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CBT Nuggets

Introduction to Machine Learning Online Training

This Introduction to Machine Learning training covers how to turn vast datasets into actionable insights, predictions, and trends. Learn to build models with scikit-learn, PyTorch, TensorFlow, and explore LLM development with OpenAI, LangChain, and HuggingFace. Perfect for junior data scientists, IT teams, or as a comprehensive reference for machine learning and AI best practices.

Updated February 2024

59Skills
395Videos
49h 4mTotal
395 videos49h 4m

Who This Course Is For

The introduction to machine learning training is presented as associate-level data science training, which means it was designed for junior data scientists and aspiring machine learning engineers. This machine learning skills course offers significant value to both emerging IT professionals with at least a year of experience and seasoned data scientists looking to validate their data science skills in an ever advancing field.

Course Curriculum

  • Explore How AI Agents Navigate Driving DirectionsFree1h 3m
  • Premium skill.Apply Probability to Real-World AI Problems44m
  • Premium skill.Define What is Machine Learning?52m
  • Premium skill.Setup a Machine Learning Development Environment44m
  • Premium skill.Explore Data Pipelines and Linear Regression51m
  • Premium skill.Apply Regression Concepts for Supervised Learning51m
  • Premium skill.Examine Cost Functions and Parameter Tuning46m
  • Premium skill.Implement Gradient Descent for Linear Regression45m
  • Premium skill.Vectorize Operations for Multiple Regression44m
  • Premium skill.Explore Feature Engineering and Data Preparation52m
  • Premium skill.Identify Key Classification Algorithms46m
  • Premium skill.Implement Logistic Regression with Python50m
  • Premium skill.Build a Python Decision Tree Classification Model51m
  • Premium skill.Build a Python Random Forest Classification Model44m
  • Premium skill.Apply Regularization to Overcome Overfitting45m
  • Premium skill.Build a Support Vector Machine Classifier46m
  • Premium skill.Build a K-Nearest Neighbors Classifier57m
  • Premium skill.Explore Neural Network Basics With The Perceptron51m
  • Premium skill.Implement a Perceptron for Classification45m
  • Premium skill.Explore PyTorch Fundamentals for Machine Learning50m
  • Premium skill.Leverage PyTorch Tensor Attributes and Operators46m
  • Premium skill.Explore Fundamental PyTorch Tensor Operations46m
  • Premium skill.Apply PyTorch Tensor Manipulation and Indexing53m
  • Premium skill.Explore Gradient Descent & Back Propagation53m
  • Premium skill.Predict Ice Cream Sales with PyTorch Regression45m
  • Premium skill.Implement a Logistic Regression Model with PyTorch45m
  • Premium skill.Explore Neural Network Classification with PyTorch56m
  • Premium skill.Build a PyTorch Classifier with Non-Linearity1h 8m
  • Premium skill.Explore Multi-class Classification with PyTorch1h 3m
  • Premium skill.Tune Hyperparameters and Analyze Fit with PyTorch51m
  • Premium skill.Discover What's New with PyTorch 2.044m
  • Premium skill.Explore TensorFlow Machine Learning Foundations46m
  • Premium skill.Explore TensorFlow Aggregation and Manipulation45m
  • Premium skill.Implement Matrix Multiplication with TensorFlow47m
  • Premium skill.Reshape, Transpose, and Alter TensorFlow Tensors48m
  • Premium skill.Squeeze, Encode, and Optimize TensorFlow Tensors45m
  • Premium skill.Explore Neural Network Regression with TensorFlow50m
  • Premium skill.Build a Simple Regression Model with TensorFlow47m
  • Premium skill.Evaluate Regression Models with TensorFlow46m
  • Premium skill.Visualize and Evaluate Performance with TensorFlow54m
  • Premium skill.Normalize and Feature Scale Data with TensorFlow48m
  • Premium skill.Explore TensorFlow Neural Network Classification56m
  • Premium skill.Build a Neural Network Classifier with TensorFlow47m
  • Premium skill.Build a TensorFlow Classifier with Non-Linearity52m
  • Premium skill.Evaluate TensorFlow Classification Models51m
  • Premium skill.Explore Multi-Class Classification with TensorFlow47m
  • Premium skill.Tune Multi-Class Classification TensorFlow Models49m
  • Premium skill.Explore Multi-Label Classification with TensorFlow57m
  • Premium skill.Explore The Fundamentals of Large Language Models48m
  • Premium skill.Build LLM Apps with ChatGPT and the OpenAI API56m
  • Premium skill.Design Effective Prompts for Large Language Models56m
  • Premium skill.Implement LangChain in Language Model Workflows44m
  • Premium skill.Implement LangChain Memory for Autonomous Tasks53m
  • Premium skill.Combine LangChain Components for Coherent Apps54m
  • Premium skill.Build Task-Driven Autonomous Agents with LangChain49m
  • Premium skill.Use LangChain to Interact with PDFs and Documents50m
  • Premium skill.Use LangChain to Chat with PDFs and Documents48m
  • Premium skill.Explore Transformer Encoders and Decoders48m
  • Premium skill.Examine the Fundamentals of HuggingFace58m

For IT leaders

What IT leaders need to know before assigning this course

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.

Team Impact

How this training helps your team succeed

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.

After completion

Knowledge & ability your team will gain

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.

This course is included with every subscription

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