Python with ML Training in Noida Java/PHP/Python/hadoop Training Institute in Noida, Delhi/NCR
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Machine Learning with Python Training Institute in Noida

Join Best Machine Learning Training in Noida, Machine Learning Online Training Course in Noida, Machine Learning Online Training Institute in Noida

Are you seeking for the Best Machine Learning with Python Training Institute, or online Machine Learning with Python Training Institute in Noida? In Noida, JavaTpoint Noida offers world-class Machine Learning with Python training. Live projects and simulations are incorporated in our institute's comprehensive Machine Learning coursework. Our students have been able to find jobs in a variety of MNCs thanks to this in-depth Machine Learning with Python training. The instructors at JavaTpoint Noida are subject matter experts and business professionals who provide in-depth Python with Machine Learning training in Noida. Participants will receive a certification in Machine Learning with Python upon completion of the course, as well as a plethora of job opportunities in the industry.

What is machine learning?

Machine learning is a subfield of computer science that enables computers to learn without being explicitly programmed. Generally, it is a type of artificial intelligence (AI) that makes software capable of getting better at predicting events without having to be explicitly coded. Experts predict that artificial intelligence and machine learning will change the way we interact with everything in our surroundings in the coming days.

Machine Learning using Python training in Noida will cover all of the fundamental and advanced ideas of ML (Machine Learning), as well as provide hands-on experience working on real-world scenarios. ML training in Noida will help students gain comprehensive end-to-end mastery of all theoretical and practical aspects of the course. Furthermore, we assist students in developing knowledge in all Machine Learning real-world projects and case studies as well and have accredited partners with a number of multinational corporations, which help to get placement on the basis of their qualifications.

Why JavaTpoint for Machine Learning with Python training?

JavaTpoint Noida is a world-class training institute that offers beginner, intermediate, and advanced training modules. It gives applicants with academic and practical understanding of the course, assisting them in obtaining the finest work possibilities in sectors. Whether you are a project manager, an IT professional, or a college student, the top ML with Python training facility in Noida provides a positive learning environment, experienced Machine Learning with Python instructors, as well as flexible training schedules for whole modules. In addition, the leading training center for Machine Learning with Python training in Noida charges student’s cost-effective tuition. The inexpensive Machine Learning with Python course cost structure is accessible to students from all areas of life.

For a number of reasons, our college is one of the Top Machine Learning Training Institutes in Noida. Some of the causes are as follows:

  • Our curriculum is tailored in order to meet the changing demands of businesses. Our lectures are augmented by projects and tasks that are applicable in the workplace.
  • Microsoft, Nuvoton, Panasonic, Oracle, and Autodesk have all certified JavaTpoint as a training center.
  • We offer world-class laboratory services to implement theoretical into practical. Our experts will provide you the opportunity to work on carefully selected case studies and industry issues.
  • Batch sizes and timings can be adjusted.
  • Our trainers come from the IT industry and have many years of teaching expertise and mentoring students in the most up-to-date technology.
  • Online training is available in addition to classroom training.
  • The course content has been entirely updated with the most recent upgrades so that students can take the lead in the race and get more opportunities in the different industries.

Career after Machine Learning with Python Course in Noida

In modern times, machine learning is advancing at a rapid and gradual pace. Machine Learning will require a large number of technology specialists in the coming years.

To succeed in this field, you will need to learn mathematics, business expertise, technology, statistics, and a variety of technical and logical skills. Data analysis is one of the most important aspects of the Machine Learning field, which is heavily reliant on data in order for the machine to learn on its own.

Before a machine can learn for itself, this necessitates the processing of a large amount of important data.  A Data Analyst's profession in Machine Learning can quickly be transformed.  In the field of Machine Learning, Python is the most common used programming language.

