Machine Learning Training - LIVE, Instructor Led

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that which makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.

Machine Learning Training Plans

  PLAN A PLAN B
Description Machine Learning SQL & Machine Learnig
Duration 2 Weeks 5.5 Weeks
Completely Real-time Check-Symbol-for-Yes Check-Symbol-for-Yes
Resume, Job Assistance Check-Symbol-for-Yes Check-Symbol-for-Yes
SSRS Installation, Configuration Check-Symbol-for-Yes Check-Symbol-for-Yes
SSRS Groups, Sub Groups Check-Symbol-for-Yes Check-Symbol-for-Yes
SSRS Chart Reports Check-Symbol-for-Yes Check-Symbol-for-Yes
Dashboards, Actions, Bookmarks Check-Symbol-for-Yes Check-Symbol-for-Yes
SSRS Deployment Check-Symbol-for-Yes Check-Symbol-for-Yes
Mobile & KPI Reports Croos-symbol-for-No Check-Symbol-for-Yes
SSRS Integrations Croos-symbol-for-No Croos-symbol-for-No
Power BI Report Design Croos-symbol-for-No Croos-symbol-for-No
Power BI Desktop, Custom Visuals Croos-symbol-for-No Croos-symbol-for-No
Data Modelling with DAX Croos-symbol-for-No Croos-symbol-for-No
Power BI Cloud, Excel Analysis Croos-symbol-for-No Croos-symbol-for-No
Power BI Mobile, R, REST API Croos-symbol-for-No Croos-symbol-for-No
Python Integration, Data Flows Croos-symbol-for-No Croos-symbol-for-No
MCSA - MSBI Certification Croos-symbol-for-No Croos-symbol-for-No
Total Course Fee INR 13000/- INR 15000/-

Machine Learning Training

  Timings (IST) Free Demo Start Date Register
1 9 AM to 10 AM August 27th August 28th Register
2 6:30 PM - 7:30 PM August 19th August 20th Register

For Weekend / FastTrack Trainings Click Here

Machine Learning Training - Highlights :

Funel Reports Dashboards
TreeMaps Power BI Integration
Azure Integration Interviews, Job Support
Real-time Project Basic to Advanced DAX
Resume Guidance Certification, Placements
Custom Visuals Power BI Mobile
Big Data Sources R, Python, JSON
Excel Analysis Excel Online
Google Big Query Report Server
Gateways, Rest API Power BI Admin

SSRS LIVE Online Training Course Contents:

PART 1 Of 2: SQL Server Basics, Queries, Stored Procedures and Database Development

Module I: SQL Basics, SQL Server Concepts

(For Plan B)

Module II: T-SQL Queries

(For Plan B)

Module III: T-SQL Programming, Banking Project

(For Plan B)

DAY 1: INTRODUCTION, INSTALLATION

  • Data, Databases and RDBMS Software
  • Database Types : OLTP,DWH,OLAP,HTAP
  • Microsoft SQL Server Advantages, Use
  • DB Engine, BI, Data Science Components
  • SQL : Purpose, Real-time Usage Options
  • SQL Versus Microsoft T-SQL [MSSQL]
  • Microsoft SQL Server - Career Options
  • Real-time Projects & Job Responsibilities
  • SQL Server @ Cloud: Azure, AWS, G Cloud
  • Versions and Editions of SQL Server
  • SQL Server and SSMS Installation Plan
  • SQL Server Pre-requisites : S/W, H/W
  • System Configuration Checker (SCC) Tool
  • SQL Server 2019 : Installation [Overview]

