- 2 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
Hands-on Labs: The certification offers practical labs for a real-world understanding of Azure machine learning tools and techniques.
Industry-Relevant Curriculum: Aligned with industry requirements, the course ensures participants are prepared for challenges in the field.
Expert Instructors: Learn from experienced instructors with in-depth knowledge of Azure machine learning and practical applications.
Certification Exam: Completing the course qualifies participants to take the official Azure Machine Learning Certification exam.
Data Scientists
Machine Learning Engineers
IT Professionals
Software Developers
Business Intelligence Professionals
Basic programming knowledge (preferably in Python or R)
Understanding of fundamental machine learning concepts
Familiarity with cloud computing concepts (Azure platform knowledge is a plus)
Mastering Azure Machine Learning Tools: Gain proficiency in using Azure's machine learning tools and services.
Implementing Machine Learning Models: Learn to build, train, and deploy machine learning models on the Azure platform.
Real-world Applications: Apply machine learning concepts to real-world scenarios and business problems.
Azure Services Integration: Understand how Azure machine learning integrates with other Azure services for comprehensive solutions.
Salaries may vary based on the region or country. For instance, technology hubs often offer higher compensation.
Different industries value machine learning skills differently. Sectors like finance, healthcare, and technology tend to offer lucrative packages.
Entry-level professionals may start with a competitive base salary, while those with advanced experience and expertise can command higher pay.
The complexity and responsibility of the roles undertaken influence salary levels. Senior or specialized roles generally come with higher compensation.
Holding an Azure Machine Learning Certification validates skills and can positively impact salary negotiations.
Professionals who pursue continuous learning, specialize in niche areas, or acquire additional certifications may command higher salaries.
Transition into roles focused on designing and implementing machine learning models, algorithms, and solutions.
Apply machine learning techniques to analyze and interpret complex datasets, deriving valuable insights for decision-making.
Dive into artificial intelligence development, creating solutions that emulate human-like intelligence and enhance automation.
Advance to roles involving the design and implementation of scalable, secure, and efficient cloud-based machine learning solutions.
Specialize in leveraging machine learning for business intelligence, providing actionable insights to enhance organizational strategies.
Explore specialized roles in areas such as natural language processing, computer vision, or reinforcement learning for further expertise.
Progress into leadership or management roles, overseeing machine learning teams and strategies.
Pursue entrepreneurial endeavors by applying machine learning skills to develop innovative products or services.
Industry Demand: With the increasing adoption of Azure in various industries, the demand for certified professionals is on the rise.
Comprehensive Curriculum: The certification covers a broad spectrum of machine learning topics, making participants well-rounded professionals.
Practical Applications: Emphasis on hands-on labs and real-world scenarios ensures participants can apply their knowledge effectively.
Develop and design machine learning models tailored to meet specific business requirements.
Utilize Azure Machine Learning services to create effective and efficient models.
Implement machine learning algorithms and optimize them for improved performance.
Ensure the seamless integration of machine learning solutions within Azure environments.
Analyze large datasets using Azure tools to extract meaningful insights.
Interpret data trends and patterns to facilitate informed decision-making.
Monitor machine learning models in real-time to assess their performance.
Implement improvements and optimizations to enhance model accuracy over time.
Collaborate with data engineers and scientists to streamline data pipelines.
Work in tandem to ensure the smooth flow of data for effective machine learning processes.
Implement predictive analytics using Azure Machine Learning capabilities.
Integrate predictive models into existing business processes for actionable insights.
Implement security measures to protect machine learning models and data.
Ensure compliance with data privacy regulations and industry standards.
Effectively communicate complex results and insights to non-technical stakeholders.
Use data visualization tools within the Azure environment for clear communication.
Identify and address issues related to machine learning models promptly.
Troubleshoot challenges that may arise during the deployment and execution of models.
Stay abreast of the latest advancements in Azure Machine Learning.
Continuously update skills and knowledge to leverage new features and functionalities.
Collaborate with cross-functional teams to align machine learning initiatives with overall business goals.
Participate in discussions and contribute insights to enhance data-driven decision-making.
Technology and IT: Demand for Azure machine learning professionals in tech companies for innovative solutions.
Healthcare: Utilization of Azure machine learning for improved patient outcomes and operational efficiency.
Finance: Application of Azure machine learning in financial institutions for risk management and fraud detection.
E-commerce: Leveraging Azure machine learning to enhance customer experience and optimize business operations.
