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- The Azure Machine Learning Certification equips individuals with skills for machine learning on Microsoft Azure. It covers various topics, offering hands-on experience and theoretical knowledge.
- Key Features:
- Who Can Enroll:
- The Azure Machine Learning Certification is suitable for:
- Prerequisites
- To enroll in the Azure Machine Learning Certification, participants should have:
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)
Azure Machine Learning Certification
- The primary objectives of the Azure Machine Learning Certification include:
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.
- Upon completing the Azure Machine Learning Certification, participants can expect competitive salaries in the machine learning and data science domain. Salary potential is influenced by factors such as experience, geographical location, and industry demand.
- Geographical Location:
- Industry Verticals:
- Experience Level:
- Job Roles and Responsibilities:
- Certification Impact:
- Continued Learning and Specialization:
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.
- The certification opens doors to various career paths, including:
- Machine Learning Engineer:
- Data Scientist:
- AI Developer:
- Cloud Solutions Architect:
- Business Intelligence Analyst:
- Specialized Roles:
- Management Positions:
- Entrepreneurship:
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.
- Some factors to Azure Machine Learning Certification popularity:
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.
- Designing Machine Learning Models:
- Implementation and Optimization:
- Data Analysis and Interpretation:
- Continuous Model Monitoring and Improvement:
- Collaboration with Data Engineers and Scientists:
- Integration of Predictive Analytics:
- Ensuring Data Security and Compliance:
- Communication of Results:
- Troubleshooting and Issue Resolution:
- Staying Updated with Azure ML Advancements:
- Collaborating in Cross-Functional Teams:
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.
- Top Hiring Industries Like:
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.
- Upon successful completion of the Azure Machine Learning Certification, participants receive a prestigious training certificate. This certificate serves as tangible evidence of their acquired skills and knowledge in the field of Azure machine learning. Recognized by industry professionals, the certificate enhances the graduates' credibility and employability.
- You May Also Read:
Why Should You Learn Azure Machine Learning Certification?
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Azure Machine Learning Training Program
Azure Machine Learning Training Program
Programming with Python Training
- Python is a very powerful high-level, object-oriented programming language. Python is an interpreted language. Python interpreters are available for many operating systems, allowing Python code to run on a wide variety of systems.
- Python has been a favourite option for Data Scientists who use it for building and using Machine Learning Applications and other Scientific Computations.
- Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger.
- In this program you will learn:
Python Training Curriculum
Data Analysis and Visualization using NumPy, Pandas, and MatPlotLib,Seaborn
Get full course syllabus in your inbox
- In this program you will learn:
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
Introduction To Python
- In this program you will learn:
- Python Keyword and Identifiers
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
Python Keyword and Identifiers
- In this program you will learn:
- 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
Introduction To Variables:
- In this program you will learn:
- 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
Python Data Type:
- In this program you will learn:
- 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.
Control Structure & Flow
- In this program you will learn:
- 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
Python Function, Modules and Packages
- In this program you will learn:
- 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
- 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
Python Date Time and Calendar:
- In this program you will learn:
- 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
List
- In this program you will learn:
- 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)
Tuple
- In this program you will learn:
- 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.)
Dictionary
- In this program you will learn:
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
Sets
- In this program you will learn:
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
Strings
- In this program you will learn:
- Python Errors and Built-in-Exceptions
- Exception handing Try, Except and Finally
- Catching Exceptions in Python
- Catching Specific Exception in Python
- Raising Exception
- Try and Finally
Python Exception Handling
- In this program you will learn:
- 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
Python File Handling
- In this program you will learn:
- SQL Database connection using
- Creating and searching tables
- Reading and Storing config information on database
- Programming using database connections
Python Database Interaction
- In this program you will learn:
- Installing SMTP Python Module
- Sending Email
- Reading from file and sending emails to all users
Contacting user Through Emails Using Python
- In this program you will learn:
- Working With Excel
- Reading an excel file using Python
- Writing to an excel sheet using Python
- Python| Reading an excel file
- Python | Writing an excel file
- Adjusting Rows and Column using Python
- ArithmeticOperation in Excel file.
