AI Career Accelerator: From Zero to AI-Builder Course
-
Introduction to the Course2 Lessons
-
Module 1: AI Foundation and Mindset5 Lessons|1 Quiz
-
Module 2: Python Programming for AI5 Lessons|1 Quiz
-
Module 3: Data Handling & Preprocessing5 Lessons|1 Quiz
-
Module 4: Machine Learning Fundamentals5 Lessons|1 Quiz
-
Module 5: Advanced Machine Learning & Tuning4 Lessons|1 Quiz
-
Module 6: Deep Learning and Neural Networks6 Lessons|1 Quiz
-
Module 7: Natural Language Processing (NLP)5 Lessons|1 Quiz
-
Lesson 1: Text Processing Basics: Tokenization, Stemming, Embeddings
-
Lesson 2: Sentiment Analysis, Text Classification
-
Lesson 3: Using Pre-Trained Models (e.g., BERT, GPT)
-
Lesson 4: Building chatbots and language apps with OpenAI API?
-
Coding Project: Build a Chatbot Using NLP Techniques and OpenAI API
-
Lesson 1: Text Processing Basics: Tokenization, Stemming, Embeddings
-
Module 8: AI Deployment & Tools4 Lessons|1 Quiz
-
Module 9: Ethics, Bias and Responsible AI5 Lessons|1 Quiz
-
Module 10: Portfolio Building & Career Preparation6 Lessons
-
Lesson 1: Setting Up GitHub with your projects
-
Lesson 2: Writing AI-Focused Resumes and LinkedIn Profiles
-
Lesson 3: Job Search Strategies and Freelancing Platforms
-
Lesson 4: Preparing for Entry-Level AI Certifications
-
Final capstone project: Full AI application + portfolio presentation
-
End of course message
-
Lesson 1: Setting Up GitHub with your projects
This comprehensive course takes you on a structured, hands-on journey into Artificial Intelligence (AI) starting with foundational concepts and moving onto real world applications.
First, learners are introduced to AI, machine learning (ML) and deep learning (DL) and then they learn the basic essentials of Python programming. Next, the course covers important data handling and preprocessing techniques, machine learning fundamentals and model evaluation. Deep learning with TensorFlow/Keras, computer vision using CNNs and NLP techniques such as sentiment analysis and pre trained models like BERT are covered through advanced modules.
It teaches practical deployment skills through Streamlit, Flask and cloud platforms. It also stresses ethics, fairness and responsible AI development. Finally, the course ends with career preparation to prepare learners to build a portfolio, write a good resume and conduct job search strategies to successfully launch an AI career.