Benefits of doing a Machine Learning Course

  • You will gain a deeper understanding of programming as well as how to apply it to real-world development needs in industrial projects and applications.
  • You will gain more knowledge about the web development framework, which can be used to create dynamic web pages with this framework.
  • You will learn how to deploy desktop, bespoke web, and mobile applications, as well as how to design, develop, test, and support them.
  • Create and improve activities and procedures for testing and maintenance as well.
  • In a Machine Learning environment, design, execute and build key applications.
  • Thus, the opportunity to work for top software companies such as TCS, IBM, Amazon, Wipro, and Infosys, will increase.

Placement Assistance after Machine Learning with Python Training in Noida

JavaTpoint's Placement Department is aggressive and business-savvy, teaching applicants machine learning using Python on real-world projects. Candidates seeking employment and the Best Python with Machine Learning Internships in Noida can count on the Training & Placement unit for all of their needs. The placement department works with other departments to help students learn the principles of a variety of sectors.

This machine learning with a Python Certificate proves that you have learnt a lot about the Python programming language and, as a result, can help you get work in the industry. To get a certificate, you must pass an exam after completing the course. Training is available on all five days of the week, as well as on weekends. Training sessions can also be scheduled according to their preferred weekdays.

You will be able to keep up with the latest updates and modifications while also developing your confidence in your own abilities with this course. Python with machine learning has a bright future ahead of it, with long-term employment opportunities and support in securing students' dream jobs in the field. JavaTpoint is also the Best Machine Learning with Python training center in Noida when it comes to providing placement assistance to all students.

Perks of studying Web development from JavaTpoint:

  • The Machine Learning with Python training centre in Noida at JavaTpoint helps students write resumes that fit current industry needs.
  • Our institute in Noida offers the best placement rate to students.
  • Students' interview abilities are sharpened at JavaTpoint's Machine Learning with Python training institute in Noida, which also offers group discussion, sessions on personality development, spoken English, mock interviews, as well as presentations.
  •  Our institute for best ML with Python training in Noida helps students successfully secure placement in top IT businesses such as Wipro, Accenture, Infosys, HCL, TCS, and others.

Will I get any certification on completion of ML with Python course?

Yes, JavaTpoint will provide you with a course completion certificate once you have satisfactorily completed all sections of the Machine Learning with Python course.

Furthermore, with the help of working on genuine projects, you will be able to demonstrate your newly gained Machine Learning skills, thereby adding value to your portfolio. The assignments and module-level projects will enrich your learning experience even further. You will also get the chance to put your new skills and knowledge as well to use on your own capstone projects.

What projects will I get to work on during Machine Learning with Python online training?

You will have the opportunity to work on a capstone project towards the end of the training. The project is based on the basis of real-life events and is being carried out with the help of industry professionals. Also, you will approach it the same way you would approach a Machine Learning project in the real world.

I am a beginner in Machine Learning. Is this course suitable for me?

The curriculum is tailored to students with varying levels of Machine Learning knowledge. Our trainers provide all necessary information about the course; all information you need to know about Machine Learning, from the fundamentals to advanced ideas, whether you are a beginner or an expert.

Instructor-led training, hands-on exercises, projects, and activities are all used in the training to assist the development of instantly applicable skills.

This intensive and engaging program, which includes an industry-relevant curriculum, a capstone project, and supervised mentoring, is your opportunity to start a career as a Machine Learning specialist in the IT field. The course is broken down into readily digestible units that cover the most recent advances in machine learning and Python. The initial lessons focus on the technical components that will help you become a Machine Learning specialist. The following modules address Python fundamentals, best practices, and Machine Learning applications.

The last sessions delve into Machine Learning in-depth, walking students through algorithms, data kinds, and more. The curriculum incorporates case studies, examples, and real-world scenarios, as well as a reason-based learning method in addition to a practical and problem-solving approach.

What are the software and system requirements to learn ML with Python?

The minimal hardware and software requirements for the Machine Learning with Python training program are shown below.

Hardware requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or newest edition of other popular Linux flavors
  • 10 GB of free space 
  • 4 GB RAM

Software Requirements 

  • The web browser like Google Chrome, Microsoft Edge, or Firefox is required.

