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  • Machine Learning Training with Python in Delhi offers a unique learning opportunity for those interested in data science and programming. The training focuses on teaching you the basics of Python and how to use it in machine learning, which helps computers learn from data and make predictions. The course is flexible so you can choose how you want to learn - online courses, self-paced modules, or face-to-face sessions.
  • Croma Campus understands that many people work alongside their studies. We offer a variety of study methods, including online courses that you can join live and learn at your own pace, as well as traditional classroom sessions, so you can fit your studies around work and other commitments.
  • Whether you're new to programming or looking to advance in your career, Machine Learning Training with Python in Delhi will help you develop the skills you need to succeed in today's data-driven world.

Machine Learning with Python Training in Delhi

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  • Machine Learning With Python Course in Delhi courses provide participants with the knowledge and skills required to effectively configure and manage the various features of the Machine Learning With Python Training Course in Delhi:
    • Learn the Basics of Python: Understand how to use Python for data processing and writing programs.

      Understand the Basics of Machine Learning: Learn the basics of how computers learn from data and make predictions.

      Data Processing Skills: Learn how to organize and manage data using tools such as Pandas and NumPy.

      Using Statistics for Analysis: Learn how to apply statistical methods to understand and interpret data.

      Practice on Real Projects: Work on real projects to apply what you've learned and gain practical experience.

  • Machine Learning Training with Python in Delhi focuses on practical skills and prepares you for a successful career in today's data-driven world.

  • After completing Machine Learning Training with Python in Delhi, freshers can expect a reasonable starting salary in the field of data science and analytics. Typically, entry-level jobs offer a salary that indicates how much the company needs someone who is good at Python and well versed in machine learning. In Delhi, an entry-level worker with good Python skills and basic knowledge of machine learning can start working with a salary of INR 300,000 to INR 500,000 per year.

  • Completing a Machine Learning Training with Python in Delhi course will significantly improve your career prospects.
    • Start your career as a Junior Data Analyst or Data Associate, helping in data analysis and report creation using Python and Machine Learning learning tools.

      Keep learning Python and Machine Learning techniques to stay up to date with what's happening in the industry and improve your skills.

      With more experience, you can become a Data Scientist, which means analyzing complex data, building predictive models, and providing data-driven advice.

      You can also focus more on becoming a Machine Learning Engineer, where you design and configure systems that use machine learning to help companies improve their operations.

      Depending on your preference, you can work in areas such as banking, healthcare, online shopping, telephony, etc.

  • Machine Learning Course with Python in Delhi is popular for a few clear reasons. First, Python is known for being easy to learn and powerful for analyzing data. It has tools that make it perfect for Machine Learning, where computers learn from data to make predictions. Many people find Python friendly and useful, so they choose it to study Machine Learning.
  • There's a big demand for people who know Machine Learning and Python in Delhi. Companies in finance, healthcare, and more need experts who can use data to help them make smart decisions. Learning these skills can lead to good jobs with competitive salaries, making it a smart choice for anyone interested in a career in technology and data analysis.

  • The Machine Learning Training with Python in Delhi prepares professionals for a wide range of roles and responsibilities.
    • Data Analyst: As a data analyst, your primary job is to interpret data and turn it into information that a company can use to make decisions.

      Data Scientist: Data Scientists delve into data and build complex machine learning models using Python. Your responsibilities include identifying data sources, developing algorithms, and applying advanced statistical methods to generate insights and solve business problems.

      Machine Learning Engineer: This role focuses on designing and deploying machine learning systems using Python. Tasks include data pre-processing, selecting the right model, and optimizing performance.

      AI Specialist: As an AI Specialist, you will specialize in developing artificial intelligence solutions based on machine learning algorithms.

      Business Intelligence Developer: In this role, you'll use Python and machine learning to build tools and applications that help companies analyze data more effectively.

  • Machine Learning Training with Python in Delhi opens the door to jobs in some of key industries: IT companies use it for software and cybersecurity; the finance industry uses it to understand the market and manage funds better. In the medical sector, it helps in diagnosing and treating patients; and in e-commerce, it is used to predict what customers want to buy. Telecommunication companies use it to improve their networks and understand their customers better. Enroll in our Artificial Intelligence Course in Delhi to gain the skills needed to leverage Python and machine learning for solving problems and enhancing operations across various industries.

  • A certificate in Machine Learning Training with Python in Delhi is important because it proves that you can learn and use Python and Machine Learning. It can help you get a job by showing employers that you have the skills they need. The certificate also shows that you are serious about learning and improving, which can help you advance in your career or get admitted into a higher education program.

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Machine Learning with Python Training

  • 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

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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
    • Python Keyword and Identiers

      Python Comments, Multiline Comments.

      Python Indentation

      Understating the concepts of Operators

      • Arithmetic
      • Relational
      • Logical
      • Assignment
      • Membership
      • Identity
  • 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

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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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  • 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

  • Techniques of Machine Learning
    • Supervised Learning

      Unsupervised Learning

      Semi-supervised and Reinforcement Learning

      Bias and variance Trade-off

      Representation Learning

  • Regression
    • Regression and its Types

      Logistic Regression

      Linear Regression

      Polynomial Regression

  • Classication
    • Meaning and Types of Classication

      Nearest Neighbor Classiers

      K-nearest Neighbors

      Probability and Bayes Theorem

      Support Vector Machines

      Naive Bayes

      Decision Tree Classier

      Random Forest Classier

  • Unsupervised Learning: Clustering
    • About Clustering

      Clustering Algorithms

      K-means Clustering

      Hierarchical Clustering

      Distribution Clustering

  • Model optimization and Boosting
    • Ensemble approach

      K-fold cross validation

      Grid search cross validation

      Ada boost and XG Boost

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FAQ's

Some knowledge is required. Programming basics. Already knowing Python is helpful but not required.

Croma Campus regularly updates its courses to reflect and include the latest advancements in Python and Machine Learning. Your teachers are experts who bring real-world experience to the classroom, so you'll learn the latest skills.

After completing their training, graduates often work as data analysts, data scientists, machine learning engineers, AI specialists, or business intelligence developers. You can find jobs in industries such as IT, finance, healthcare, e-commerce, and communications.

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