Professional Certificate in AI & Machine Learning Basics

Day 1
Learning the Basics:
The fundamental of AI

[Putting Into perspective – A big picture]

• History / Timeline of ML
• AI vs ML vs Deep Learning
• The Perceptron and Neural Network
• Machine Learning Cheat Sheets

[ML Basics – Branches and Applications]

• Sup/Unsupervised Learning
• Reinforcement Learning
• Decision Trees
• SVM

[Getting Ready – Installation and Setting up]

• What you need for this course
• Conda Environment
• Python
• Other Useful Packages
• A simple python program

[Getting Ready with Google Co-Lab]

• Using Google Co-lab
• Linear Regression using NN

Day 2
From Data to Insights

[Supervised vs Unsupervised Learning]

• Loading Data Sets
• Preprocessing Data
• Label Encoding
• Logistic Regression and Naïve Bayes Classifier
• Confusion Matrix
• Support Vector Machines
• Classifying Data
• What is Regression
• Estimation of a variable

[Ensemble Learning]

• Decision Trees
• Random Forests
• Class Imbalance
• Optimal Training Parameters
• Prediction

[Clustering Data with K-Means]

• Estimating number of Clusters
• Estimating quality of Clusters
• Building a Classifier
• Segmenting Data
• Create Training Pipeline
• Extracting Nearest Neighbours
• Building a K-Nearest Neighbours Classifier
• Computing similarity Scores
• Finding similar users
• Building a Recommendation System