Manufacturing: Implementation of Azure machine learning for efficiency and quality improvement in manufacturing.
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02-Nov-2024*
04-Nov-2024*
30-Oct-2024*
02-Nov-2024*
04-Nov-2024*
30-Oct-2024*
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Python Training Curriculum
Data Analysis and Visualization using NumPy, Pandas, and MatPlotLib,Seaborn
Introduction To Python
Python Keyword and Identifiers
Introduction To Variables:
Python Data Type:
Control Structure & Flow
Python Function, Modules and Packages
Python Date Time and Calendar:
List
Tuple
Dictionary
Sets
Strings
Python Exception Handling
Python File Handling
Python Database Interaction
Contacting user Through Emails Using Python
Reading an excel
Complete Understanding of OS Module of Python
NumPy
Pandas
MatPlotLib
Introduction to Seaborn
Python Object Oriented Programming—Oops Concepts
HTML
HTML 5
CSS 2.0
CSS 3.0
JavaScript
JQuery
Bootstrap Framework Latest Version (HTML, CSS, and JS Library)
Web Hosting & SEO Basics
Python Training Curriculum
Data Analysis and Visualization using NumPy, Pandas, and MatPlotLib, Seaborn
Placement Guide
What is HTML
What is a Web Browser
What are Versions of HTML
What can you Do with HTML
HTML Development Environments
Writing Code with a Text Editor
Rules of Syntax
Making your Code Readable
Building a Document
Using Colors
Adding Color to your Page
Using Headings
Using Paragraphs
Aligning Block-Level Elements
Displaying Preformatted Text
Formatting with Inline Elements
Controlling Fonts
Introducing List Elements
Creating Unordered Lists
Creating Ordered Lists
Nesting Lists
Building a Table
Cell Padding and Cell Spacing
Controlling Table and Cell Width
Aligning a Table on the Page
Aligning Tables and Text
Aligning Table Data
Spanning Columns and Rows
Understanding and Using URLs
Linking to a Web Document
Linking to a Local Document
Linking to Anchors
Opening a New Browser Window
Inserting Inline Images
Aligning Images
Using Images to Anchor Links
Sizing Images
Using Transparent Images
Using GIF Animation
Forms and Form Elements
Form Actions, Form Methods, Form Design
Laying out a page with HTML5
Page Structure
New HTML5 Strutural Tags
Page Simplification
New Features of HTML5
The HTML5 Semantic Element
Current State of Browser Support
The section Tag
The article Tag
The header Tag
The Footer Tag
Supported Media Types
The audio Element
The video Element
New Input Types
autocomplete
novalidate
required
placeholder
autofocus
autocomplete
form
pattern
Inline
Internal
External
ID
Class
Attribute
Grouping
Universal
RGB Value
Hex Value
Color Name
background-color
background-repeat
background-attachement
background position
background-size
background-image
Margin-top
Margin-bottom
Margin-left
Margin-Right
Padding -top
Padding -bottom
Padding -left
Padding –Right
Outline-Style
Outline-color
Outline Width
Outline-Offset
Outline Shorthand Property
Border
border-radius
Text-shadow
Box-shadow
transition
transition - delay
transition - duration
transition - property
transform
matrix ()
translate (x,y)
scale(x,y)
rotate(angle)
Skew (x - angle, y-angle)
@keyframes
animation
animation-direction
animation-duration
animation-name
CSS combinations
Pseudo Elements
Linear Gradients
Radial Gradients
resize
box-sizing
outline-offset
Blur
Opacity
What is Responsive Web Design
Intro to the Viewport
The Viewport Tag
Media Queries
Tablet Styles
Mobile Styles
Making a Mobile Drop-down Menu
@font-face
font- family
src
font-stretch
font-Style
font-weight
flex - grow
flex - shrink
flex - basis
flex
flex - wrap
flex - direction
flex - flow
justify - content
align-items
order
Django Web Framework
Getting Started with Django
Create an Application
Django - URL Mapping
Django Template Language (DTL)
Django – Models
Django – Sending E-mails
Django – Form Processing/le handling/cooking handling
Django Admin
Django API (Application Program Interface)
Static les
Placement Guide
What is a Framework
Introduction to Django
Django – Design Philosophies
History of Django
Why Django and Features
Environment setup
Web Server
MVC Pattern
MVC Architecture vs MVT Architecture
Django MVC – MVT Pattern
Creating the rst Project
Integrating the Project to sublime text
The Project Structure
Running the server
Solving the issues and Migrations
Database Setup
Setting Up Your Project.