- Play with Workbook, Sheets and Cells in Excel using Python
- Creating and Removing Sheets
- Formatting the Excel File Data
- More example of Python Function
Reading an excel
- In this program you will learn:
- Check Dirs. (exist or not)
- How to split path and extension
- How to get user profile detail
- Get the path of Desktop, Documents, Downloads etc.
- Handle the File System Organization using OS
- How to get any files and folder’s details using OS
Complete Understanding of OS Module of Python
Get full course syllabus in your inbox
- In this program you will learn:
- Introduction to NumPy: Numerical Python
- Importing NumPy and Its Properties
- NumPy Arrays
- Creating an Array from a CSV
- Operations an Array from aCSV
- Operations with NumPy Arrays
- Two-Dimensional Array
- Selecting Elements from 1-D Array
- Selecting Elements from 2-D Array
- Logical Operation with Array
NumPy
- In this program you will learn:
- Working With Excel using Pandas
- Reading an excel & CSV file using Pandas
- Writing to an excel sheet using Pandas
- Adjusting Rows and Column using Pandas
- Arithmetic Operation in Excel file using Pandas.
- Play with Workbook, Sheets and Cells in Excel using Pandas
- Creating and Removing Sheets using Pandas
- Formatting the Excel File Data using Pandas
Pandas
- In this program you will learn:
- 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
- Export the Chart as Image
- Create Charts as Image
- Other Useful Properties of Charts.
- Plotting Different Charts, Labels, and Labels Alignment etc.
MatPlotLib
- In this program you will learn:
- Introduction to Seaborn
- Making a scatter plot with lists
- Making a count plot with a list
- Using Pandas with seaborn
Introduction to Seaborn
- In this program you will learn:
- Concept of Class, Object and Instances
- Constructor, Class attributes and Destructors
- Real time use of class in live projects
- Inheritance, Overlapping and Overloading operators
- Adding and retrieving dynamic attributes of classes
- Programming using Oops support
Python Object Oriented Programming—Oops Concepts
Get full course syllabus in your inbox
Python Django Full Stack Development Training Program
30k
LearnersWeekend/WeekdayLive
Class
- 2 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
Get full course syllabus in your inbox
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
Arithmetic
Relational
Logical
Assignment
Membership
Identity
Get full course syllabus in your inbox
- 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
Get full course syllabus in your inbox
- 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
Get full course syllabus in your inbox
- 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.
Get full course syllabus in your inbox
Django Framework Training Program
30k
LearnersWeekend/WeekdayLive
Class
- 1 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- Croma Campus's Django course helps you gain expertise in Django REST framework, Django Models, Django AJAX, Django jQuery etc. You'll master Django web framework while working on real-time use cases and receive Django certification at the end of the course.
- In this program you will learn:
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
Get full course syllabus in your inbox
- Django Web Framework
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
Get full course syllabus in your inbox
- Getting Started with Django
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.
Get full course syllabus in your inbox
- Create an Application
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
Get full course syllabus in your inbox
- Django - URL Mapping
Organizing Your URLs
Role of URLs in Django
Working URLs
Forms
Sending Parameters to Views
Templates layer
The Render Function
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Python for Data Science Training Program
30k
LearnersWeekend/WeekdayLive
Class
- 2 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- Python has been a favourite option for Data Scientists who use it for building and using Machine Learning Applications and other Scientific Computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger.
- In this program you will learn:
Python Training
Data Analysis and Visualization using Pandas.
Data Analysis and Visualization using NumPy and MatPlotLib
Introduction to Data Visualization with Seaborn
Get full course syllabus in your inbox
- Introduction To Python
Installation and Working with Python
Understanding Python variables
Python basic Operators
Understanding the Python blocks.
- Python Keyword and Identiers
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
Python Keyword and Identiers
Python Comments, Multiline Comments.
Python Indentation
Understating the concepts of Operators
- Introduction To Variables
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
- Python Data Type
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
- Control Structure & Flow
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 Function, Modules and Packages
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
- Python Date Time and Calendar
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
- List
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
- Tuple
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)
- Dictionary
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.)
