Day | Aug 24 | 10:00 AM to 06:00 PM EST | $895.00 | Enroll |
Exploring AI / Artificial Intelligence & Machine Learning Overview | 1 Day | Sep 23 | 10:00 AM to 06:00 PM EST | $895.00 | Enroll |
Exploring AI / Artificial Intelligence & Machine Learning Overview | 1 Day | Oct 26 | 10:00 AM to 06:00 PM EST | $895.00 | Enroll |
Exploring AI / Artificial Intelligence & Machine Learning Overview | 1 Day | Nov 30 | 10:00 AM to 06:00 PM EST | $895.00 | Enroll |
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