DAY 7: JOINS, T-SQL QUERIES - Level 1

  • JOINS - Table Comparisons Queries
  • INNER JOIN - Examples, WHERE, ON
  • OUTER JOIN - Examples, WHERE, ON
  • Left Outer Joins with Example Queries
  • Right Outer Joins with Example Queries
  • FULL Outer Joins - Real-time Scenarios
  • MERGE, LOOP, HASH Join Options
  • Big Table Versus Small Table Joins
  • Join Types Versus Join Options in T-SQL
  • CROSS JOIN Versus CROSS APPLY
  • Using Joins for DB Metadata Audits
  • Joining more than 2 Tables in T-SQL
  • Joining Tables with Query Conditions
  • Joining Unrelated Tables, Join Options

DAY 14: STORED PROCEDURES - Level 2

  • Table Valued Parameters (TVP) - Usage
  • SQL Injection Attacks - Type Precautions
  • READONLY Parameters - Stored Procedures
  • OUTPUT Parameters - Stored Procedures
  • User Defined Data Types, Real-time Use
  • Dynamic Data Insertions with Stored Procs
  • Table Cloning, Data Inserts @ Table Variables
  • CTE : Common Table Expressions
  • Real-time Scenarios with CTEs - Usage
  • ROW_NUMBER() with CTE Queries
  • Using CTEs for Avoiding Self Joins
  • Using CTEs for Avoiding Sub Queries
  • Recursive CTEs and ANCHOR Element
  • Termination Checks in Recursive CTEs

Day 2: INSTALLATIONS [DETAILED]

  • SQL Server 2016 Installation, Guidance
  • SQL Server 2017 Installation, Guidance
  • Instance Types: Default, Named Instances
  • SQL Server Features and Importance
  • SQL Server Database Engine For OLTP
  • File Stream and Collation Properties
  • SQL Server Authentication Modes
  • Windows Login & SQL Server Login Types
  • System Databases: Master, Model, MSDB
  • TempDB and Resource Database Locations
  • "sa" System Account, File Stream Property
  • SQL Server Management Studio. SSMS
  • Test Connection to Local Servers
  • Test Connection to Remote Servers

DAY 8: JOINS, T-SQL QUERIES - Level 2

  • GROUP BY Queries and Aggregations
  • GROUP BY Queries with Having Clause
  • Group By Queries - Query Design Rules
  • ROLLUP( ) & CUBE( ) Summary Values
  • GROUPING() Function for Row Status
  • Replacing Nulls: ISNULL, COALESCE
  • Joining Tables with Group By, Having
  • Sub Queries and Alternatives to Joins
  • Using Joins with Group By Queries
  • Using Joins with Nested Sub Queries
  • Sub Queries with Joins and Group By
  • Using UNION and UNION ALL in Queries
  • Nested Sub Queries with Group By, Joins
  • Comparing WHERE, HAVING Conditions

DAY 15: STORED PROCEDURES - Level 3

  • Views on Tables - SCHEMABINDING
  • ENCRYPTION and CHECK OPTION
  • Cascaded Views, Encrypted Views
  • Updatable Views, Joins with Triggers
  • Error Handling in T-SQL: TRY & CATCH
  • Error Handling, THROW in Procedures
  • Stored Procedures - WITH RESULT SETS
  • Cursors - Benefits, Cursors in SProcs
  • ForwardOnly, Scroll & Local Cursors
  • Static, Dynamic & Global Cursors
  • Keyset Cursors and @@FetchStatus
  • Nesting of Stored Procedures - Dynamic
  • Data Formatting and WHILE Loops
  • Using Temporary Tables for Formatting

Day 3: DATABASE & SQL BASICS - Level 1

  • SQL : Purpose and Real-time Usage
  • DDL, DML, SELECT, DCL and TCL
  • SSMS Tool : Connections and Usage
  • SQL Versus T-SQL : Basic Difference
  • Server Connections, Session Creations
  • Creating Databases and DB Connections
  • Creating Tables. Int, Char Data Types
  • Single Row Inserts, Multi Row Inserts
  • INSERT and INSERT INTO Statements
  • SELECT Statement for Table Retrieval
  • WHERE Conditions with =, OR, IN
  • AND, OR, NOT, IN, NOT IN Conditions
  • LIVE QUERY STATISTICS in SSMS
  • Table Scan Properties in SQL Server

