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  • Machine Learning is considered as one of the most crucial skills today and its demand is surely going to increase in the future too tremendously. According to experts, Machine Learning will stay relevant for at least next 20 years. The Machine Learning Training in Delhi helps to acquire all the required skills and makes you proficient as an ML engineer.
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  • With the top Machine learning institute in Delhi, you can learn all the concepts step-by-step and make a strong foundation in the AI domain. It does not matter where you stand in your career currently, we can help you at all levels with course customization features.

  • The Machine learning training in Delhi is designed with the core objective to define all the machine learning concepts in-depth and helps you to understand the roles and responsibilities in machine learning space.
    • You may know about ML algorithms in detail including designing, development, and their implementation in the workplace.

      You will get hands-on training in component analysis to design detailed data models using the best ML practices.

      Know about different types of learning in ML and how it can be supported using vector algorithms.

      With the comprehensive machine learning course, know about various machine learning concepts including linear regression, data modelling types, component analysis, vector machines, and more.

      Know how to design an artificial neutral network and logical classification of data using R programming language.

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  • You must be wondering how much can you get as an expert ML professional Well, you are eligible to get $74K to $140K as a certified ML engineer at the fresher level. Moving ahead, variation can be seen in salaries in terms of location, Company, skillset, experience, etc. further, you have different job profiles that also affects the salary range. With Machine Learning placement course in Delhi, you become eligible to apply for jobs and bag attractive salaries even at the fresher level.
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  • At Croma Campus, we strongly believe that you should always start your training from basics. Even if you are an experienced user, brush up your skills first to make a strong foundation in the machine learning space. Once you have gained all the required skills, you must be excited to know about the career growth after the machine learning certification course.
    • As soon as you become stronger in the Machine Learning domain, we help you to attempt the certification exam further.

      As an expert ML engineer, you can design or implement ML algorithms successfully and showcase the same at your workplace too.

      Get hired by top industries around the world and BAG huge salary packages that you ever dreamt of.

      In a span of 2-3 years, see yourself more established when compared to your colleagues working at the same level.

  • Gradually with our machine learning training course in Delhi, you can learn technical things like ML machines, supervised or unsupervised learning, ML algorithms, use-cases, projects, etc. Also, you will understand what are your roles and responsibilities in Machine Learning.

  • As the name suggests, Machine learning is a popular AI concept that helps machines to learn from patterns, examples, or past experiences. The best thing is that developers dont have to program much to achieve the excellence through machine learning. Instead of writing the code, you have to develop machine learning algorithms and the machine can build the logic by analyzing the existing data.
  • Let us see some of the most important facts what makes the Machine Learning certification course more popular for aspirants.
    • It is #1 IT skill in the AI space.

      The average salary taken by machine learning experts lie between $70K to $140K per annum.

      You have the flexibility to work on various profiles and earn accordingly.

      The machine learning market is growing exponentially worldwide.

      Almost every IT industry believe that Ai can make their lives easy.

  • With the Machine Learning training in Delhi, we make you proficient in using different Machine learning tools or techniques. Also, work on multiple projects, assignments, and case studies as the part of training.
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  • Machine learning has various roles and job profiles to obey on the completion of the ML certification course. Let us see a few common job duties that usually remains the same for all profiles.
    • You should know about all the machine learning concepts in-depth and helps you to understand the roles and responsibilities in machine learning space.

      You must know about ML algorithms in detail including designing, development, and their implementation in the workplace.

      You must have hands-on expertise in component analysis to design detailed data models using the best ML practices.

      You should know about different types of learning in ML and how it can be supported using vector algorithms.

      You should know about various machine learning concepts including linear regression, data modelling types, component analysis, vector machines, and more.

      You must know how to design an artificial neutral network and logical classification of data using R programming language.

  • Being the trailblazing Machine Learning Training Institute in Delhi, we prepare you to manage all these roles and responsibilities in Machine Learning with the utmost care and confidence.

  • IBM, Accenture, Deloitte, Facebook, Google, Microsoft, American Express, User, Oracle, etc.
  • These are a few top hiring industries in machine learning and you may also get a chance to work with them if you are skilled enough to clear their interviews in the first attempt. The Machine Learning Training in Delhi gives you just the right skills to start your career in one of the top IT industries and bag BIG salary packages too.
  • With the most prominent Machine Learning Training Institute in Delhi, you not only become industry-ready but prepare yourself for global certification exam too. Also, work on real-time projects, case studies, or assignment to make yourself highly relevant in the corporate world.

  • To get a training certification on the completion of machine learning certification course in Delhi, you need to complete your training successful and submit one project work to the trainer.

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Course Duration 40-45 Days 7 Weekends 8 Days
Hours 1 Hrs. Per Day 2 Hrs. Per Day 6+ Hrs. Per Day
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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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                        + More Lessons

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                        Mock Interviews

                        Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
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                        Projects

                        Validate your skills and knowledge by working on industry-based projects that includes significant real-time use cases.Gain hands-on expertize in Top IT skills and become industry-ready after completing our project works and assessments.Our projects are perfectly aligned with the modules given in the curriculum and they are picked up based on latest industry standards. Add some meaningful project works in your resume, get noticed by top industries and start earning huge salary lumps right away.
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                        FAQ's

                        Python brings an exceptional amount of power and versatility to machine-learning environments. The language's simple syntax simplifies data validation and streamlines the scraping, processing, refining, cleaning, arranging and analyzing processes, thereby making collaboration with other programmers less of an obstacle.

                        Machine Learning is a field of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed. Python has gained immense popularity in the machine learning community because of its simplicity, versatility, and rich ecosystem of libraries and tools like TensorFlow, sci-kit-learn, and PyTorch. Python's ease of use, extensive documentation, and active community support make it an ideal programming language.

                        No, it's not a very complex course as it seems. For candidates belonging to a technical educational background, it will become quite easier for them to learn this combination advancement in fewer days. But for a complete beginner, it might take time, but with adequate guidance, you will end up knowing every detail.

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