Markovdata
Machine Learning and Big Data Track
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Course sections

Section 1
1- Machine learning and Python (36hrs)
Section 2
Module 1: Statistics Linear Algebra & Probability for Machine Learning
1
Descriptive Statistics
2
Probability
3
Normalizatio
4
Inferential Statistics
5
Linear Algebra Review
Section 3
Module 2: Machine Learning
1
Introduction to Artificial Intelligence
2
Introduction to Data Science
3
Data Science life cycle.
4
Introduction to Machine Learning & Data Mining
5
Machine Learning
6
Data Mining
7
Supervised and Unsupervised Learning
8
Types of Data
9
Data Preprocessing
10
Frequent Item Sets
11
Association Rules & Apriori Algorithm
12
Regression for Data Science
13
Bias and Variance
14
Base Classifiers for Data Science
15
Clustering
16
Evaluation of Learning Models for a Data Scientist
Section 4
Module 3: Python for Machine Learning
1
Python Basics
2
Python Data Structures
3
Python Programming Fundamentals
4
Data Science Libraries
Section 5
Module 4: Class Projects
1
Project 1: Market Basket Analysis (Apriori)
2
Project 2: Automotive Price Prediction (Linear Regression Algorithim)
3
Project 3: Fraud Detection (Logistic Regression Algorithm)
4
Project 4: Customer Churn Prediction (Neural Networks Algorithm)
5
Project 5: Customer Segmentation (K-Means Clustering)
Section 6
2- Big Data Fundamentals (32hrs)
Section 7
Module 1: Apache Hadoop essentials
1
Big Data
2
Markov Data Cluster Overview
3
Hadoop
4
Hive & SQOOP
5
Labs
Section 8
Module 2: Hadoop Processing Engine [ Apache Spark ]
1
Introduction to Apache Spark
2
Spark fundamentals
3
Working With RDDs
4
Writing Spark applications
5
Spark Data aggregation
6
Spark data Persistence
7
Sparkling queries with Spark SQL
8
Running Spark on a Spark standalone cluster
9
Running Spark on a Spark multi-node cluster
10
Machine Learning in spark
11
Labs
Section 9
Module 3: Hadoop Cluster Data Ingestion [Apache KAFKA]
1
Overview of ZooKeeper
2
Cluster, Nodes, Kafka Brokers
3
Consumers, Producers, Logs, Partitions, Records, Keys
4
Replicas, Followers, Leaders
5
Kafka Connect API
6
Labs
Section 10
Module 4: Class Projects
1
Project 1: Website Activity Tracking
2
Project 2: Building ETL PipeLine along with RDBMS
3
Project 3: Analyze website logs using spark
4
Project 4: Twitter Data
Section 11
3- Advanced Big Data (40hrs)
Section 12
Module 1: Bash Scripting Language
1
What is a Bash Script
2
Variables
3
Input – Different ways to supply data
4
Arithmetic
5
If Statements
6
Loops
7
Functions
Section 13
Module 2: Data Warehousing with Apache Hive2
1
Data base fundamentals
2
Data Warehouse Concepts
3
Apache Hive
4
Labs
Section 14
Module 3: Hadoop Processing Engine [ Apache Spark 2]
1
Spark streaming
2
Spark Streaming Architecture and Advantages
3
Goals of Spark Streaming
4
How does Spark Streaming works
5
Spark Streaming Sources
6
Spark Streaming Operations
7
Streaming applications with kafak
8
Consume data from kafka using spark
9
produce data to kafka using spark
10
spark with yarn configurations
11
Labs
Section 15
Module 4: NO SQL Data Bases [Apache Hbase]
1
World Before NoSQL
2
WHAT IS HBASE
3
HBASE TABLES
4
Merge And Compaction
5
DDI Concepts
6
Labs
Section 16
Module 5: Class Projects
1
Project 1: Twitter Online HashTag
2
Project 2: Customer profile data management
3
Project 3: customer offers accumulators
Section 17
4- Final project

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