DAY 9: JOINS, T-SQL QUERIES - Level 3

  • Cast, Convert, DateAdd, DateDiff Functions
  • Date & Time Styles, Data Formatting
  • Using Date and Time Formats in Queries
  • String Functions: SUBSTRING,REPLICATE
  • CHARINDEX, PATINDEX, LEFT, RIGHT
  • LEN, STUFF, LTRIM, RTRIM, REVERSE
  • DIFFERENCE, SOUNDEX, STRING_SPLIT
  • WHEN MATCHED and NOT MATCHED
  • Incremental Loads with MERGE Statement
  • IIF(), CASE with WHEN and ELSE, END
  • FETCH - OFFSET, NEXT ROWS, Order By
  • Using PIVOT Function and FOR Values
  • ROW_NUMBER() and RANK() Queries
  • Dense Rank and Partition By Queries

DAY 16: FUNCTIONS - Level 2

  • Functions : Types, Real-world Usage
  • Inline Functions, Multi Line Functions
  • Looping Concepts in SQL Server
  • WHILE Loop Queries and UNPIVOT
  • GROUPING SETS and OUTPUT Function
  • EXISTS and RAISEERROR Functions
  • TRY_CONVERT, TRY_PARSE Functions
  • Using BULK INSERT & BULKCOLUMN
  • OPENROWSET For Data Import, CAST
  • OPENJSON For JSON Data Formats
  • JSON Files - Data Import into SQL DB
  • Json $Tag Notations, SELECT .. INTO
  • XML Options in T-SQL Queries, Joins
  • XML AUTO, XML RAW and XML PATH

DAY 4: DATABASE & SQL BASICS - Level 2

  • Creating Databases : Files [MDF, LDF]
  • Single Row Inserts, Multi Row Inserts
  • SELECT. WHERE Conditions, Operators
  • AND, OR, NOT, Mathematical Operators
  • IN, NOT IN, BETWEEN, NOT BETWEEN
  • IS NULL, LIKE, NOT LIKE. % and _
  • CHAR Versus VARCHAR Data Types
  • GO Statement, SQL BATCH Concept
  • DISTINCT, TOP, FETCH, ORDER BY
  • Basic Sub Queries with SELECT
  • UPDATE and DELETE Statements
  • TRUNCATE, ALTER, ADD and DROP
  • Table Scans, Measuring Query Time
  • CLIENT STATISTICS and Query Trails

DAY 10: Views, Functions, Procedure Basics

  • Views : Types, Usage in Real-time
  • System Predefined Views and Audits
  • Listing Databases, Tables, Indexes
  • Functions : Types, Usage in Real-time
  • Scalar, Inline and Multi-Line Functions
  • System Predefined Functions, Audits
  • DBId, DBName, ObjectID, ObjectName
  • Variables & Parameters in SQL Server
  • Procedures : Types, Usage in Real-time
  • User & System Predefined Procedures
  • Parameters and Dynamic SQL Queries
  • Sp_help, Sp_helpdb and sp_helptext
  • Sp_recompile, sp_pkeys, sp_rename
  • Compare Views, SPs and Functions

Day 17: Database, Index Architecture

  • Database Architecture - Detailed
  • Primary File, Secondary Files [mdf, ndf]
  • Database Log Files (T-LOG) For Audits [ldf]
  • Data Files, Log Files, LSN & VLF
  • Transaction Log File [LDF] & LSN
  • Filegroups : ReadWrite & Read Only
  • Indexes: Architecture and Types
  • Clustered and Non Clustered Indexes
  • Included and ColumnStore Indexes
  • FILTERED and COVERING Indexes
  • UNIQUE Indexes, Online Indexes
  • B Tree Structure, IAM Page [Root]
  • Indexed Views / Materialized Views
  • Pages, Extents, and Checkpoints