- Sets
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
Frozen Sets
- Strings
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
- Python Exception Handling
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
- Python File Handling
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
- Python Database Interaction
SQL Database connection using
Creating and searching tables
Reading and Storing cong information on database
Programming using database connections
- Contacting user Through Emails Using Python
Installing SMTP Python Module
Sending Email
Reading from le and sending emails to all users
- Reading an excel
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
- Complete Understanding of OS Module of Python
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
Get full course syllabus in your inbox
- Statistics
Categorical Data
Numerical Data
Mean
Median
Mode
Outliers
Range
Interquartile range
Correlation
Standard Deviation
Variance
Box plot
- Pandas
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)
Get full course syllabus in your inbox
- NumPy
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
- MatPlotLib
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.
Get full course syllabus in your inbox
- Introduction to Seaborn
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
- Visualizing Two Quantitative Variables
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
- Visualizing a Categorical and a Quantitative Variable
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
- Customizing Seaborn Plots
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
Get full course syllabus in your inbox
Machine Learning with Python Training Program
30k
LearnersWeekend/WeekdayLive
Class
- 3 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- Machine learning is important because it gives enterprises a view of trends in ustomer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations
- In this program you will learn:
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
Get full course syllabus in your inbox
- Introduction To Python
Installation and Working with Python
Understanding Python variables
Python basic Operators
Understanding the Python blocks.
- Python Keyword and Identiers
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
Python Keyword and Identiers
Python Comments, Multiline Comments.
Python Indentation
Understating the concepts of Operators
- Introduction To Variables
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
- Python Data Type
- Strings
- List
- Tuples
- Dictionary
- Sets
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.
- Control Structure & Flow
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 Function, Modules and Packages
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
- Python Date Time and Calendar
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
- List
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
- Tuple
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)
- Dictionary
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.)
- Sets
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
Frozen Sets
- Strings
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
- Python Exception Handling
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
- Python File Handling
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
- Python Database Interaction
SQL Database connection using
Creating and searching tables
Reading and Storing cong information on database
Programming using database connections
- Contacting user Through Emails Using Python
Installing SMTP Python Module
Sending Email
Reading from le and sending emails to all users
- Reading an excel
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
- Complete Understanding of OS Module of Python
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
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- Statistics
Categorical Data
Numerical Data
Mean
Median
Mode
Outliers
Range
Interquartile range
Correlation
Standard Deviation
Variance
Box plot
- Pandas
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)
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- NumPy
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
- MatPlotLib
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.
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- Introduction to Seaborn
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
- Visualizing Two Quantitative Variables
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
- Visualizing a Categorical and a Quantitative Variable
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
- Customizing Seaborn Plots
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
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Machine Learning Training Program
30k
LearnersWeekend/WeekdayLive
Class
- 2 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- Machine learning is important because it gives enterprises a view of trends in ustomer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations
- In this program you will learn:
Introduction to Machine Learning
Techniques of Machine Learning
Regression
Classification
Unsupervised Learning: Clustering
Distribution Clustering
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- Introduction to Machine Learning
Articial Intelligence
Machine Learning
Machine Learning Algorithms
Algorithmic models of Learning
Applications of Machine Learning
Large Scale Machine Learning
Computational Learning theory
Reinforcement Learning
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- Techniques of Machine Learning
Supervised Learning
Unsupervised Learning
Semi-supervised and Reinforcement Learning
Bias and variance Trade-off
Representation Learning
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- Regression
Regression and its Types
Logistic Regression
Linear Regression
Polynomial Regression
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- Classification
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
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Deep Learning Training Program
30k
LearnersWeekend/WeekdayLive
Class
- 2 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- Overview of Text Mining
- Need of Text Mining
- Natural Language Processing (NLP) in Text Mining
- Applications of Text Mining
- OS Module
- Reading, Writing to text and word files
- Setting the NLTK Environment
- Accessing the NLTK Corpora
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- Tokenization
- Frequency Distribution
- Different Types of Tokenizers
- Bigrams, Trigrams & Ngrams
- Stemming
- Lemmatization
- Stopwords
- POS Tagging
- Named Entity Recognition
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- Syntax Trees
- Chunking
- Chinking
- Context Free Grammars (CFG)
- Automating Text Paraphrasing
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- Machine Learning: Brush Up
- Bag of Words
- Count Vectorizer
- Term Frequency (TF)
- Inverse Document Frequency (IDF)
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- Introduction to TensorFlow 2.x
- Installing TensorFlow 2.x
- Defining Sequence model layers
- Activation Function
- Layer Types
- Model Compilation
- Model Optimizer
- Model Loss Function
- Model Training
- Digit Classification using Simple Neural Network in TensorFlow 2.x
- Improving the model
- Adding Hidden Layer
- Adding Dropout
- Using Adam Optimizer
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Data Science Training Program
30k
LearnersWeekend/WeekdayLive
Class
- 3 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- NA
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Version Control with Git & GitHub
- 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
- 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
- Basic SQL, DDL and DML commands
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- 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 folders details using OS
Introduction To Python
Python Keyword and Identiers
Introduction To Variables
Python Data Type
Control Structure & Flow
Python Function, Modules and Packages
List
Tuple
Dictionary
Sets
Strings
Python Exception Handling
Python File Handling
Python Database Interaction
Reading an excel
Complete Understanding of OS Module of Python
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- NA
- 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 Nonvalues
- 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 Data Frame 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
- NumPys Mean and Axis
- NumPys Mode, Median and Sum Function
- NumPys 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
- Visualizing Two Quantitative Variables
- Visualizing a Categorical and a Quantitative Variable
- Customizing Seaborn Plots
Data Analysis and Visualization using Pandas.