System Requirements

  • 8 GB of RAM
  • 32 or 64-bit Operating System

What kind of math is needed for machine learning?

Math is at the heart of machine learning, which aids in the development of an algorithm that can learn from data and generate an accurate prediction. It might be as easy as identifying dogs or cats on the basis of a set of images or recommending products to a consumer based on previous purchases. As a result, a thorough understanding of the arithmetic ideas underlying any central machine learning algorithm is most important. In this way, it assists you to choose all of the appropriate algorithms for your machine learning and data science projects.

However, Statistics, Probability, Linear Algebra, and Calculus are the four key principles that drive machine learning. Calculus aids in the learning and optimization of the models, while statistical ideas lie at the heart of such models. 

Importance of Machine Learning

Machine learning is important because it helps organisations to see trends in corporate operating patterns and customer behaviour, as well as aid in the creation of new products. Machine learning is used by many of today's most successful companies, like Facebook, Google, Uber, and others. For many businesses, machine learning has become a significant competitive differentiation.

Machine learning can be used to reduce costs, manage risks, and improve the overall quality of life in a variety of ways. There are multiple cases that machine learning that can be applied, including recommending products/services, identifying cybersecurity breaches, and enabling self-driving automobiles. Machine learning is growing more common every day, thanks to increased access to data and computing capacity, and will soon be integrated into many aspects of human existence.

How do I start learning Python for machine learning?

There are some basic steps to learn Machine Learning in Python:

  • First of all, install the Python and SciPy platform.
  • Then, loading the dataset.
  • Now, you need to summarize the dataset.
  • After that visualize the dataset.
  • Start evaluating some algorithms.
  • Then, you are required to make some predictions.

Python with ML Course Curriculum 2022

Introduction to Python

check icon What is Python?

check icon Features of Python

check icon Installation of Python

check icon Execution of Python Program

check icon Installation of IDE (Anaconda/PyCharm/Visual Studio/Sublime/Atom)

check icon How to work on IDE?

check icon Debugging Process in IDE

check icon What is PIP?

check icon What is Cpython?

check icon What is Jython?

check icon What is Ironpython?

check icon What is Pypy?

check icon Python versions

check icon How to give input?

check icon Printing to the screen

check icon Understanding the print() function

check icon Python Comments

check icon Python Keywords

check icon Python program in debugger mode

check icon Python interpreter architecture

check icon Python byte code compiler

check icon Python virtual machine (PVM)

check icon Python script mode

check icon How to compile Python program explicitly

Python Data Types

check icon About: Integer, Float, Complex Numbers, Boolean, nonetype

check icon String, List, Tuple, range

check icon Dictionary

check icon Set, Frozenset

check icon Type Conversion

Python Conditional Execution

check icon Boolean expressions

check icon Logical operators

check icon Conditional execution

check icon Alternative execution

check icon Chained conditionals

check icon Nested conditionals

check icon Short-circuit evaluation of logical expressions

Python Loop Statement

check icon Introduction to while Loop

check icon Introduction to for Loop

check icon Understanding the range() function

check icon What is Break statement in for Loop?

check icon What is Continue statement in for Loop?

check icon What is Enumerate function in for Loop?

Python Strings

check icon A string is a sequence

check icon Getting the length of a string using len

check icon Traversal through a string with a loop

check icon String slices

check icon Strings are immutable

check icon Looping and counting

check icon The in operator

check icon String comparison

check icon String methods

check icon Parsing strings

check icon Format operator

Python List

check icon Introduction to List

check icon How to create and access list

check icon Traversing a list

check icon What are List indices

check icon Lists and functions

check icon Deleting elements

check icon Basic List operations

check icon List slices

check icon List Comprehension

check icon List built-in methods

check icon Aliasing

check icon List arguments

Python Tuples

check icon Introduction to tuple

check icon How to access tuple element

check icon Basic tuple operations

check icon How to compare tuples?

check icon How to create nested tuple?

check icon About Tuple functions and methods

check icon How to use tuples as keys in dictionaries?

check icon How to Delete Tuple?

check icon About Slicing of Tuple

check icon What is Tuple immutability?