DAY 5: SQL Basics 3, Server Architecture

  • SQL Server Architecture Components
  • TDS Packets (N/W) in Client - Server
  • Protocols, SQL Native Client (SNAC)
  • Parser, Compiler, SQL Query Validations
  • Query Optimizer (QO) and SQL Manager
  • Storage Engine, File and DB Manager
  • Transaction Manager and Lock Manager
  • Buffer Manager, SQL OS and IO Buffer
  • Synchronization and Thread Scheduler
  • MDAC and CLR Components in SQL OS
  • Temporary Tables : Real-time Use
  • Local and Global Temporary Tables
  • Schemas : Real-time Usage, Creation
  • Schema - Table Transfer. 2P, 3P Naming

Day 11: Triggers, Transactions, DTC

  • Triggers - Purpose, Real-world Usage
  • FOR/AFTER Triggers - Real time Use
  • INSTEAD OF Triggers - Real time Use
  • INSERTED, DELETED Memory Tables
  • Enable Triggers and Disable Triggers
  • Database Level, Server Level Triggers
  • Auditting Triggers and Real World Use
  • Transactions : Types, ACID Properties
  • EXPLICIT & IMPLICIT Transactions
  • COMMIT and ROLLBACK Statements
  • Query Blocking Scenarios @ Real-time
  • Open Transctions in Real-world, Impact
  • NOLOCK and READPAST Lock Hints
  • Lock Hints, Joins @ T-SQL Queries

DAY 18 - 20: REAL-TIME PROJECT (BANKING)
Includes 2500 Lines of Code (COMPLETELY SOLVED).

Phase 1: DATABASE DESIGN
  • Understanding Project Requirements
  • End to End Project Work Flow
  • Naming Conventions in Real-time
  • Table Schemas : Creation and Use
  • Implementing Normal Forms (OLTP)
  • Computed Columns and Data Types
  • SQL_Variant, Bit, sysname Data Types
  • Email and Phone Number Validations
  • Data Types Conversions, Validations

Phase 2: QUERY DESIGN
  • Joining Tables for Reports
  • Views with JOIN Options
  • Implementing Indexed Views
  • Using PIVOT Tables in Queries
  • Using Functions for Queries
  • Dynamic Conditions in Queries
  • Parameterized Queries in T-SQL

Phase 3: PROGRAMMING
  • Event Handling , Error Handling
  • Stored Procedures with Transactions
  • Error Handling, Event Handling Options
  • Transaction Nesting, Save Points
  • Stored Procedures with Tables
  • Stored Procedures with Views
  • Stored Procedures with Functions
  • Automating DML with Triggers
  • Project Deployments, Project FAQ

   Project Solution Explanation
   Resume Points from the Project
   Interview FAQs from Project

DAY 6 : CONSTRAINTS, INDEXES - BASICS

  • Constraints and Keys - Data Integrity
  • NULL, NOT NULL Property on Tables
  • UNIQUE KEY Constraints: Importance
  • PRIMARY KEY Constraint: Importance
  • FOREIGN KEY Constraint: Importance
  • REFERENCES, CHECK and DEFAULT
  • Candidate Keys and Identity Property
  • Database Diagrams and ER Models
  • Relationships Verification and Links
  • Indexes : Basic Types and Creation
  • Index Sort Options, Search Advantages
  • Clustered and NonClustered Indexes
  • Primary Key and Unique Key Indexes
  • Need for Indexes with working with Keys

