Description

“Master AI Development with Python - The Skill Top Engineers Swear By” is a focused, hands-on course that equips you with the essential tools, frameworks, and coding techniques needed to start building AI-powered applications from scratch. Whether you’re an aspiring engineer, data analyst, or tech-savvy professional, this course will help you build a strong foundation in AI development using the world’s most in-demand programming language.

Course Objectives

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

Understand the core concepts of Artificial Intelligence, including machine learning and natural language processing.

Get hands-on experience with Python libraries such as NumPy, pandas, scikit-learn, and OpenAI's API or Hugging Face Transformers.

Learn how to preprocess data, build basic ML models, and integrate AI features into real applications.

Understand the structure of AI development workflows, from data to model to deployment.

Build and test a basic AI project using Python in a collaborative environment.

Target Audience

This course is ideal for:

Junior developers, tech professionals, or data analysts ready to upskill in AI development.

Python beginners with basic programming experience looking to apply their skills to machine learning or AI.

STEM students or career switchers exploring practical, in-demand AI skills.

Product managers and technical leads who want to understand how AI is built and deployed.

Anyone looking to get hands-on with AI tools, libraries, and workflows using Python.

Basic Understanding

There are no prerequisites for this course.

Course Content

No sessions available.

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Simpliv LLC
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Fremont, CA 94539, USA

Master AI Development with Python - The Skill That Sets You Apart

Session 1: Module 1: Python for AI - Essential Building Blocks

  1. Refresher: Python syntax for AI (lists, loops, functions, classes)
  2. Working with NumPy and pandas for data manipulation
  3. Loading and preparing datasets (CSV, JSON, API calls)
  4. Mini project: Analyze a dataset and prep it for modelling

Session 2: Module 2: Machine Learning Basics with scikit-learn

  1. What is machine learning? (Supervised vs unsupervised learning)
  2. Introduction to scikit-learn: model selection, training, and evaluation
  3. Build your first ML model (e.g., classification or regression task)
  4. Model evaluation: accuracy, confusion matrix, and overfitting
  5. Hands-on lab: Create and test a model using a real dataset

Session 3: Module 3: Building AI with NLP & Generative Tools

  1. Intro to Natural Language Processing (NLP) with spaCy or Hugging Face
  2. Using OpenAI’s API or transformers to build generative text models
  3. Create a simple chatbot or AI content generator using Python
  4. Responsible AI: Prompt safety, biases, and API rate limits
  5. Mini build: Your own GPT-powered text assistant

Session 4: Module 4: From Code to Concept - How to Keep Building

  1. Structuring AI projects for growth and maintainability
  2. Model deployment basics (Streamlit, Flask, or Jupyter Notebook sharing)
  3. Using GitHub and community tools to accelerate your learning
  4. Final project: Build and present a simple AI-powered tool (team or solo)
  5. Wrap-up: Next steps, career paths, and portfolio tips for AI developers

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