Description
Introduction to Machine Learning and AI Engineering is a course on the concepts, tools, and workflows used to build modern artificial intelligence systems, published by CBTNuggets Online Academy. Students will learn how machines can analyze data, identify patterns, make predictions, and support intelligent decision-making through machine learning techniques. This course introduces core concepts of AI and machine learning, including data preparation, model training, evaluation, feature engineering, and practical implementation using industry-standard tools and frameworks. You will understand how models learn, build machine learning models, and master rigorous evaluation and pipeline design. The course covers machine learning fundamentals such as regression and tensors, then moves on to real-world LLM applications, focusing on illusion reduction and retrieval-based applications.
Individuals will also explore the fundamentals of AI engineering, including model deployment, automation, scalability, and real-world application development. You will integrate latency awareness to ensure testable, data-driven outputs, and learn how modern AI is scaled to production using tools like PyTorch, TensorFlow, and LangChain. By the end, you will have a foundation in AI workflow thinking and be equipped with skills like machine learning principles, RAG, evaluation, and AI security to consistently build reliable applications.
What you will learn in Introduction to Machine Learning and AI Engineering:
- Design effective guidelines and workflows for LLM-based applications
- Create portfolio projects that demonstrate familiarity with machine learning models
- Engineer features and evaluate model performance using learning curves
- Build regression and classification models using PyTorch and TensorFlow
- Apply gradient descent, backpropagation, and tensor mathematics to train models
- Build LangChain-based RAG applications that answer document questions
- And…
Course specifications
Publisher: CBTNuggets
Instructors: Jonathan Barrios
Language: English
Level: BEGINNER
Number of Lessons: 165
Duration: 25 hours and 3 minutes
Course topics
- What is AI, ML, DL, and GenAI?
- Compare Machine Learning and AI Engineering
- Explore Data Science & ML Development Environments
- Think in Machine Learning with Pseudocode
- Model Linear Regression As Learning A Line
- Classify with Logistic Regression Decision Lines
- Build a One-Neuron Model With the Perceptron
- Explain Scalars, Vectors, Matrices and Tensors
- Break Down the ML Pipeline Step by Step
- Build Your First PyTorch Machine Learning Model
- Implement Linear Regression with PyTorch
- Implement Logistic Regression with PyTorch
- Implement a Non-Linear Neural Network in PyTorch
- Analyze Tensors To Prevent PyTorch Errors
- Apply Data Pipelines to Train PyTorch Models
- Explain Loss, Gradients, and Optimizers
- Build a PyTorch Neural Network Image Classifier
- Apply TensorFlow to Build Your First NLP Pipeline
- Build an RNN Text Classifier with TensorFlow
- Compare RNNs, LSTMs, and GRUs with TensorFlow
- Evaluate a TensorFlow Movie Review Text Classifier
- Explore LLM APIs for AI Engineering
- Analyze LLM Responses and Token Costs
- Program LLMs Using Prompt Engineering
- Build a RAG Application with Chroma & LangChain
- Evaluate AI System Outputs
- Automate AI Evaluation Workflows
- Integrate Tools Into AI Systems
- Build AI Agentic Workflows
Introduction to Machine Learning and AI Engineering Prerequisites
None
Pictures
Introduction to Machine Learning and AI Engineering introduction video
Installation guide
After Extract, watch with your favorite Player.
English subtitle
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
8.4 GB