DAY 12 : ER MODELS, NORMAL FORMS

  • Normal Forms for Entity Relationships
  • First, Second, Third Normal Forms Usage
  • Boycee-Codd Normal Form : BNCF : Usage
  • 4 NF, EKNF, ETNF. Functional Dependency
  • Multi-Valued, Transitive Dependencies
  • Composite Keys and Composite Indexes
  • 1:1, 1:M, M:1, M:M Relationship Types
  • Self Referencing Keys and Self Joins
  • Adding NOT NULL Property to Columns
  • Adding Primary Key to Existing Tables
  • Adding Foreign Key to Existing Tables
  • Synonyms : Creation and Real-time Use
  • Using Synonyms in Self Join Queries
  • Cascading Keys. UPDATE/DELETE Types
Real-time Case Study - 1 (Sales & Retail)
Objective : DB Design, Table Design, Relations
Involves Purchases, Products, Customers
and Time Data with Various Data Types.
Solution Explanation in Day 13
DAY 13: Real-time Case Study - 2 (Sales & Retail)
Objective : Query Writing, Excel Integration
Writing Queries, Generate Excel Pivot Tables
Excel Pivot Charts, Data Formatting,
ODC Connections, Charts, Data Labelling.

Chapter 1 : INTRODUCTION TO SCRIPT

What is Script in Python?; What is a program in Python?; Types of Scripts in Python?; Difference between Script; programming languages list; main features of scripting Lang.; limitation of client side scripting; Programming Language Paradigms; Basic understanding of Python; Is Python a compiled language?; where is python used in real life?; Why is Python called Python?

Chapter 2 : INTRODUCTION TO PYTHON

What is Python Programming?; Why Python is used in DS?; Where is python Mostly used?; Characteristics of Python Programming; History of Python Programming Language; What is PSF Python Programming?; Python Versions - Python Application; How to Download Python,print to the screen; How to Install Python , Creating Program; Install Python with Diff IDEs; Features of Python Programming; Limitations of Python Programming

Chapter 3: Data Analytics

Introduction to data Big Data?; Introduction to NumPY and SciPY; Introduction to Pandas and MatPlotLib;

Data Science

What is Data Science in Python; Data Science Life Cycle in python; what is data analysis using python; what is Data Mining in Python; Analytics vs Data Science in python; How Python is used in big data?; Is Python or R better for data science?; Why is Python used in data science?

Chapter 4 : String Handling 

what is String ? - String Operations - String indices; String Functions - len , upper, lower,join,Split; SwapCase(), Title(),find(),isupper(),islower(); Delete a string - Python Keywords; String Multiplication and concatenation; Python Identifiers - Python Literals; string formatting operator in python; Built-in String Methods - Data Structures; Structuring with indentation in Python; Define Data Structure in Python Language; Reverse words - Reverse Characters Examples; How do you split a string in Python?

Chapter 5 : Python Conditional

Control Structures - Sequential Control Structure; Selective and Repetative Control Structure; How to use “if condition” in conditional; control Structures in python; if statement (One-Way Decisions); if .. else statement (Two-way Decisions); How to use “else condition”; if .. elif .. else statement (Multi-way); When “else condition” does not work; How to use “elif” condition; How to execute conditional statement with; minimal code - Nested IF Statement; Nested IF Statement in python

Chapter 6: Python LOOPS

How to use While loop and For loop; Break and Continue Statements in For loop; Python Enumerate function for For Loop

 Sequence or Collections and Lists

Strings - Unicode Strings; Lists - Tuples - Sets - Dictionary – Xrange; Lists are mutable - Accessing the List; Updating a List - Deleting a List; List indices - Traversing a list; List operations - List Slices - List Methods; Map, filter and reduce - Deleting elements; What is list of list in python?; What is Python list function?; How do you add to a list in Python?

Chapter 7 : Python TUPLE

Advantages of Tuple over List; Packing and Unpacking – Tuples; Creating Nested tuple –Examples; Deleting Tuples - Slicing of Tuples; Comparing Tuple Membership Test; Built in Functions ,Dotted Charts

 Python Sets

how to create/declare a set in python; Iteration Over Sets - Python Methods; Python Set Operations - Union of Sets; Built-in Functions with Set; python frozenset get element

Chapter 8 : Python Dictionary

How to create a dictionary?; PYTHON HASHING - Dictinary Methods; Copying dictionary - Updating Dictionary; Delete Keys from the dictionary; Sorting the Dictionary - Dictionary len(); Python Dictionary in-built Functions; Variable Types - python List Cmp(); Python List cmp() Method; Python Dictionary Str(dict); How do you create a dictionary in Python?; Can Python dictionary have multiple values?; How do you add to a dictionary in python?