What Django Follows
Structure of Django framework
Model Layer
What are models
Model elds
Query sets
Django – Admin Interface
Starting the Admin Interface
Migrations
Views Layer
Simple View
Basic view (displaying hello world)
Functional views, class based views
Organizing Your URLs
Role of URLs in Django
Working URLs
Forms
Sending Parameters to Views
Templates layer
The Render Function
Python Training
Data Analysis and Visualization using Pandas.
Data Analysis and Visualization using NumPy and MatPlotLib
Introduction to Data Visualization with Seaborn
Installation and Working with Python
Understanding Python variables
Python basic Operators
Understanding the Python blocks.
Python Keyword and Identiers
Python Comments, Multiline Comments.
Python Indentation
Understating the concepts of Operators
Variables, expression condition and function
Global and Local Variables in Python
Packing and Unpacking Arguments
Type Casting in Python
Byte objects vs. string in Python
Variable Scope
Declaring and using Numeric data types
Using string data type and string operations
Understanding Non-numeric data types
Understanding the concept of Casting and Boolean.
Strings
List
Tuples
Dictionary
Sets
Statements – if, else, elif
How to use nested IF and Else in Python
Loops
Loops and Control Statements.
Jumping Statements – Break, Continue, pass
Looping techniques in Python
How to use Range function in Loop
Programs for printing Patterns in Python
How to use if and else with Loop
Use of Switch Function in Loop
Elegant way of Python Iteration
Generator in Python
How to use nested Loop in Python
Use If and Else in for and While Loop
Examples of Looping with Break and Continue Statement
How to use IN or NOT IN keyword in Python Loop.
Python Syntax
Function Call
Return Statement
Arguments in a function – Required, Default, Positional, Variable-length
Write an Empty Function in Python –pass statement.
Lamda/ Anonymous Function
*args and **kwargs
Help function in Python
Scope and Life Time of Variable in Python Function
Nested Loop in Python Function
Recursive Function and Its Advantage and Disadvantage
Organizing python codes using functions
Organizing python projects into modules
Importing own module as well as external modules
Understanding Packages
Random functions in python
Programming using functions, modules & external packages
Map, Filter and Reduce function with Lambda Function
More example of Python Function
Day, Month, Year, Today, Weekday
IsoWeek day
Date Time
Time, Hour, Minute, Sec, Microsec
Time Delta and UTC
StrfTime, Now
Time stamp and Date Format
Month Calendar
Itermonthdates
Lots of Example on Python Calendar
Create 12-month Calendar
Strftime
Strptime
Format Code list of Data, Time and Cal
Locale’s appropriate date and time
What is List.
List Creation
List Length
List Append
List Insert
List Remove
List Append & Extend using “+” and Keyword
List Delete
List related Keyword in Python
List Revers
List Sorting
List having Multiple Reference
String Split to create a List
List Indexing
List Slicing
List count and Looping
List Comprehension and Nested Comprehension
What is Tuple
Tuple Creation
Accessing Elements in Tuple
Changing a Tuple
Tuple Deletion
Tuple Count
Tuple Index
Tuple Membership
TupleBuilt in Function (Length, Sort)
Dict Creation
Dict Access (Accessing Dict Values)
Dict Get Method
Dict Add or Modify Elements
Dict Copy
Dict From Keys.
Dict Items
Dict Keys (Updating, Removing and Iterating)
Dict Values
Dict Comprehension
Default Dictionaries
Ordered Dictionaries
Looping Dictionaries
Dict useful methods (Pop, Pop Item, Str , Update etc.)
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
Frozen Sets
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
Python Errors and Built-in-Exceptions
Exception handing Try, Except and Finally
Catching Exceptions in Python
Catching Specic Exception in Python
Raising Exception
Try and Finally
Opening a File
Python File Modes
Closing File
Writing to a File
Reading from a File
Renaming and Deleting Files in Python
Python Directory and File Management
List Directories and Files
Making New Directory
Changing Directory
SQL Database connection using
Creating and searching tables
Reading and Storing cong information on database
Programming using database connections
Installing SMTP Python Module
Sending Email
Reading from le and sending emails to all users
Working With Excel
Reading an excel le using Python
Writing to an excel sheet using Python
Python| Reading an excel le
Python | Writing an excel le
Adjusting Rows and Column using Python
ArithmeticOperation in Excel le.
