Python with Machine Learning Training in Noida

Machine Learning with Python Certification Course Fees, Syllabus, and Jobs

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

 What is Python?

 Features of Python

 Installation of Python

 Execution of Python Program

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

 How to work on IDE?

 Debugging Process in IDE

 What is PIP?

 What is Cpython?

 What is Jython?

 What is Ironpython?

 What is Pypy?

 Python versions

 How to give input?

 Printing to the screen

 Understanding the print() function

 Python Comments

 Python Keywords

 Python program in debugger mode

 Python interpreter architecture

 Python byte code compiler

 Python virtual machine (PVM)

 Python script mode

 How to compile Python program explicitly

Python Data Types

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

 String, List, Tuple, range

 Dictionary

 Set, Frozenset

 Type Conversion

Python Conditional Execution

 Boolean expressions

 Logical operators

 Conditional execution

 Alternative execution

 Chained conditionals

 Nested conditionals

 Short-circuit evaluation of logical expressions

Python Loop Statement

 Introduction to while Loop

 Introduction to for Loop

 Understanding the range() function

 What is Break statement in for Loop?

 What is Continue statement in for Loop?

 What is Enumerate function in for Loop?

Python Strings

 A string is a sequence

 Getting the length of a string using len

 Traversal through a string with a loop

 String slices

 Strings are immutable

 Looping and counting

 The in operator

 String comparison

 String methods

 Parsing strings

 Format operator

Python List

 Introduction to List

 How to create and access list

 Traversing a list

 What are List indices

 Lists and functions

 Deleting elements

 Basic List operations

 List slices

 List Comprehension

 List built-in methods

 Aliasing

 List arguments

Python Tuples

 Introduction to tuple

 How to access tuple element

 Basic tuple operations

 How to compare tuples?

 How to create nested tuple?

 About Tuple functions and methods

 How to use tuples as keys in dictionaries?

 How to Delete Tuple?

 About Slicing of Tuple

 What is Tuple immutability?

Python Set

 How to create a set, and iteration over set

 Python set operations

 Python set methods

 Set built-in methods

 Python Frozensets

Python Dictionary

 Introduction to dictionary

 How to declare dictionary?

 Properties of dictionary

 Dictionary as a set of counters

 Accessing Items from Dictionary

 Python Hashing

 Updating Dictionary

 Copying Dictionary

 Advanced text parsing

 Dictionary basic operations

 Advanced text parsing

 Sorting the Dictionary

 Looping and dictionaries

 Dictionary built-in methods

Variables, expressions, and statements

 Values and types

 Variables

 Variable names and keywords

 Statements

 Operators and operands

 Expressions

 Order of operations

 Modulus operator

 String operations

 Comments

 Choosing mnemonic variable names

Python Functions

 What is a Function?

 Why functions?

 Define and call a function

 Types of Functions

 Built-in functions

 Significance of Indentation (Space) in Python

 Return Statement

 Adding new functions

 Types of Arguments in Functions

 Parameters and arguments

 Default Arguments, Non-Default Arguments

 Keyword Arguments, Non-keyword Arguments, Arbitrary Arguments

 Type conversion functions

 Scope of variables

 Anonymous Functions

 Math functions

 Random numbers

 Map(), filter(), reduce() functions

 Definitions and uses

 Generator function

 Decorator function

 Python Iterator

 Function as arguments

 Nested functions

 Flow of execution

 Fruitful functions and void functions

 Functions as return statement

 Function return statement

 Closure

Python Iteration

 Updating variables

 The while statements

 Infinite loops

 Finishing iterations with continue

 Definite loops using for

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

Advanced Python

Exception Handling in Python

 What is Exception Handling

 Try Expert

 Many Exceptions

 Else in Exception Handling

 Finally Keyword

 Raise an exception

 Common RunTime Errors in Python

 Try …Except

 Try …Except …else

 Try …finally

 Abnormal termination

 Python Errors

 Hashability

Python Class and Object

 Introduction to OOPs Programming

 Object Oriented Programing System and its Principles

 Basic concepts of Object and Classes

 How to define Python Classes

 Self-variable in Python

 Access Modifier

 What is inheritance and its Types?

 How inheritance Works

Python Regular Expressions

 What is Regular Expression?

 Regular Expression Syntax

 Extracting data using regular expressions

 What is the need of Regular expressions?

 Character matching in regular expressions

 Combining searching and extracting

 About Re module

 About Regular expression Patterns

 About Literal characters and Meta characters

 Functions/ methods related to rogex

Modules and Packages

 Why Modules?

 How to Import Modules?

 Script v/s Module

 Standard v/s third party Modules

 Why packages

File Handling

 About File Handling

 How to create a File

 Pass by reference vs Value

 Append Data to a File

 How to read a File

 How to Read File line by line

 About Files modes and its syntax

 Lambda Expressions

 Understanding File Handling with Block

Python Data Structure

 How to implement Lists and its methods?

