Machine Learning & AI

Learning Outcomes

· Understanding of AI and Machine Learning

· Ability to identify real-life problems that can be solved through Machine Learning

· Ability to perform feature engineering and pre-processing steps

· Understanding of Machine learning algorithms

· Skill to implement and build machine learning models with python and its packages                                

· Understanding of model evaluation and visualization techniques.

· Ability to apply Machine Learning to Images

· Attain a basic Understanding of Deep Learning and its implementation

Module 1: Introduction to Machine Learning

· Introduction to AI

· What is machine learning?

· Types of learning and Fundamentals

· Installation and update of tools

· Taming Python

Hands on

· Python Recap

· Numpy

Module 2: Exploring and using data sets

· Machine learning algorithms

· Collecting data

· Feature Engineering and Model Selection

· Learn the steps to pre-process a dataset and prepare it for machine learning algorithms

Hands on

· Matplotlib, Seaborn -visualization

· Feature engineering and pre-processing –loading datasets, PCA, LDA, Label Encoding, Scaling

Module 3: Supervised vs. unsupervised learning

· Review of machine learning algorithms

· Regression - Linear Regression, Lasso, Ridge and Others- Implementation and Evaluation

· Classification – SVM, Naïve Bayes, Decision Trees, KNN, Random Forest - Implementation and Evaluation

· Clustering – K Means, DBScan, K Medoid - Implementation and Evaluation

Hands on

· pandas –loading external dataset, data pre-processing

· Implementing – Regression, Classification and Clustering

· Evaluating and visualizing results

Module 4: Neural Networks and NLP

· Introduction to Neural Networks

· Perceptron – Implementation

· MLP – Implementation

· Basics of NLP

Hands on

· Implementing perceptron

· Implementing MLP

· Text feature extraction using NLTK and classification

Module 5: Applied Machine Learning and Computer Vision

· Image representation

· Manipulating images with OpenCV and PIL

· Feature extraction from images

· Classification

· Visualization

Hands on

· Data collection

· Image feature extraction using opencv

· Image classification using svm