Python Set

check icon How to create a set, and iteration over set

check icon Python set operations

check icon Python set methods

check icon Set built-in methods

check icon Python Frozensets

Python Dictionary

check icon Introduction to dictionary

check icon How to declare dictionary?

check icon Properties of dictionary

check icon Dictionary as a set of counters

check icon Accessing Items from Dictionary

check icon Python Hashing

check icon Updating Dictionary

check icon Copying Dictionary

check icon Advanced text parsing

check icon Dictionary basic operations

check icon Advanced text parsing

check icon Sorting the Dictionary

check icon Looping and dictionaries

check icon Dictionary built-in methods

Variables, expressions, and statements

check icon Values and types

check icon Variables

check icon Variable names and keywords

check icon Statements

check icon Operators and operands

check icon Expressions

check icon Order of operations

check icon Modulus operator

check icon String operations

check icon Comments

check icon Choosing mnemonic variable names

Python Functions

check icon What is a Function?

check icon Why functions?

check icon Define and call a function

check icon Types of Functions

check icon Built-in functions

check icon Significance of Indentation (Space) in Python

check icon Return Statement

check icon Adding new functions

check icon Types of Arguments in Functions

check icon Parameters and arguments

check icon Default Arguments, Non-Default Arguments

check icon Keyword Arguments, Non-keyword Arguments, Arbitrary Arguments

check icon Type conversion functions

check icon Scope of variables

check icon Anonymous Functions

check icon Math functions

check icon Random numbers

check icon Map(), filter(), reduce() functions

check icon Definitions and uses

check icon Generator function

check icon Decorator function

check icon Python Iterator

check icon Function as arguments

check icon Nested functions

check icon Flow of execution

check icon Fruitful functions and void functions

check icon Functions as return statement

check icon Function return statement

check icon Closure

Python Iteration

check icon Updating variables

check icon The while statements

check icon Infinite loops

check icon Finishing iterations with continue

check icon Definite loops using for

  • check icon Loop patterns
    • Counting and summing loops
    • Maximum and minimum loops

Advanced Python

Exception Handling in Python

check icon What is Exception Handling

check icon Try Expert

check icon Many Exceptions

check icon Else in Exception Handling

check icon Finally Keyword

check icon Raise an exception

check icon Common RunTime Errors in Python

check icon Try …Except

check icon Try …Except …else

check icon Try …finally

check icon Abnormal termination

check icon Python Errors

check icon Hashability

Python Class and Object

check icon Introduction to OOPs Programming

check icon Object Oriented Programing System and its Principles

check icon Basic concepts of Object and Classes

check icon How to define Python Classes

check icon Self-variable in Python

check icon Access Modifier

check icon What is inheritance and its Types?

check icon How inheritance Works

Python Regular Expressions

check icon What is Regular Expression?

check icon Regular Expression Syntax

check icon Extracting data using regular expressions

check icon What is the need of Regular expressions?

check icon Character matching in regular expressions

check icon Combining searching and extracting

check icon About Re module

check icon About Regular expression Patterns

check icon About Literal characters and Meta characters

check icon Functions/ methods related to rogex

Modules and Packages

check icon Why Modules?

check icon How to Import Modules?

check icon Script v/s Module

check icon Standard v/s third party Modules

check icon Why packages

File Handling

check icon About File Handling

check icon How to create a File

check icon Pass by reference vs Value

check icon Append Data to a File

check icon How to read a File

check icon How to Read File line by line

check icon About Files modes and its syntax

check icon Lambda Expressions

check icon Understanding File Handling with Block

Python Data Structure

check icon How to implement Lists and its methods?

check icon How implement Tuple and its methods?

check icon How implement Set and its methods?

check icon Difference between List, Tuple and Set

check icon How implement Dictionary and its methods?