Play with Workbook, Sheets and Cells in Excel using Python
Creating and Removing Sheets
Formatting the Excel File Data
More example of Python Function
Check Dirs. (exist or not)
How to split path and extension
How to get user prole detail
Get the path of Desktop, Documents, Downloads etc.
Handle the File System Organization using OS
How to get any les and folder’s details using OS
Categorical Data
Numerical Data
Mean
Median
Mode
Outliers
Range
Interquartile range
Correlation
Standard Deviation
Variance
Box plot
Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
Using the Excel File class to read multiple sheets More Mapping, Filling
Nonvalue’s
Exploring the Data Plotting, Correlations, and Histograms
Getting statistical information about the data Analysis Concepts, Handle the None Values
Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
Applying formulas on the columns Basic Grouping, Concepts of Aggre
gate Function
Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
Under sting the Properties of Pivot Table in Pandas Advanced Reading
CSVs/HTML, Binning, Categorical Data
Exporting the results to Excel Joins:
Python | Pandas Data Frame Inner Join
Under sting the properties of Data Frame Left Join (Left Outer Join)
Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
Pandas | Find Missing Data and Fill and Drop NA Appending DataFrame and Data
Pandas | How to Group Data How to apply Lambda / Function on Data
Frame
Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
Introduction to NumPy: Numerical Python
Importing NumPy and Its Properties
NumPy Arrays
Creating an Array from a CSV
Operations an Array from a CSV
Operations with NumPy Arrays
Two-Dimensional Array
Selecting Elements from 1-D Array
Selecting Elements from 2-D Array
Logical Operation with Arrays
Indexing NumPy elements using conditionals
NumPy’s Mean and Axis
NumPy’s Mode, Median and Sum Function
NumPy’s Sort Function and More
Bar Chart using Python MatPlotLib
Column Chart using Python MatPlotLib
Pie Chart using Python MatPlotLib
Area Chart using Python MatPlotLib
Scatter Plot Chart using Python MatPlotLib
Play with Charts Properties Using MatPlotLib
Export the Chart as Image
Understanding plt. subplots () notation
Legend Alignment of Chart using MatPlotLib
Create Charts as Image
Other Useful Properties of Charts.
Complete Understanding of Histograms
Plotting Different Charts, Labels, and Labels Alignment etc.
Introduction to Seaborn
Making a scatter plot with lists
Making a count plot with a list
Using Pandas with seaborn
Tidy vs Untidy data
Making a count plot with a Dataframe
Adding a third variable with hue
Hue and scattera plots
Hue and count plots
Introduction to relational plots and subplots
Creating subplots with col and row
Customizing scatters plots
Changing the size of scatter plot points
Changing the style of scatter plot points
Introduction to line plots
Interpreting line plots
Visualizing standard deviation with line plots
Plotting subgroups in line plots
Current plots and bar plots
Count plots
Bar plot with percentages
Customizing bar plots
Box plots
Create and interpret a box plot
Omitting outliers
Adjusting the whisk
Point plots
Customizing points plots
Point plot with subgroups
Changing plot style and colour
Changing style and palette
Changing the scale
Using a custom palette
Adding titles and labels: Part 1
Face Grids vs. Axes Subplots
Adding a title to a face Grid object
Adding title and labels: Part 2
Adding a title and axis labels
Rotating x-tics labels
Putting it all together
Box plot with subgroups
Bar plot with subgroups and subplots
Well done! What’s next
Python Training Curriculum
Data Analysis and Visualization using Pandas.
Data Analysis and Visualization using NumPy and MatPlotLib
Introduction to Data Visualization with Seaborn
Machine Learning
Installation and Working with Python
Understanding Python variables
Python basic Operators
Understanding the Python blocks.
Python Keyword and Identiers
Python Comments, Multiline Comments.
Python Indentation
Understating the concepts of Operators
Variables, expression condition and function
Global and Local Variables in Python
Packing and Unpacking Arguments
Type Casting in Python
Byte objects vs. string in Python
Variable Scope
Declaring and using Numeric data types
Using string data type and string operations
Understanding Non-numeric data types
Understanding the concept of Casting and Boolean.
Statements – if, else, elif
How to use nested IF and Else in Python
Loops
Loops and Control Statements.
