Introduction
  • Introduction
Environment Setup
  • Installing PyCharm and Python on Windows
  • Installing PyCharm and Python on Mac
  • Installing TensorFlow and Keras
Artificial Intelligence Basics
  • Why to learn artificial intelligence and machine learning?
  • Introduction to machine learning
Linear Regression
  • Linear regression introduction
  • Linear regression theory - optimization
  • Linear regression theory - gradient descent
  • Linear regression implementation I
  • Linear regression implementation II
Logistic Regression
  • Logistic regression introduction
  • Logistic regression introduction II
  • Logistic regression example I - sigmoid function
  • Logistic regression example II- credit scoring
  • Logistic regression example III - credit scoring
  • Cross validation introduction
  • Cross validation example
K-Nearest Neighbor Classifier
  • K-nearest neighbor introduction
  • K-nearest neighbor introduction - lazy learning
  • K-nearest neighbor introduction - Euclidean-distance
  • Bias and variance revisited
  • K-nearest neighbor implementation I
  • K-nearest neighbor implementation II
  • K-nearest neighbor implementation III
Naive Bayes Classifier
  • Naive Bayes classifier introduction I
  • Naive Bayes classifier introduction II - illustration
  • Naive Bayes classifier implementation
  • Text clustering - basics
  • Text clustering - inverse document frequency (TF-IDF)
  • Naive Bayes example - clustering news
Support Vector Machines (SVMs)
  • Support vector machine introduction - linear case
  • Support vector machine introduction - non-linear case
  • Support vector machine introduction - kernels
  • Support vector machine example I - simple
  • Support vector machine example II - iris dataset
  • Support vector machines example III - parameter tuning
  • Support vector machine example IV - digit recognition
  • Support vector machine example V - digit recognition
Decision Trees
  • Decision trees introduction - basics
  • Decision trees introduction - entropy
  • Decision trees introduction - information gain
  • The Gini-index approach
  • Decision trees introduction - pros and cons
  • Decision trees implementation
  • Decision trees implementation II - parameter tuning
  • Decision tree implementation III - identifying cancer
Random Forest Classifier
  • Pruning introduction
  • Bagging introduction
  • Random forest classifier introduction
  • Random forests example I - iris dataset
  • Random forests example II - credit scoring
  • Random forests example III - OCR parameter tuning
Boosting
  • Boosting introduction - basics
  • Boosting introduction - illustration
  • Boosting introduction - equations
  • Boosting introduction - final formula
  • Boosting implementation I - iris dataset
  • Boosting implementation II -wine classification
  • Boosting vs. bagging
Principal Component Analysis (PCA)
  • Principal component analysis introduction
  • Principal component analysis example
  • Principal component analysis example II
Clustering
  • K-means clustering introduction I
  • K-means clustering introduction II
  • K-means clustering example
  • K-means clustering - text clustering
  • DBSCAN introduction
  • DBSCAN example
  • Hierarchical clustering introduction
  • Hierarchical clustering example
  • Hierarchical clustering - market segmentation
Computer Vision - Face Detection
  • Computer vision introduction
  • Viola-Jones algorithm
  • Haar-features
  • Integral images
  • Boosting in computer vision
  • Cascading
  • Face detection implementation I - installing OpenCV
  • Face detection implementation II - CascadeClassifier
  • Face detection implementation III - CascadeClassifier parameters
  • Face detection implementation IV - tuning the parameters
  • Face detection implementation V - detecting faces real-time
  • Computer vision bootcamp
Machine Learning Project I - Face Recognition
  • The Olivetti dataset