Database

check icon Introduction to Database

check icon About Database concepts

check icon What is Database Package?

check icon Understanding Data Storage

check icon Basic data modeling

check icon Database Browser for SQLite

check icon Structured Query Language summary

check icon About Relational Database (RDBMS) concepts

check icon Hosting a Database on Cloud/Local System

SQL

check icon SQL (Structured Query Language) Basics

check icon DML (Data Manipulation Language), DDL (Data Definition Language), DQL (Data Query Language)

check icon How to create, alter and drop the DDL?

check icon How to insert, update, delete and merge the DML?

check icon How to select the DQL?

check icon SQL constraints

check icon Primary and foreign key, composite key

check icon How to select distinct?

  • check icon SQL operators
    • Addition (+)
    • Subtraction (-)
    • Multiplication (*)
    • Division (/)
    • Modulus (%)
  • check icon SQL Comparison Operators:
    • =
    • !=
    • <>
    • >
    • <
    • >=
    • <=
    • !<
    • !>
  • check icon SQL Logical Operators:
    • ALL
    • AND
    • ANY
    • BETWEEN
    • EXISTS
    • IS NULL
    • OR
    • UNIQUE
  • check icon  SQL like, where, order by, view, joins, aliases
  • check icon Joins:
    • Inter Join
    • Full (Outer) Join
    • Left (Outer) Join
    • Right (Outer) Join
  • check icon MySQL Functions
  • check icon String Functions:
    • Char_length
    • Lower
    • Reverse
    • Upper
  • check icon Numeric Functions:
    • Max
    • Min
    • Sum
    • Avg
    • Count
    • abs
  • check icon Date Functions:
    • Curdate
    • Curtime
    • Now

Python for Data Analytics

NumPy Package

check icon What is NumPy array?

check icon Array Constructor

check icon Introduction to Array

check icon Range() function

check icon LINPAC

check icon How to create 2-D Arrays

check icon About: Vector Operation and Matrix Operation

check icon What is Array indexing and Slicing?

check icon Indexing in 1-D Arrays

check icon Indexing in 2-D Arrays

check icon Slicing in 1-D Arrays

check icon Slicing in 2-D Arrays

check icon Scalar Vectorization

check icon Array Comparison

Pandas Package

check icon Introduction to pandas

check icon Pandas and Data Manipulation

check icon What is Labeled and structured data?

check icon What are Series and DataFrame objects?

check icon What is Data Cleansing?

check icon What is Data normalization?

check icon What is Data visualization?

check icon Deleting and Dropping Columns

check icon Series

check icon Apply() function

check icon Creating Series

check icon Data Frame and Basic Functionality

check icon Head() function

check icon About: Merges and Joins

check icon What is Data inspection?

check icon What is Data fill?

check icon Mean() function

check icon Data Frame Manipulation

check icon Indexing and missing Values

check icon Grouping and Reshaping

How to load datasets

check icon From excel

check icon From CSV

check icon From HTML table

Accessing Data from DataFrame

check icon  at and iat

check icon loc() Function and Iloc() function

check icon head() Function and tail() Function

Exploratory Data Analysis (EDA)

check icon About describe() function

check icon groupby() function

check icon crosstab() function

check icon About Boolean slicing and query

Data Cleaning

check icon About: Map() and apply() functions

check icon How to combine Data Frames?

check icon How to add and remove rows and columns?

check icon How to sort data?

check icon How to handle missing values?

check icon How to handle duplicates?

check icon How to handle data error?

check icon How to handle Date and Time?

check icon Hosting a Database on Cloud or local system

check icon CRUD Operation on Database Tables trough Python

check icon Processing and Cleaning Data through Pandas methods

check icon Dealing with missing values

Python for Data Visualization:

check icon Introduction to Data Visualization

Matplotlib package:

check icon Introduction to MatPlotlib Library

check icon How to use matplotlib.pyplot interface

check icon Types of charts

check icon How to plot Histogram and pie chart?

check icon About: Bar Chart, Stacked Chart, Scatter plot

check icon Outlier detection using Boxplot

check icon Adding data to an Axes object

check icon How to customize plots?