Jumping Statements – Break, Continue, pass
Looping techniques in Python
How to use Range function in Loop
Programs for printing Patterns in Python
How to use if and else with Loop
Use of Switch Function in Loop
Elegant way of Python Iteration
Generator in Python
How to use nested Loop in Python
Use If and Else in for and While Loop
Examples of Looping with Break and Continue Statement
How to use IN or NOT IN keyword in Python Loop.
Python Syntax
Function Call
Return Statement
Arguments in a function – Required, Default, Positional, Variable-length
Write an Empty Function in Python –pass statement.
Lamda/ Anonymous Function
*args and **kwargs
Help function in Python
Scope and Life Time of Variable in Python Function
Nested Loop in Python Function
Recursive Function and Its Advantage and Disadvantage
Organizing python codes using functions
Organizing python projects into modules
Importing own module as well as external modules
Understanding Packages
Random functions in python
Programming using functions, modules & external packages
Map, Filter and Reduce function with Lambda Function
More example of Python Function
Day, Month, Year, Today, Weekday
IsoWeek day
Date Time
Time, Hour, Minute, Sec, Microsec
Time Delta and UTC
StrfTime, Now
Time stamp and Date Format
Month Calendar
Itermonthdates
Lots of Example on Python Calendar
Create 12-month Calendar
Strftime
Strptime
Format Code list of Data, Time and Cal
Locale’s appropriate date and time
What is List.
List Creation
List Length
List Append
List Insert
List Remove
List Append & Extend using “+” and Keyword
List Delete
List related Keyword in Python
List Revers
List Sorting
List having Multiple Reference
String Split to create a List
List Indexing
List Slicing
List count and Looping
List Comprehension and Nested Comprehension
What is Tuple
Tuple Creation
Accessing Elements in Tuple
Changing a Tuple
Tuple Deletion
Tuple Count
Tuple Index
Tuple Membership
TupleBuilt in Function (Length, Sort)
Dict Creation
Dict Access (Accessing Dict Values)
Dict Get Method
Dict Add or Modify Elements
Dict Copy
Dict From Keys.
Dict Items
Dict Keys (Updating, Removing and Iterating)
Dict Values
Dict Comprehension
Default Dictionaries
Ordered Dictionaries
Looping Dictionaries
Dict useful methods (Pop, Pop Item, Str , Update etc.)
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
Frozen Sets
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
Python Errors and Built-in-Exceptions
Exception handing Try, Except and Finally
Catching Exceptions in Python
Catching Specic Exception in Python
Raising Exception
Try and Finally
Opening a File
Python File Modes
Closing File
Writing to a File
Reading from a File
Renaming and Deleting Files in Python
Python Directory and File Management
List Directories and Files
Making New Directory
Changing Directory
SQL Database connection using
Creating and searching tables
Reading and Storing cong information on database
Programming using database connections
Installing SMTP Python Module
Sending Email
Reading from le and sending emails to all users
Working With Excel
Reading an excel le using Python
Writing to an excel sheet using Python
Python| Reading an excel le
Python | Writing an excel le
Adjusting Rows and Column using Python
ArithmeticOperation in Excel le.
Play with Workbook, Sheets and Cells in Excel using Python
Creating and Removing Sheets
Formatting the Excel File Data
More example of Python Function
Check Dirs. (exist or not)
How to split path and extension
How to get user prole detail
Get the path of Desktop, Documents, Downloads etc.
Handle the File System Organization using OS
How to get any les and folder’s details using OS
Categorical Data
Numerical Data
Mean
Median
Mode
Outliers
Range
Interquartile range
Correlation
Standard Deviation
Variance
Box plot
Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
Using the Excel File class to read multiple sheets More Mapping, Filling
Nonvalue’s
Exploring the Data Plotting, Correlations, and Histograms
Getting statistical information about the data Analysis Concepts, Handle the None Values
Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
Applying formulas on the columns Basic Grouping, Concepts of Aggre
gate Function
Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
Under sting the Properties of Pivot Table in Pandas Advanced Reading
CSVs/HTML, Binning, Categorical Data
Exporting the results to Excel Joins:
Python | Pandas Data Frame Inner Join
Under sting the properties of Data Frame Left Join (Left Outer Join)
Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
Pandas | Find Missing Data and Fill and Drop NA Appending DataFrame and Data
Pandas | How to Group Data How to apply Lambda / Function on Data
Frame
Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
Introduction to NumPy: Numerical Python
Importing NumPy and Its Properties
NumPy Arrays
Creating an Array from a CSV
Operations an Array from a CSV
Operations with NumPy Arrays
Two-Dimensional Array
Selecting Elements from 1-D Array
Selecting Elements from 2-D Array
Logical Operation with Arrays
Indexing NumPy elements using conditionals
NumPy’s Mean and Axis
NumPy’s Mode, Median and Sum Function
NumPy’s Sort Function and More
Bar Chart using Python MatPlotLib
Column Chart using Python MatPlotLib
Pie Chart using Python MatPlotLib
Area Chart using Python MatPlotLib
Scatter Plot Chart using Python MatPlotLib
Play with Charts Properties Using MatPlotLib
Export the Chart as Image
Understanding plt. subplots () notation
Legend Alignment of Chart using MatPlotLib
Create Charts as Image
Other Useful Properties of Charts.
