KwickAcademy Artificial Intelligence · free
Artificial Intelligence: free video lessons
50 lessons on Artificial Intelligence, about 340 minutes in total, taught by Kajal Ma'am. Each one has the video, the tables from the board, the quick answers and the full text.
AI Ethics: Principles, Frameworks and Policies
AI Terminologies, Benefits and Limitations
AI and Society: Self-driving Cars and Other Challenges
Bias in AI: Sources, Awareness and Mitigation
Big Data Analytics and Mining Data Streams
Big Data: Types, Characteristics, Advantages and Disadvantages
Build a No-code AI Model: Teachable Machine and Machine Learning for Kids
Careers in AI: Demand, Job Roles, Skills and Industries
Chatbots, Sentiment Analysis and Emotion Detection
Classification and the k-Nearest Neighbours Algorithm
Clustering and the k-Means Algorithm
Computer Vision: Applications, Challenges and the Future
Computer Vision: How Machines See
Convolutional Neural Networks
Data Acquisition: Sources, Features and Types of Data
Data Exploration and Visualisation
Data Literacy: Meaning, Importance and the Data Literacy Process
Data Pre-processing and Data Interpretation
Data Privacy and Data Security
Data Science Methodology
Data Storytelling: Turning Data into a Story
Decision Trees
Deep Learning Hands-on with TensorFlow and Keras
Deployment of AI Solutions
Design Thinking, Empathy Maps and the SDGs
Evaluation Basics: True Positive, False Positive, True Negative, False Negative
Finding Patterns: How AI Spots Patterns in Numbers and Pictures
Generative AI: How it Works, Types and Applications
Generative AI: Types, Tools, Applications, Benefits and Limitations
Image Processing with OpenCV in Python
Images as Data: Pixels, Resolution, Features and Convolution
Large Language Models and the Ethics of Generative AI
Matrices for AI
Model Evaluation Metrics: Confusion Matrix, Accuracy, Precision, Recall and F1
Modelling: Rule-based vs Learning-based AI
Natural Language Processing: Language, Phases and Applications
Neural Networks: Parts, Working and Types
No-code AI with Orange Data Mining
Orange Data Mining: Components and the Widget Catalogue
Probability for AI
Problem Scoping: the 4Ws Canvas and 5W1H
Regression: Correlation and Linear Regression
Statistics for AI: Mean, Median, Mode, Variance and Standard Deviation
Storytelling Basics: Elements of a Story and Freytag's Pyramid
Text Processing: Normalisation, Tokenisation, Bag of Words and TF-IDF
The AI Capstone Project: Planning, Log Book, Documentation and Video
The AI Project Cycle
The Three Domains of AI: Data, Computer Vision and Natural Language Processing
Types of Machine Learning: Supervised, Unsupervised and Reinforcement
