Curriculum

(Pre-requisites - ML - Level1

Upon successful completion, student will be promoted to project development 

Lesson 1: Introduction to Machine Learning

 Introduction to  Data and Machine Learning

Lesson 2: Walking with Python 

 Understanding Python 

Lesson 3: Machine Learning Techniques

 Types of Learning

 Supervised Learning

 Unsupervised Learning

 Advice for Applying Machine Learning

 Machine Learning System Design

Lesson 4: Supervised Learning

 Regression

 Classification

Lesson 5: Supervised Learning - Regression

 Predicting house prices: A case study in Regression

 Linear Regression & Logistic: A Model-Based Approach

 Regression fundamentals: Data and Models

Lesson 6: Supervised Learning - Classification

 Analyzing the sentiment of reviews: A case study in Classification

 Classification fundamentals : Data and Models

 Understanding Decision Trees and Naive Bayes

 Feature selection in Model building

 Linear classifiers

 Decision boundaries

 Training and evaluating a classifier

 False positives, false negatives, and confusion matrices

 Classification ML block diagram

Lesson 7: Unsupervised Learning

 Clustering

 Recommendation

 Deep Learning

Lesson 8: Unsupervised Learning - Clustering

 Document retrieval: A case study in clustering and measuring similarity

 Clustering System Overview

 Clustering fundamentals : Data and Models

 Feature selection in Model building

 Prioritizing important words with tf-idf

 Clustering and similarity ML block diagram

Lesson 9: Unsupervised Learning - Recommendation

 Recommending Products

 Recommender systems overview

 Collaborative filtering

 Understanding Collaborative Filtering and Support Vector Machine

 Effect of popular items

 Normalizing co-occurrence matrices and leveraging purchase histories

 The matrix completion task

 Recommendations from known user/item features

 Recommender systems ML block diagram

Lesson 10: Unsupervised Learning – Deep Learning

 Deep Learning: Searching for Images

 Searching for images: A case study in deep learning

 Learning very non-linear features with neural networks

 Application of deep learning to computer vision

 Deep learning performance

 Demo of deep learning model on ImageNet data

 Deep learning ML block diagram