Complete Understanding of Histograms
Plotting Different Charts, Labels, and Labels Alignment etc.
Introduction to Seaborn
Making a scatter plot with lists
Making a count plot with a list
Using Pandas with seaborn
Tidy vs Untidy data
Making a count plot with a Dataframe
Adding a third variable with hue
Hue and scattera plots
Hue and count plots
Introduction to relational plots and subplots
Creating subplots with col and row
Customizing scatters plots
Changing the size of scatter plot points
Changing the style of scatter plot points
Introduction to line plots
Interpreting line plots
Visualizing standard deviation with line plots
Plotting subgroups in line plots
Current plots and bar plots
Count plots
Bar plot with percentages
Customizing bar plots
Box plots
Create and interpret a box plot
Omitting outliers
Adjusting the whisk
Point plots
Customizing points plots
Point plot with subgroups
Changing plot style and colour
Changing style and palette
Changing the scale
Using a custom palette
Adding titles and labels: Part 1
Face Grids vs. Axes Subplots
Adding a title to a face Grid object
Adding title and labels: Part 2
Adding a title and axis labels
Rotating x-tics labels
Putting it all together
Box plot with subgroups
Bar plot with subgroups and subplots
Well done! What’s next
Introduction to Machine Learning
Techniques of Machine Learning
Regression
Classification
Unsupervised Learning: Clustering
Distribution Clustering
Articial Intelligence
Machine Learning
Machine Learning Algorithms
Algorithmic models of Learning
Applications of Machine Learning
Large Scale Machine Learning
Computational Learning theory
Reinforcement Learning
Supervised Learning
Unsupervised Learning
Semi-supervised and Reinforcement Learning
Bias and variance Trade-off
Representation Learning
Regression and its Types
Logistic Regression
Linear Regression
Polynomial Regression
Meaning and Types of Classification
Nearest Neighbor Classifiers
K-nearest Neighbors
Probability and Bayes Theorem
Support Vector Machines
Naive Bayes
Decision Tree Classifier
Random Forest Classifier
Introduction to Deep Learning
Deep Learning Networks
Deep Learning with Keras
Convolutional Neural Networks (CNN)
Recurrent Neural Network (RNN)
Natural Language Processing
Overview of Tensor Flow
Neural Networks Using Tensor Flow
What are the Limitations of Machine Learning
What is Deep Learning
Advantage of Deep Learning over Machine learning
Reasons to go for Deep Learning
Real-Life use cases of Deep Learning
What is Deep Learning Networks
Why Deep Learning Networks
How Deep Learning Works
Feature Extraction
Working of Deep Network
Training using Backpropagation
Variants of Gradient Descent
Types of Deep Networks
Feed forward neural networks (FNN)
Convolutional neural networks (CNN)
Recurrent Neural networks (RNN)
Restrict Boltzman Machine (RBM)
Dene Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predened Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Intuitively building networks with Keras
Introduction to Convolutional Neural Networks
CNN Applications
Architecture of a Convolutional Neural Network
Convolution and Pooling layers in a CNN
Understanding and Visualizing CNN
Transfer Learning and Fine-tuning Convolutional Neural Networks
In this program you will learn:
Introduction To Python
Python Keyword and Identiers
Introduction To Variables
Python Data Type
Control Structure & Flow
Python Function, Modules and Packages
Python Date Time and Calendar
List
Tuple
Dictionary
Sets
Strings
Python Exception Handling
Python File Handling
Python Database Interaction
Contacting user Through Emails Using Python
Reading an excel
Complete Understanding of OS Module of Python
Data Analysis and Visualization using Pandas.