Data Analysis and Visualization using NumPy
Data Analysis and Visualization using MatPlotLib
Introduction to Data Visualization with Seaborn
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- NA
- Introduction to ML, AI, and Deep Learning
- Types of ML (Supervised, Unsupervised, Reinforcement)
- ML Pipeline: Data Cleaning, Feature Engineering
- Common ML Algorithms: Linear, Logistic, DT, RF, SVM, KNN
- Model Evaluation: Accuracy, Precision, Recall, F1, ROC-AUC
- Overfitting, Underfitting, Cross-Validation
- Hands-on Project: Titanic Dataset (or similar)
Foundation for AI: Learn traditional ML models, evaluation, and workflows.
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- NA
- Introduction to Neural Networks & Deep Learning
- Activation Functions (ReLU, Sigmoid, Tanh)
- Feedforward Neural Network
- Backpropagation & Gradient Descent
- Learning Rate, Schedulers & Optimizers (SGD, Adam, RMSProp)
- Softmax, Cross-Entropy Loss
- Keras Basics: Sequential API & Functional API
- Fully Connected Layer Forward/Backward Pass
- Regularization Dropout, Batch Normalization
- Data Preprocessing & Data Augmentation
- Weight Initialization Strategies
- Babysitting Learning: Overfit detection, TensorBoard Monitoring
- Hands-on: MLP on MNIST / Tabular data (e.g. HR Analytics)
Understand the inner workings of neural networks and train them with Keras.
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- NA
- Basics of Images, Image Preprocessing
- Convolution: 2D Conv, Forward & Backward
- Pooling, Padding, Stride, Transposed Conv
- CNN Architectures: LeNet, AlexNet, VGG, ResNet
- GPU vs CPU for DL
- Transfer Learning: Inception, MobileNet, fine-tuning
- Semantic Segmentation using UNet
- Object Detection YOLO, SSD, Region Proposal
- Bounding Box Regressor
- Siamese Networks for Similarity Search
- Hands-on:
Master CNNs, object detection, segmentation, and deployment.
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Artificial Intelligence Training Program
30k
LearnersWeekend/WeekdayLive
Class
- 1 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- Foundation for AI: Learn traditional ML models, evaluation, and workflows.
Introduction to ML, AI, and Deep Learning
Types of ML (Supervised, Unsupervised, Reinforcement)
ML Pipeline: Data Cleaning, Feature Engineering
Common ML Algorithms: Linear, Logistic, DT, RF, SVM, KNN
Model Evaluation: Accuracy, Precision, Recall, F1, ROC-AUC
Overfitting, Underfitting, Cross-Validation
Hands-on Project: Titanic Dataset (or similar)
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- Understand the inner workings of neural networks and train them with Keras.
Introduction to Neural Networks & Deep Learning
Activation Functions (ReLU, Sigmoid, Tanh)
Feedforward Neural Network
Backpropagation & Gradient Descent
Learning Rate, Schedulers & Optimizers (SGD, Adam, RMSProp)
Softmax, Cross-Entropy Loss
Keras Basics: Sequential API & Functional API
Fully Connected Layer – Forward/Backward Pass
Regularization – Dropout, Batch Normalization
Data Preprocessing & Data Augmentation
Weight Initialization Strategies
Babysitting Learning: Overfit detection, TensorBoard Monitoring
Hands-on: MLP on MNIST / Tabular data (e.g. HR Analytics)
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- Master CNNs, object detection, segmentation, and deployment.
