Python with ML Training in Noida
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.
- 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
- The web browser like Google Chrome, Microsoft Edge, or Firefox is required.
- 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?
How to give input?
Printing to the screen
Understanding the print() function
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
Python Conditional Execution
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?
A string is a sequence
Getting the length of a string using len
Traversal through a string with a loop
Strings are immutable
Looping and counting
The in operator
Introduction to List
How to create and access list
Traversing a list
What are List indices
Lists and functions
Basic List operations
List built-in methods
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?
How to create a set, and iteration over set
Python set operations
Python set methods
Set built-in methods
Introduction to dictionary
How to declare dictionary?
Properties of dictionary
Dictionary as a set of counters
Accessing Items from 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
Variable names and keywords
Operators and operands
Order of operations
Choosing mnemonic variable names
What is a Function?
Define and call a function
Types of Functions
Significance of Indentation (Space) in Python
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
Map(), filter(), reduce() functions
Definitions and uses
Function as arguments
Flow of execution
Fruitful functions and void functions
Functions as return statement
Function return statement
The while statements
Finishing iterations with continue
Definite loops using for
- Loop patterns
- Counting and summing loops
- Maximum and minimum loops
Exception Handling in Python
What is Exception Handling
Else in Exception Handling
Raise an exception
Common RunTime Errors in Python
Try …Except …else
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
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
How to Import Modules?
Script v/s Module
Standard v/s third party Modules
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
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?
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 (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?
Primary and foreign key, composite key
How to select distinct?
- SQL operators
- Addition (+)
- Subtraction (-)
- Multiplication (*)
- Division (/)
- Modulus (%)
- SQL Comparison Operators:
- SQL Logical Operators:
- IS NULL
- SQL like, where, order by, view, joins, aliases
- Inter Join
- Full (Outer) Join
- Left (Outer) Join
- Right (Outer) Join
- MySQL Functions
- String Functions:
- Numeric Functions:
- Date Functions:
Python for Data Analytics
What is NumPy array?
Introduction to Array
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
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
Data Frame and Basic Functionality
About: Merges and Joins
What is Data inspection?
What is Data fill?
Data Frame Manipulation
Indexing and missing Values
Grouping and Reshaping
How to load datasets
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
About Boolean slicing and query
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
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
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
- 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
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
What is Normality?
What is Mean Test?
What is ANOVA test?
What is Chi square test?
About Correlation and covariance
- 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?
- 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 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?
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?
The javaTpoint Advantage:
We partner with you to understand and address your unique transformation imperatives. We work in transparent consultation with you to devise best-in-class solutions and define the best course of action to implement them across your organization. Our integrated consulting and IT services will bring continuity and consistency to your strategic programs.
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.