Data Analysis and Visualization using NumPy and MatPlotLib
Introduction to Data Visualization with Seaborn
SQL Server Fundamentals
SQL Server 2019 Database Design
SQL Tables in MS SQL Server
Data Validation and Constraints
Views and Row Data Security
Indexes and Query tuning
Stored Procedures and Benets
System functions and Usage
Triggers, cursors, memory limitations
Cursors and Memory Limitations
Transactions Management
Introduction to Data Science
Accessing/Importing and Exporting Data
Data Manipulation Cleansing - Munging Using Python Modules
Feature Engineering in Data Science
Data Analysis Visualization Using Python
Introduction to Statistics
Introduction to Predictive Modelling
EDA (Exploratory Data Analysis)
Data Pre-Processing & Data Mining
Python Statistics for AI
Python - MySQL
Data Science Professional Program
Machine Learning
Live Projects
Installation and Working with Python
Understanding Python variables
Python basic Operators
Understanding the Python blocks.
Variables, expression condition and function
Global and Local Variables in Python
Packing and Unpacking Arguments
Type Casting in Python
Byte objects vs. string in Python
Variable Scope
Declaring and usingNumeric data types
Using stringdata type and string operations
Understanding Non-numeric data types
Understanding the concept of Casting and Boolean.
Strings
List
Tuples
Dictionary
Sets
Python Keyword and Identifiers
Python Comments, Multiline Comments.
Python Indentation
Understating the concepts of Operators
List
Dictionary
Sets
Tuple
Control Flow
Python Exception Handling
Python File Handling
Python Function, Modules and Packages
Python Object Oriented Programming—Oops
Python Database Interaction
Reading an excel
Working with PDF and MS Word using Python
Complete Understanding of OS Module of Python
Pandas
NumPy
MatPlotLib
Introduction to Seaborn
Visualizing Two Quantitative Variables
Visualizing a Categorical and a Quantitative Variable
Customizing Seaborn Plots
Introduction to MySQL
What is the MySQLdb
How do I Install MySQLdb
Connecting to the MYSQL
Selecting a database
Adding data to a table
Executing multiple queries
Exporting and Importing data tables.
SQL Functions
Count, Average, Sum, Now etc.
Obtaining data from Multiple Tables
Types of Joins (Inner Join, Left Join, Right Join & Full Join)
Sub-Queries Vs. Joins
Distinct, Order by, Group by, Equal to etc.
Not Null
Unique
Primary Key
Foreign Key
SQL Basic
SQL Advance
SQL Functions
What is Analytics & Data Science
Common Terms in Analytics
What is data
Classification of data
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem-solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates
Build Resource plan for analytics project
Why Python for data science
Importing Data from various sources (Csv, txt, excel, access etc)
Database Input (Connecting to database)
Viewing Data objects - sub setting, methods
Exporting Data to various formats
Important python modules: Pandas
Cleansing Data with Python
Filling missing values using lambda function and concept of Skewness.
Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
Normalizing data
Feature Engineering
Feature Selection
Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
Label encoding/one hot encoding
Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)
Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)
Descriptive Statistics
Sample vs Population Statistics
Random variables
Probability distribution functions
Expected value
Normal distribution
Gaussian distribution
Z-score
Central limit theorem
Spread and Dispersion
Inferential Statistics-Sampling
Hypothesis testing
Z-stats vs T-stats
Type 1 & Type 2 error
Confidence Interval
ANOVA Test
Chi Square Test
T-test 1-Tail 2-Tail Test
Correlation and Co-variance
Concept of model in analytics and how it is used
Common terminology used in Analytics & Modelling process
Popular Modelling algorithms
Types of Business problems - Mapping of Techniques
Different Phases of Predictive Modelling
Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outliers’ data
Imbalanced Data Techniques
Data Preparation
Feature Engineering
Feature Scaling
Datasets
Dimensionality Reduction
Anomaly Detection
Parameter Estimation
Data and Knowledge
Selected Applications in Data Mining
Artificial Intelligence
Machine Learning
Techniques of Machine Learning
Machine Learning Algorithms
Algorithmic models of Learning
Applications of Machine Learning
Large Scale Machine Learning
Computational Learning theory
