Overview What You Will Learn Curriculum Instructor Student Reviews

Course Overview

This learning path is the perfect destination for you. Here, you'll master the basics of machine learning and AI, while you'll also gain practical training on how to apply these technologies to business problems. You'll learn data mining techniques, and you'll also learn to build custom object detector models using Deep Learning. The learning path will provide a hands-on understanding of unsupervised learning and natural language processing.

What You Will Learn

  • Build, train, and evaluate machine learning models for real data.
  • Apply data mining workflows to extract patterns and insights.
  • Implement deep learning architectures for vision tasks.
  • Use unsupervised learning to uncover hidden data structures.
  • Design and deploy AI-driven chatbots and assistants.
  • Build generative AI apps using LangChain.
  • Apply reinforcement learning concepts to decision-making tasks.
  • Translate business problems into measurable AI solutions.

Program Curriculum

Content
  • Chapter 1: Introduction to Machine Learning
  • Chapter 2: Supervised and Unsupervised Learning
  • Chapter 3: Overview of the Machine Learning Workflow
  • Chapter 4: Data Preprocessing Applications
  • Chapter 5: Training, Testing, and Evaluating a ML Model
  • Chapter 6: Deploying and Maintaining a Machine Learning Pipeline
  • Chapter 7: When to Use Machine Learning and When Not To
  • Chapter 8: Structure and Tools of a ML Team

Content
  • Chapter 1: Overview of the Course
  • Chapter 2: Deep Understanding of Regression Models
  • Chapter 3: Optimizing Techniques
  • Chapter 4: Analytical Visit of Classification Models
  • Chapter 5: Practical Visit of Imbalance Classification
  • Chapter 6: Dimensionality Reduction Techniques
  • Chapter 7: Hands-on with the Project

Content
  • Chapter 1: Introduction to Data Mining
  • Chapter 2: Essentials of Data Mining
  • Chapter 3: Analyzing Mining Technique and Algorithm
  • Chapter 4: Deep Dive into Clustering
  • Chapter 5: Deep Dive into Tree Classification
  • Chapter 6: Deep Dive into Handling Outlier and Effect
  • Chapter 7: Understanding Project Pipeline
  • Chapter 8: Advance Clustering Techniques

Content
  • Chapter 1: Overview of Course
  • Chapter 2: Essential of Deep Learning
  • Chapter 3: Introduction to CNN
  • Chapter 4: Deep Dive into CNN
  • Chapter 5: Activation Function & Gradient Descent
  • Chapter 6: Hands-on with CNN
  • Chapter 7: In-depth with Autoencoders
  • Chapter 8: Hands-on with RNN

Content
  • Chapter 1: Overview of Course
  • Chapter 2: Data Collection and Annotation
  • Chapter 3: Introduction to YOLO
  • Chapter 4: Mathematics Behind YOLO
  • Chapter 5: Essentials of Model Training
  • Chapter 6: Train Your Custom Object Detection
  • Chapter 7: Test your Model

Content
  • Chapter 1: Introduction
  • Chapter 2: Principal Component Analysis
  • Chapter 3: t-SNE
  • Chapter 4: Autoencoders
  • Chapter 5: Restricted Boltzmann Machines
  • Chapter 6: Latent Semantic Analysis
  • Chapter 7: Recommender System

Content
  • Chapter 1: Set up the Environment
  • Chapter 2: Jupyter Notebook Coding Environment
  • Chapter 3: Image Process
  • Chapter 4: Classification Model Explanation
  • Chapter 5: TensorFlow Introduction and Quick Guide
  • Chapter 6: Write a Classification Class Program
  • Chapter 7: FaceNet Concepts
  • Chapter 8: Create FaceNet Model
  • Chapter 9: Face Alignment of CASIA Dataset using SSD Face Detection
  • Chapter 10: Face Alignment of CASIA Dataset using MTCNN
  • Chapter 11: CASIA Data Cleaning
  • Chapter 12: Create a Dataset with Facial Masks
  • Chapter 13: Train FaceNet Model
  • Chapter 14: Training Skills
  • Chapter 15: Evaluation of Recognizing Faces with Facial Masks
  • Chapter 16: Training Skills Episode 2
  • Chapter 17: Real Time Face Detection, Facial Mask Detection, and Face Recognition
  • Chapter 18: How to Train a Smaller Model

