###### Machine Learning Advanced Certification

###### Training

Lesson 1: Introduction to Machine Learning

ï‚· Introduction to Big Data and Machine Learning

Lesson 2: Walking with Python or R

ï‚· Understanding Python or R

Lesson 3: Machine Learning Techniques

ï‚· Types of Learning

ïƒ¼ Supervised Learning

ïƒ¼ Unsupervised Learning

ï‚· Advice for Applying Machine Learning

ï‚· Machine Learning System Design

Lesson 4: Supervised Learning

ï‚· Regression

ï‚· Classification

Lesson 5: Supervised Learning - Regression

ï‚· Predicting house prices: A case study in Regression

ïƒ¼ Linear Regression & Logistic: A Model-Based Approach

ïƒ¼ Regression fundamentals : Data and Models

Lesson 6: Supervised Learning - Classification

ï‚· Analyzing the sentiment of reviews: A case study in Classification

ïƒ¼ Classification fundamentals : Data and Models

ïƒ¼ Understanding Decision Trees and Naive Bayes

ïƒ¼ Feature selection in Model building

ïƒ¼ Linear classifiers

ïƒ¼ Decision boundaries

ïƒ¼ Training and evaluating a classifier

ïƒ¼ False positives, false negatives, and confusion matrices

ïƒ¼ Classification ML block diagram

Lesson 7: Unsupervised Learning

ï‚· Clustering

ï‚· Recommendation

ï‚· Deep Learning

Lesson 8: Unsupervised Learning - Clustering

ï‚· Document retrieval: A case study in clustering and measuring similarity

ïƒ¼ Clustering System Overview

ïƒ¼ Clustering fundamentals : Data and Models

ïƒ¼ Feature selection in Model building

ïƒ¼ Prioritizing important words with tf-idf

ïƒ¼ Clustering and similarity ML block diagram

Lesson 9: Unsupervised Learning - Recommendation

ï‚· Recommending Products

ïƒ¼ Recommender systems overview

ïƒ¼ Collaborative filtering

ïƒ¼ Understanding Collaborative Filtering and Support Vector Machine

ïƒ¼ Effect of popular items

ïƒ¼ Normalizing co-occurrence matrices and leveraging purchase histories

ïƒ¼ The matrix completion task

ïƒ¼ Recommendations from known user/item features

ïƒ¼ Recommender systems ML block diagram

Lesson 10: Unsupervised Learning – Deep Learning

ï‚· Deep Learning: Searching for Images

ïƒ¼ Searching for images: A case study in deep learning

ïƒ¼ Learning very non-linear features with neural networks

ïƒ¼ Application of deep learning to computer vision

ïƒ¼ Deep learning performance

ïƒ¼ Demo of deep learning model on ImageNet data

ïƒ¼ Deep learning ML block diagram

Lesson 11: Spark Core and MLLib

ï‚· Spark Core

ï‚· Spark Architecture

ï‚· Working with RDDs

ï‚· Machine learning with Spark – Mllib

## Machine Learning

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