 How implement Tuple and its methods?

 How implement Set and its methods?

 Difference between List, Tuple and Set

 How implement Dictionary and its methods?

Database

 Introduction to Database

 About Database concepts

 What is Database Package?

 Understanding Data Storage

 Basic data modeling

 Database Browser for SQLite

 Structured Query Language summary

 About Relational Database (RDBMS) concepts

 Hosting a Database on Cloud/Local System

SQL

 SQL (Structured Query Language) Basics

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

 How to create, alter and drop the DDL?

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

 How to select the DQL?

 SQL constraints

 Primary and foreign key, composite key

 How to select distinct?

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

Python for Data Analytics

NumPy Package

 What is NumPy array?

 Array Constructor

 Introduction to Array

 Range() function

 LINPAC

 How to create 2-D Arrays

 About: Vector Operation and Matrix Operation

 What is Array indexing and Slicing?

 Indexing in 1-D Arrays

 Indexing in 2-D Arrays

 Slicing in 1-D Arrays

 Slicing in 2-D Arrays

 Scalar Vectorization

 Array Comparison

Pandas Package

 Introduction to pandas

 Pandas and Data Manipulation

 What is Labeled and structured data?

 What are Series and DataFrame objects?

 What is Data Cleansing?

 What is Data normalization?

 What is Data visualization?

 Deleting and Dropping Columns

 Series

 Apply() function

 Creating Series

 Data Frame and Basic Functionality

 Head() function

 About: Merges and Joins

 What is Data inspection?

 What is Data fill?

 Mean() function

 Data Frame Manipulation

 Indexing and missing Values

 Grouping and Reshaping

How to load datasets

 From excel

 From CSV

 From HTML table

Accessing Data from DataFrame

  at and iat

 loc() Function and Iloc() function

 head() Function and tail() Function

Exploratory Data Analysis (EDA)

 About describe() function

 groupby() function

 crosstab() function

 About Boolean slicing and query

Data Cleaning

 About: Map() and apply() functions

 How to combine Data Frames?

 How to add and remove rows and columns?

 How to sort data?

 How to handle missing values?

 How to handle duplicates?

 How to handle data error?

 How to handle Date and Time?

 Hosting a Database on Cloud or local system

 CRUD Operation on Database Tables trough Python

 Processing and Cleaning Data through Pandas methods

 Dealing with missing values

Python for Data Visualization:

 Introduction to Data Visualization

Matplotlib package:

 Introduction to MatPlotlib Library

 How to use matplotlib.pyplot interface

 Types of charts

 How to plot Histogram and pie chart?

 About: Bar Chart, Stacked Chart, Scatter plot

 Outlier detection using Boxplot

 Adding data to an Axes object

 How to customize plots?

 How to customize Data appearance?

 How to create a grid of subplots?

 Area plot for Indexed Data

Seaborn package:

 Introduction to Seaborn library

 How to show Seaborn Plots?

 How to use Seaborn with Matplotlib defaults?

 How to set xlim and ylim in Seaborn?

 Visualizing networks and interconnections

Introduction to Statistics

 Sample and population

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

Probability Distribution

 Introduction to probability

 What is Conditional Probability?

 What is Normal Distribution?

 What is Uniform Distribution?

 What is Exponential Distribution?

 About Right and Left skewed Distribution

 What is Random Distribution?

 About Central Limit Theorem

 Probabilistically- Discrete variables

 Statistical interface rests upon probability

 Thinking Probabilistically- Continuous variables

Hypothesis Testing

 What is Normality?

 What is Mean Test?

 About T-test

 About Z-test

 What is ANOVA test?

 What is Chi square test?

 About Correlation and covariance

Advanced Excel:

•  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?
•  Working with templates:
  • How to design the structure of a template?
  • How to use templates for standardization of worksheets?
•  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?
•  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?
•  WhatIf Analysis
  • How to seek goal?
  • How to create data tables?
  • How to create scenario manager?
•  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:

•  Tableau Home
  • Tableau overview
  • Environment setup
  • About Navigations
  • About Design flow
  • About Files Types
  • About Data Terminology
•  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?
•  Tableau Worksheets:
  • How to add worksheets?
  • How to rename and reorder worksheets?
  • How to save and delete worksheets?
•  Tableau Calculation:
  • Numeric Calculation operations and Functions
  • String Calculation operations and Functions
  • Date Calculation functions
  • Table Calculation functions
  • Load expressions and operations
•  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

 How big is Big Data?

 How to store BIG DATA in Commodity Hardware's?

 Current processing frameworks

 Cluster Computing architecture insights

 Maintaining High Availability of data

 About Market domain and its growth?