Certified Artificial Intelligence Professional

Course details

• Classification Overview and Evaluation Techniques
• Decision Trees
• Prediction with Decision Trees and Student Performance Data
• Random Forests
• Predicting Bird Species with Random Forests

• The Problem of Text Classification
• Detecting YouTube Comment Spam with Bag of Words and Random Forests
• Word2Vec Models
• Detecting Positive/Negative Sentiment in User Reviews

• Neural Networks
• Identifying the Genre of a Song Using Audio Analysis and Neural Networks
• Revising the Spam Detector to Use Neural Networks
• Overview of Deep Learning and Convolutional Neural Networks
• Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks
• Revising the Bird Species Identifier to Use Images

• Solving problems with computers
• Machine Learning: Why should you jump on the bandwagon?
• Plunging In - Machine Learning Approaches to Spam Detection
• Spam Detection with Machine Learning Continued
• Get the Lay of the Land: Types of Machine Learning Problems

• Random Variables
• Bayes Theorem
• Naive Bayes Classifier
• Naive Bayes Classifier: An example
• K-Nearest Neighbours
• K-Nearest Neighbours: A few wrinkles
• Support Vector Machines Introduced
• Support Vector Machines: Maximum Margin Hyperplane and Kernel Trick
• Artificial Neural Networks: Perceptrons Introduced

• Clustering: Introduction
• Clustering: K-Means and DBSCAN

• Association Rules Learning

• Dimensionality Reduction
• Principal Component Analysis

• Regression Introduced: Linear and Logistic Regression
• Bias Variance Trade-off

• Applying ML to Natural Language Processing
• Installing Python - Anaconda and Pip
• Natural Language Processing with NLTK
• Natural Language Processing with NLTK - See it in action
• Web Scraping with BeautifulSoup
• A Serious NLP Application: Text Auto Summarization using Python
• Autosummarize News Articles
• News Article Classification using K-Nearest Neighbors
• News Article Classification using Naive Bayes Classifier
• Scraping News Websites
• Feature Extraction with NLTK
• Classification with KNN
• Classification with Naive Bayes
• Document Distance using TF-IDF
• News Article Clustering with K-Means and TF-IDF
• Clustering with K Means

• Solve Sentiment Analysis using Machine Learning
• Sentiment Analysis - What's all the fuss about?
• ML Solutions for Sentiment Analysis - the devil is in the details
• Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)
• Regular Expressions
• Regular Expressions in Python
• Put it to work: Twitter Sentiment Analysis
• Twitter Sentiment Analysis

• Using Tree Based Models for Classification
• Planting the seed - What are Decision Trees?
• Growing the Tree - Decision Tree Learning
• Branching out - Information Gain
• Decision Tree Algorithms
• Decision Trees predict Survival (Kaggle)

• Overfitting - the bane of Machine Learning
• Overfitting Continued
• Cross Validation
• Simplicity is a virtue – Regularization
• The Wisdom of Crowds - Ensemble Learning
• Ensemble Learning continued - Bagging, Boosting and Stacking

• Random Forests - Much more than trees
• Back on the Titanic - Cross Validation and Random Forests

• Solving Recommendation Problems
• What do Amazon and Netflix have in common?
• Recommendation Engines - A look inside
• What are you made of? - Content-Based Filtering
• With a little help from friends - Collaborative Filtering
• A Neighbourhood Model for Collaborative Filtering
• Top Picks for You! - Recommendations with Neighbourhood Models
• Discover the Underlying Truth - Latent Factor Collaborative Filtering
• Latent Factor Collaborative Filtering contd.
• Gray Sheep and Shillings - Challenges with Collaborative Filtering
• The Apriori Algorithm for Association Rules

• Numpy in Python
• Numpy and Scipy in Python
• Movielens and Pandas
• Data Analysis with Pandas
• Movie Recommendation with Nearest Neighbour CF
• Top Movie Picks (Nearest Neighbour CF)
• Movie Recommendations with Matrix Factorization
• Association Rules with the Apriori Algorithm

• Computer Vision - An Introduction
• Perceptron Revisited
• Deep Learning Networks Introduced
• Handwritten Digit Recognition