Chapter 9 : Python Functions

What is a function? - Types of Function; How to define and call a function in Python; Significance of Indentation (Space) in Python; How Function Return Value?; Types of Arguments in Functions; Default Arguments - Non Default Arg.; Keyword Arguments - Non Keyword Arg.; Arbitrary Arguments in python; Various Forms of Function Arguments; Scope and Lifetime of variables - Nested Fun; Call By Value, Call by Reference in python; Anonymous Functions/Lambda functions;

Chapter 10 : Python Modules

What is a Module? - Types of Modules; The import Statement - The from… import st; ..import * Statement - Underscores in python; The Dir() Function in python; Creating User defined Modules; Command line Arguments in python; Getting Python Module Search Path; What are modules and packages in Python?; What is Python import statement?; How do you import random in Python?; import <module_name> string python; from <module_name> import <name(s)>; from <module> import <name> as <name>

Chapter 11 : Packages in Python

What is a Package in Python?; Introduction to Packages?; py file - Creating a package; Importing module from a package; Creating Sub Package in Python; Importing from Sub-Packages; Most Popular Python Packages; How many libraries are there in Python?; What are libraries in Python?; What is the difference between NumPy&SciPy?; Why is SciPy and NumPy used?; Python what is Seaborn? – Examples; Is NumPy a Python framework?

Chapter 12 : Python Date and Time

How to Use Date &DateTime Class; How to Format Time Output; How to use Timedelta Objects; Calendar in Python; datetime classes in Python; How to Format Time Output?; Python Calendar Module,Time Module; Python Text Calendar; Python HTML Calendar Class; Unix Date and Time Commands; Python strftime(); How strftime() works?

Chapter 13 : Machine Learning Basics

What Converting business problems to data problems; Understanding supervised and unsupervised learning with examples; Understanding biases associated with any machine learning algorithm; Ways of reducing bias and increasing generalization capabilities; Drivers of machine learning algorithms; Cost functions; Brief introduction to gradient descent; Importance of model validation; Methods of model validation; Cross validation & average error

Chapter 14 : Generalized Linear Models in Python

Linear Regression; Regularization of Generalized Linear Models; Ridge and Lasso Regression; Logistic Regression; Methods of threshold determination and; performance measures for classification score models;

Tree Models using Python

Introduction to decision trees; Tuning tree size with cross validation; Introduction to bagging algorithm; Random Forests; Grid search and randomized grid search; Extra Trees (Extremely Randomized Trees); Partial dependence plots

Chapter 15 : Boosting Algorithms using Python

Concept of weak learners; Introduction to boosting algorithms; Adaptive Boosting; Extreme Gradient Boosting (XGBoost);

Support Vector Machines (SVM) &kNN in Python

Introduction to idea of observation based learning; Distances and similarities; k Nearest Neighbors (kNN) for classification; Brief mathematical background on SVM; Regression with kNN& SVM

Chapter 16 : Unsupervised learning in Python

Need for dimensionality reduction; Principal Component Analysis (PCA); Difference between PCAs and Latent Factors; Factor Analysis; Hierarchical, K-means & DBSCAN Clustering;

Artificial Neural Networks in Python

Introduction to Neural Networks; Single layer neural network; Multiple layer Neural network; Back propagation Algorithm; Neural Networks Implementation in Python;

Chapter 17 : Text Mining in Python

Gathering text data using web scraping with urllib; Processing raw web data with Beautiful Soup; Interacting with Google search using urllib with custom user agent; Collecting twitter data with Twitter API; Naive Bayes Algorithm; Feature Engineering with text data; Sentiment analysis;