- Image Classification with CNN
- Object Detection with YOLOv8
- Visual Search with Embeddings
Basics of Images, Image Preprocessing
Convolution: 2D Conv, Forward & Backward
Pooling, Padding, Stride, Transposed Conv
CNN Architectures: LeNet, AlexNet, VGG, ResNet
GPU vs CPU for DL
Transfer Learning: Inception, MobileNet, fine-tuning
Semantic Segmentation using UNet
Object Detection – YOLO, SSD, Region Proposal
Bounding Box Regressor
Siamese Networks for Similarity Search
Hands-on:
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- Train text models from scratch and with BERT.
- Sentiment Classifier (LSTM or BERT)
- Deploy NLP Model with Streamlit
Introduction to NLP and Use Cases
Preprocessing: Tokenization, Lemmatization, Stopwords, Normalization
Feature Extraction: BOW, TF-IDF, N-Grams
Word Embeddings: Word2Vec, GloVe, Dense Vectors
POS Tagging, Named Entity Recognition
RNN, LSTM – Forward Pass and BPTT
Advanced LSTM Applications + Architectures
Attention Mechanism + Encoder-Decoder
Transformers, BERT, Hugging Face Pipelines
NLP Evaluation Metrics: BLEU, ROUGE
Hands-on:
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- Take models from notebooks to real-world applications.
- Deploy CV or NLP model with Streamlit
- Create API using FastAPI
Saving & Loading Models (Pickle, Joblib, Keras)
Flask vs FastAPI – Serving ML models
Streamlit/Gradio for Web Apps
Hosting Models on Hugging Face Spaces, Streamlit Cloud
MLflow Intro – Model Tracking & Versioning
Hands-on:
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Internet of Things (IoT) Certification Training
40k
LearnersWeekend/WeekdayLive
Class
- 2 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- In this program you will learn:
Internet of Things (IoT) Introduction
IoT Architecture
Understanding IoT Ecosystems
Raspberry Pi
IoT Gateways
Cloud Platforms for IoT
IoT Implementations [Applications]
Future & Security Concerns
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- Internet of Things (IoT) Introduction
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
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- IoT Architecture
How IoT Works
High level Data Flow in IoT
Technical Architecture
Description of all layers of IoT Architecture
Technologies for IoT
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- Understanding IoT Ecosystems
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
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- Raspberry Pi
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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Advanced Python Training Program
30k
LearnersWeekend/WeekdayLive
Class
- 0 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- What is Data Analytic
- Common Terms in Data Analytics
- What is data
- Classication 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 Data Analytics tools & their popularity.
- Data Analytics Methodology & problem-solving framework.
- List of steps in Data Analytics projects
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- Introduction To Python
Installation and Working with Python
Understanding Python variables
Python basic Operators
Understanding the Python blocks.
- Python Keyword and Identiers
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
Python Comments, Multiline Comments.
Python Indentation
Understating the concepts of Operators
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- Introduction to Statistics
Categorical Data
Numerical Data
Mean
Median
Mode
Outliers
Range
Interquartile range
Correlation
Standard Deviation
Variance
Box plot
- Understanding Statistics
Descriptive Statistics
Sample vs Population Statistics
Random variables
Probability distribution functions
Expected value
Normal distribution
Gaussian distribution
Z-score
Spread and Dispersion
Correlation and Co-variance
- Data Pre-Processing & Data Mining
Data Preparation
Feature Engineering
Feature Scaling
Datasets
Dimensionality Reduction
Anomaly Detection
Parameter Estimation
Data and Knowledge
Selected Applications in Data Mining
- EDA (Exploratory Data Analysis)
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
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- Data Analysis and Visualization using Pandas.
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 Data Frame 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)
- Data Analysis and Visualization using NumPy
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
- Data Analysis and Visualization using MatPlotLib
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 Data Visualization with Seaborn
- 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 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
Visualizing Two Quantitative Variables
Visualizing a Categorical and a Quantitative Variable
Customizing Seaborn Plots
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