Reinforcement Learning
Supervised Learning
Semi-supervised and Reinforcement Learning
Bias and variance Trade-off
Representation Learning
Regression and its Types
Logistic Regression
Linear Regression
Polynomial Regression
Meaning and Types of Classification
Nearest Neighbor Classifiers
K-nearest Neighbors
Probability and Bayes Theorem
Support Vector Machines
Naive Bayes
Decision Tree Classifier
Random Forest Classifier
About Clustering
Clustering Algorithms
K-means Clustering
Hierarchical Clustering
Distribution Clustering
Ensemble approach
K-fold cross validation
Grid search cross validation
Ada boost and XG Boost
What are the Limitations of Machine Learning
What is Deep Learning
Advantage of Deep Learning over Machine learning
Reasons to go for Deep Learning
Real-Life use cases of Deep Learning
What is Deep Learning Networks
Why Deep Learning Networks
How Deep Learning Works
Feature Extraction
Working of Deep Network
Training using Backpropagation
Variants of Gradient Descent
Types of Deep Networks
Feed forward neural networks (FNN)
Convolutional neural networks (CNN)
Recurrent Neural networks (RNN)
Generative Adversal Neural Networks (GAN)
Restrict Boltzman Machine (RBM)
Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Intuitively building networks with Keras
Introduction to Convolutional Neural Networks
CNN Applications
Architecture of a Convolutional Neural Network
Convolution and Pooling layers in a CNN
Understanding and Visualizing CNN
Transfer Learning and Fine-tuning Convolutional Neural Networks
Intro to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term Memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model
Time Series Forecasting
NLP with python
Bags of words
Stemming
Tokenization
Lemmatization
TF-IDF
Sentiment Analysis
Overview of Tensor Flow
Tensor Flow code-basics
Graph Visualization
Constants, Placeholders, Variables
Tensor flow Basic Operations
Linear Regression with Tensor Flow
Logistic Regression with Tensor Flow
K Nearest Neighbor algorithm with Tensor Flow
K-Means classifier with Tensor Flow
Random Forest classifier with Tensor Flow
Quick recap of Neural Networks
Activation Functions, hidden layers, hidden units
Illustrate & Training a Perceptron
Important Parameters of Perceptron
Understand limitations of A Single Layer Perceptron
Illustrate Multi-Layer Perceptron
Back-propagation – Learning Algorithm
Understand Back-propagation – Using Neural Network Example
TensorBoard
What is Big Data
Big Data Customer Scenarios
Understanding BIG Data: Summary
Few Examples of BIG Data
Why BIG data is a BUZZ
How Hadoop Solves the Big Data Problem
What is Hadoop
Hadoop’s Key Characteristics
Hadoop Cluster and its Architecture
Hadoop: Different Cluster Modes
Why Spark is needed
What is Spark
How Spark differs from other frameworks
Spark at Yahoo!
What is BIG data Analytics
Why BIG Data Analytics is a ‘need’ now
BIG Data: The Solution
Implementing BIG Data Analytics – Different Approaches
The Traditional Approach: Business Requirement Drives Solution Design
The BIG Data Approach: Information Sources drive Creative Discovery
Traditional and BIG Data Approaches
BIG Data Complements Traditional Enterprise Data Warehouse
Traditional Analytics Platform v/s BIG Data Analytics Platform
Scala
Spark
What is Kafka Why Kafka
Configuring Kafka Cluster
Kafka architecture
Producing and consuming messages
Operations, Kafka monitoring tool
Need of Apache Flume
What is Apache Flume
Understanding the architecture of Flume
Basic Flume Architecture
Internet of Things (IoT) Introduction
IoT Architecture
Understanding IoT Ecosystems
Raspberry Pi
IoT Gateways
Cloud Platforms for IoT
IoT Implementations [Applications]
Future & Security Concerns
Background and Development
Three waves of Internet
Why IoT
Market Analysis & Investment In IoT
Industrial & Consumer IoT
M2M communication and automation history
Relation with embedded systems
General introduction to Arduino , Raspberry Pi and SmartWifi boards
How IoT Works
High level Data Flow in IoT
Technical Architecture
Description of all layers of IoT Architecture
Technologies for IoT
What is IoT application
What are basic elements / building blocks of IoT app
How are these blocks connected together
The systematic method to design IoT application
Architecting our hands-on project
Learning fundamentals of Raspberry Pi
Different types of pi boards.
Installation of OS in Raspberry Pi
Programming Raspberry Pi Using Python
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