check icon How to customize Data appearance?

check icon How to create a grid of subplots?

check icon Area plot for Indexed Data

Seaborn package:

check icon Introduction to Seaborn library

check icon How to show Seaborn Plots?

check icon How to use Seaborn with Matplotlib defaults?

check icon How to set xlim and ylim in Seaborn?

check icon Visualizing networks and interconnections

Introduction to Statistics

check icon Sample and population

  • check icon Measures of central tendency:
    • Arithmetic mean
    • Harmonic mean
    • Geometric mean
    • Mode
    • First quartile, Second quartile (median), Third quartile
    • Standard deviation
  • check icon Graphical exploratory Data Analysis
  • check icon How to plot and compute simple summary Statistics
  • check icon Quantitively exploratory Data Analysis

Probability Distribution

check icon Introduction to probability

check icon What is Conditional Probability?

check icon What is Normal Distribution?

check icon What is Uniform Distribution?

check icon What is Exponential Distribution?

check icon About Right and Left skewed Distribution

check icon What is Random Distribution?

check icon About Central Limit Theorem

check icon Probabilistically- Discrete variables

check icon Statistical interface rests upon probability

check icon Thinking Probabilistically- Continuous variables

Hypothesis Testing

check icon What is Normality?

check icon What is Mean Test?

check icon About T-test

check icon About Z-test

check icon What is ANOVA test?

check icon What is Chi square test?

check icon About Correlation and covariance

Advanced Excel:

  • check icon Data Visualization:
    • How to specify a valid range of values for a cell?
    • How to specify a list of valid values for a cell?
    • How to specify custom validations based on formula for a cell?
  • check icon Working with templates:
    • How to design the structure of a template?
    • How to use templates for standardization of worksheets?
  • check icon Sorting and Filtering Data
    • How to sort tables?
    • How to use multiple-level sorting and custom sorting?
    • How to filter data for selected view?
    • How to use advanced filter options?
  • check icon Working with Reports
    • How to create subtotals and multi-level subtotals?
    • How to create, format and customize Pivot tables and Pivot charts?
    • How to use advanced options of Pivot tables?
    • How to use external data sources?
    • How to create slicers?
  • check icon WhatIf Analysis
    • How to seek goal?
    • How to create data tables?
    • How to create scenario manager?
  • check icon Charts
    • How to use and format charts?
    • How to use 3D Graphs?
    • How to use Bar and Line chart together?
    • How to use Secondary Axis in Graphs?

Tableau:

  • check icon Tableau Home
    • Tableau overview
    • Environment setup
    • About Navigations
    • About Design flow
    • About Files Types
    • About Data Terminology
  • check icon Data Sources:
    • How to customize data view?
    • How to extract data?
    • How to use field operations?
    • How to edit metadata?
    • What is Data joining?
    • What is Data Blending?
  • check icon Tableau Worksheets:
    • How to add worksheets?
    • How to rename and reorder worksheets?
    • How to save and delete worksheets?
  • check icon Tableau Calculation:
    • Numeric Calculation operations and Functions
    • String Calculation operations and Functions
    • Date Calculation functions
    • Table Calculation functions
    • Load expressions and operations
  • check icon Tableau Sorting and Filter:
    • How to use basic sorting and basic filters?
    • How to use quick filters, conditional filters, top filters, context filters and Filter operations?

BONUS

check icon How big is Big Data?

check icon How to store BIG DATA in Commodity Hardware's?

check icon Current processing frameworks

check icon Cluster Computing architecture insights

check icon Maintaining High Availability of data

check icon About Market domain and its growth?

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We will help you with the following:

  • Adapt to the changing market conditions.
  • Adapt new technologies.
  • Innovate continually.
  • Align IT with business goals.
  • Optimize costs, while maintaining high customer satisfaction.
  • Accelerate time-to-market for new products and services.
  • Integrate distributed operations and systems into a cohesive organization.

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