Foundations of Predictive AI

Build production-ready predictive AI across supervised learning, unsupervised learning, and deep neural networks. Predictive AI fundamentals cover problem framing, data preparation, feature...

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$2,495USD
Duration 3 days
Course Code DS-2204
Available Formats Classroom

Overview

Course Description

Build production-ready predictive AI across supervised learning, unsupervised learning, and deep neural networks. Predictive AI fundamentals cover problem framing, data preparation, feature engineering, and the supervised-versus-unsupervised typology. Supervised learning covers classification with train/test split, regression with linear models and Ridge and Lasso regularization, and ensembling across bagging, boosting, voting, random forests, and gradient-boosting trees. Unsupervised learning covers clustering, Gaussian mixtures, dimensionality reduction, and model selection across cross-validation, hyperparameter tuning, and validation curves. Neural networks and deep learning cover biological versus artificial neurons, the XOR problem, non-linear activation, loss functions, back-propagation, batch training, dense layers, GPUs, and pre-trained networks. Hands-on labs produce trained classifiers, regressors, an ensemble model, a neural network, and a deep-learning baseline on real-world datasets. The course is designed for data scientists, analysts, and developers with Python and DS-1201 experience preparing for advanced NLP, computer vision, and time-series forecasting tracks.

Skills Gained

By the end of this course, participants will be able to:

  • Apply the core principles of predictive AI to drive innovation
  • Build predictive models that evaluate cleanly on production-style problems
  • Apply Python libraries (scikit-learn, TensorFlow) to streamline model development
  • Implement neural networks and deep learning for complex problems
  • Apply best practices for model deployment and monitoring

Who Can Benefit

This course is designed for:

  • Data Scientists & Analysts
  • Software Developers

Prerequisites

Participants should enter this course with:

  • Practical Python Experience
  • DS-1201 or equivalent

Organizational Objectives

This course assists organizations to:

  • Reduce model-development cost through reusable scikit-learn and deep-learning patterns
  • Lower production risk through deliberate cross-validation and tuning practices
  • Build a working predictive-AI capability across data-science teams
  • Establish ensembling and deep-learning patterns that compound across projects

Software

All attendees must have a modern web browser and an Internet connection.

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Course Details

Course Details

Module 1 - Basics of Predictive AI

By the end of this module, you will be able to define machine learning, AI, predictive AI, and generative AI, walk the ML workflow end-to-end, prepare data, train and evaluate models, and recognize the challenges of machine learning in production.

  • Defining machine learning, AI, predictive AI, and generative AI
  • Overview of neural networks
  • The machine learning workflow
  • Data collection, exploration, and preprocessing
  • Model training, evaluation, and selection
  • Model deployment, monitoring, and challenges
  • Hands-on Lab: Walk through a complete machine-learning workflow on a sample dataset, deciding when AI is the right tool.

Module 2 - Preparing Real-World Data for Use

By the end of this module, you will be able to load, clean, and transform real-world data — handling missing values and duplicates, converting types, normalizing, handling outliers, engineering features, encoding categorical data, and integrating and exporting datasets.

  • Data collection and loading
  • Handling missing data, duplicates, and data type conversion
  • Standardization, normalization, and outlier handling
  • Feature engineering and encoding categorical data
  • Data integration and merging
  • Data exporting and storage
  • Hands-on Lab: Handle missing data, clean a dataset, and apply normalization and feature engineering.

Module 3 - Supervised Learning: Classification

By the end of this module, you will be able to train classifiers with scikit-learn, use ground-truth labels appropriately, evaluate classifier performance, recognize generalizability and overfitting, and separate training from test data deliberately.

  • Introduction to classifiers and ground-truth labels
  • Training a classifier with scikit-learn
  • Evaluating the performance of a classifier
  • Generalizability and overfitting
  • Separating training and test data
  • Limiting tests performed on test data
  • Hands-on Lab: Train a classifier on real-world data and apply a stratified split to validate generalizability.

Module 4 - Supervised Learning: Regression

By the end of this module, you will be able to apply linear and neural network regressors, train regressors with scikit-learn, and apply regularization techniques (Ridge, Lasso) to prevent overfitting.

