Introduction to Machine Learning Online Training

AI and machine learning initiatives often stall when teams can run tools but lack a shared foundation for model selection, evaluation, and tuning. This associate-level course gives IT Directors and Training Managers a structured path for junior data scientists, aspiring machine learning engineers, and IT Practitioners moving into AI-supported workflows. The visible curriculum alone represents more than 32 hours of training, with 19 additional skills listed, so plan this as a multi-week enablement track rather than a one-off assignment. It is best used to standardize fundamentals across a cohort before teams take on production-facing ML work. Risk reduction comes from topics such as overfitting, regularization, cost functions, gradient descent, and model evaluation. CBT Nuggets Playlists can sequence the learning path, and Team Reporting can help Team Leads monitor progress; the provided course data does not indicate course-specific Virtual Labs or Practice Exams.

IT teams complete this training to move beyond AI terminology and build practical fluency with model-driven problem solving. The curriculum connects core ML concepts to scenarios such as AI agents navigating directions, probability-based decisions, regression forecasting, and classification models built with Python, PyTorch, and TensorFlow.

• Build a common team vocabulary around AI, machine learning, supervised learning, regression, classification, and neural networks.
• Reduce model-quality risk by training staff to recognize overfitting and apply regularization, parameter tuning, and performance evaluation.
• Support internal analytics work by preparing practitioners to build regression models, including a PyTorch sales-prediction example.
• Improve framework readiness by exposing teams to PyTorch tensor operations, TensorFlow tensor workflows, search algorithms, and neural network optimization.

Knowledge

• Core distinctions between AI, machine learning, regression, classification, and neural network approaches.
• How probability, data pipelines, and feature preparation support machine learning workflows.
• How cost functions, gradient descent, back propagation, and hyperparameter tuning influence model performance.
• When to consider classification approaches such as logistic regression, decision trees, random forests, support vector machines, and K-nearest neighbors.
• Foundational PyTorch and TensorFlow concepts, including tensors, tensor manipulation, matrix multiplication, and model evaluation.

Ability

• Set up a machine learning development environment for introductory ML work.
• Build and evaluate linear and logistic regression models with Python-based tooling.
• Implement classification models, including decision tree, random forest, SVM, KNN, perceptron, and neural network classifiers.
• Use PyTorch for tensor operations, regression, logistic regression, non-linear classifiers, and multi-class classification.
• Use TensorFlow to manipulate tensors, build simple regression models, and visualize/evaluate model performance.