Content
  • Chapter 1: Introduction to Face Recognition
  • Chapter 2: Environment Setup: Installing Anaconda Package
  • Chapter 3: Python Basics
  • Chapter 4: Setting up Environment - Additional Dependencies (With DLib Fixes)
  • Chapter 5: DLib Error: Downgrading Python and Fixing
  • Chapter 6: Introduction to Face Detectors
  • Chapter 7: Face Detection Implementation
  • Chapter 8: cv2.imshow() Not Responding Issue Fix
  • Chapter 9: Real-time Face Detection from Webcam
  • Chapter 10: Video Face Detection
  • Chapter 11: Real-time Face Detection - Face Blurring
  • Chapter 12: Real-time Facial Expression Detection - Installing Libraries
  • Chapter 13: Real-time Facial Expression Detection - Implementation
  • Chapter 14: Video Facial Expression Detection
  • Chapter 15: Image Facial Expression Detection
  • Chapter 16: Real-time Age and Gender Detection Introduction
  • Chapter 17: Real-time Age and Gender Detection Implementation
  • Chapter 18: Image Age and Gender Detection Implementation
  • Chapter 19: Introduction to Face Recognition
  • Chapter 20: Face Recognition Implementation
  • Chapter 21: Real-time Face Recognition
  • Chapter 22: Video Face Recognition
  • Chapter 23: Face Distance
  • Chapter 24: Face Landmarks Visualization
  • Chapter 25: Multi Face Landmarks
  • Chapter 26: Multi Face Landmarks from Real-time and Pre-saved Video
  • Chapter 27: Face Makeup Using Face Landmarks
  • Chapter 28: Real-time Face Makeup

Content
  • Chapter 1: Development Environment Setup
  • Chapter 2: Introduction To Open-AI GPT API
  • Chapter 3: Creating and Running a LangChain Workflow
  • Chapter 4: Milestone Projects using LangChain

Content
  • Chapter 1: Model-based Reinforcement Learning
  • Chapter 2: Model Free Reinforcement Learning
  • Chapter 3: Deep Reinforcement Learning
  • Chapter 4: Applying Reinforcement Learning to Real Time Applications

Content
  • Chapter 1: Chatbot Introduction
  • Chapter 2: Building Rule-Based Chatbots
  • Chapter 3: AI Chatbot Using Python
  • Chapter 4: AI Chatbot By ChatGPT
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Instructor

Vivek Chaudhary chaudhary

Vivek Chaudhary currently works as a freelance data scientist and has worked with different product-based & EdTech startups. He has published one of the best-selling books on Amazon, “Data Investigation-EDA the right way”. His areas of expertise are applied statistics, EDA, data cleaning techniques, and feature engineering and process to building statistical models. According to him “Building Assumptions” is the important factor to apply statistical tools in real-time. If you can’t build assumptions, then no matter how much you learn at the end, it will be difficult to apply statistical techniques. He has mentored 200+ professionals to start their journey and helped them understand applied statistics & EDA.

Abhilash Nelson

Abhilash Nelson is a pioneering, talented and security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing and supporting impact-full web and mobile applications. He is a Postgraduate Master's Degree holder in Computer Science and Engineering and is currently serving full time as a Senior Solution Architect managing my client's projects from start to finish to ensure high quality, innovative and functional design.

Yuan Po Liao

Johnny has an MS in electrical engineering from National Central University in Taiwan. He used to be a cellphone hardware engineer in the Hon Hai Technology Group. For the interest of coding, he learned C and Python by self-study. He completed IOT Wifi and Zigbee systems applied in TSMC, Arcadyan, Delta, Mitac, and ITRI. With the explosive growth of AI, he learned AI basics by self-study. He has been a full-time AI computer vision engineer since he left his previous firmware system engineer job several years ago.

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