  • Introduction to regression prediction
  • Linear models
  • Neural network regressors
  • Training a regressor with scikit-learn
  • Regularization techniques for linear models
  • Ridge and Lasso regression
  • Hands-on Lab: Train a linear regressor on a practical dataset and apply Ridge and Lasso to prevent overfitting.

Module 5 - Supervised Learning: Ensembling Techniques

By the end of this module, you will be able to combine multiple models through bagging and boosting, apply voting and averaging, use random forests, and apply gradient-boosting trees.

  • Combining Multiple Models
  • Bagging and Boosting
  • Voting and Averaging
  • Random Forests
  • Gradient-Boosting Trees
  • Hands-on Lab: Use random forests to classify tabular data and run XGBoost on a real-world dataset.

Module 6 - Unsupervised Learning

By the end of this module, you will be able to learn from unlabeled data, apply clustering techniques, work with Gaussian mixtures, and apply dimensionality reduction for prediction and visualization.

  • Learning from Unlabeled Data
  • Clustering Techniques and Algorithms
  • Gaussian Mixtures
  • Dimensionality Reduction
  • Reduction for Prediction vs. Visualization
  • Hands-on Lab: Apply K-Means to unlabeled data and use dimensionality reduction for visualization.

Module 7 - Model Selection & Evaluation

By the end of this module, you will be able to use cross-validation to evaluate model performance, apply scikit-learn for cross-validation, tune hyperparameters via grid and random search, and read validation and learning curves.

  • Cross-Validation to Evaluate Model Performance
  • Using Scikit-Learn to Run Cross-Validation
  • Hyper-Parameter Tuning
  • Grid vs Random Search
  • Validation and Learning Curves
  • Hands-on Lab: Compare cross-validation against train-test split and run hyperparameter tuning via random search.

Module 8 - Foundations of Neural Networks

By the end of this module, you will be able to articulate biological versus artificial neurons, recognize the XOR problem, build inner and primitive neural-network layers, apply non-linear activation functions, and use loss functions and gradient-boosting.

  • Neurons in Brain vs. Transistors in Chips
  • XOR Problem (Non-Linear Functions)
  • Inner Layer (Primitive Neural Network)
  • Non-Linear Activation Function
  • Universal Function Approximator
  • Loss Functions and Gradient-Boosting
  • Hands-on Lab: Train a neural network with scikit-learn and compare loss functions on the same task.

Module 9 - Basics of Deep Learning

By the end of this module, you will be able to apply back-propagation, batch and mini-batch training, dense layers, GPU-aware linear algebra, and layers specific to image and sequence data — and tune deep-learning hyperparameters.

  • Back-Propagation and Activation Functions
  • Batch and Mini-Batch Training
  • Dense Layers
  • GPUs and Linear Algebra
  • Layers for Specific Data Types
  • Deep Learning Hyper-Parameters
  • Hands-on Lab: Train a deep neural network with TensorFlow and use a pre-trained convolutional model on a downstream task.

Schedule

Upcoming Course Dates

There are currently no scheduled dates for this course. If you are interested in this course, contact us with the form below.

Contact Information

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does Ascendient Learning deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

What does vendor-authorized training mean?

As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

Is the training too basic, or will you go deep into technology?

It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

How up-to-date are your courses and support materials?

We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

Are your instructors seasoned trainers who have deep knowledge of the training topic?

Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

Do you provide hands-on training and exercises in an actual lab environment?

Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

Will you customize the training for our company’s specific needs and goals?

We will work with you to identify training needs and areas of growth.  We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.

How do I get started with certification?

Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.

Will I get access to content after I complete a course?

You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.

How do I request a W9 for Ascendient Learning?

View our filing status and how to request a W9.

Reviews

Thank Tech Data for sponsoring this course you really take care of your partners.

vary good online learning. instructor is vary good the way he explained every thing.

Provided good amount of material and a great instructor to teach the material.

I was very pleased with the course setup by ExitCertified and the instructor.

Course was great and the instructor had an answer for anything that was asked during the course.