Chapter 1:Essential to R programming

Introduction to the R language; Programming statistical graphics; Programming with R; Simulation; Computational linear algebra; Numerical optimization

Chapter 2:Data Manipulation Techniques using R programming

Data in R; Reading and Writing Data; R and Databases; Dates, Factors, Subscribing; Character Manipulation; Data Aggregation, Reshaping Data

Chapter 3:Statistical Applications using R programming

The R Environment; Probability and distributions; Descriptive statistics and graphics; One- and two-sample tests; Regression and correlation; Analysis of variance and the Kruskal–Wallis test; Tabular data; Power and the computation of sample size; Advanced data handling; Multiple Regression; Linear models; Logistic regression; Survival analysis; Rates and Poisson regression; Nonlinear curve fitting

Chapter 4:Data Science with R

Introduction; About S, About R, About CRAN; Installation of R; About working directory; Changing working directory temporarily; Changing working directory Permanently; Installation of R studio; Atomic Datatypes in R

Chapter 5:Vectors

Creating vector; Naming Vector; Vector selections; Adding elements to vector; Update elements of vector; Delete elements of vector; Functions (c(), names() ...etc);

Matrices

What is matrix; Create matrix; Naming a matrix; Arithmetic with matrix; Adding row; Adding column; Selection of matrix elements; Insert /delete/update matrix elements; Transpose matrix; Combine rows of matrix; Combine columns of matrix

Chapter 6:Factors

Categorical variables; Continuous variables; What is factor; Factors in Data Frame; Factor Levels in customized format; Nominal factors; Ordinal factors; compoments of a factor; How to modify a factor?; Updating Factors; methods for handling factors

Chapter 7: Functions

Introduction to functions; Function documentation; Use a function; Create own function; Nested functions; Function scoping

Chapter 8: Packages

Available R Packages; Packages installation; Default packages; Create package; Attach package...etc; Load Package to Library

Chapter 9: Graphics systems in R

Base graphics, Plot; Histogram, Scatter; Bar plot, Qqplot; Sunflowerplot, Boxplot; Add more detail to graphs; Grid graphics; Lattice graphics; ggplot2 graphics; Data layer; Aesthetics layer; Geometries layer; Facets layer; Statistics layer; Coordinates layer; Themes layer

Chapter 10: Cleaning data( equal to ETL work)

gather function; spread() function; separate() function; unite() function; Working with lubridate package; Working with stringr package; Working with Missing values; Working with Special values

Chapter 11: Machine Learning & Artificial Intelligence

What is machine learning?; What is ETP?; Types of machine learning; Supervised learning; Unsupervised learning; Semi-supervised Learning; Reinforcement learning; Algorithms or Model for Machine Learning; Linear Regression, Logistic Regression; Jackknife Regression; Density Estimation, Confidence Interval; Test of Hypotheses; Pattern Recognition; Supervised Learning; Time Series & Decision Trees; Random Numbers; Monte-Carlo Simulation; Bayesian Statistics; Naive Bayes, Principal Component

Chapter 12: Analysis - (PCA)

Ensembles, Neural Networks; Support Vector Machine - (SVM); Nearest Neighbours - (k- NN); Feature Selection - (aka Variable Reduction); Indexation / Cataloguing and Collaborative Filtering; (Geo-) Spatial Modelling, Graphs; Recommendation Engine; Search Engine and Attribution Modeling; Rule System,Linkage Analysis; Association Rules,Scoring Engine; Segmentation, Predictive Modelling

Chapter 13: Deep Learning

Reinforced Learning, Reinforcement learning Process Flow, Reinforced Learning Use cases, Deep Learning, Biological Neural Networks, Understand Artificial Neural Networks, Building an Artificial Neural Network, How ANN works, Important Terminologies of ANN’s

Chapter 14: Text Mining :  The concepts of text-mining

Use cases; Text Mining Algorithms; Quantifying text; TF-IDF; Beyond TF-IDF

 

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