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30 Hrs of Sessions

20 Hrs of Labs

Real-time Use cases

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Certification Based Curriculum

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Skill India


Get a head start on your career with Edusoft's top-rated machine learning course in Lakshmi Nagar. The course comes with a guaranteed job referral, so you can be confident in your future job prospects. Plus, the course is created by industry experts and focuses on providing practical, hands-on learning experiences. You'll gain valuable skills and knowledge that will set you apart from the competition.

1. Python for Data Science

Python Basics

  • Need for Programming
  • Advantages of Programming
  • Overview of Python
  • Organizations using Python
  • Python Applications in Various Domains
  • Python Installation
  • Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments

Data Structure and File Operations

  • Method of Accepting User Input and eval Function
  • Python - Files Input/Output Functions
  • Lists and Related Operations
  • Tuples and Related Operations
  • Strings and Related Operations
  • Sets and Related Operations
  • Dictionaries and Related Operations

Functions and Object Oriented Programming

  • User-Defined Functions
  • Concept of Return Statement
  • Concept of name =” main ”
  • Function Parameters
  • Different Types of Arguments
  • Global Variables
  • Global Keyword
  • Variable Scope and Returning Values
  • Lambda Functions
  • Various Built-In Functions
  • Introduction to Object-Oriented Concepts
  • Built-In Class Attributes
  • Public, Protected and Private Attributes, and Methods
  • Class Variable and Instance Variable
  • Constructor and Destructor
  • Decorator in Python
  • Core Object-Oriented Principles
  • Inheritance and Its Types
  • Method Resolution Order
  • Overloading
  • Overriding
  • Getter and Setter Methods
  • Inheritance-In-Class Case Study

Working with Modules and Handling Exceptions

  • Standard Libraries
  • Packages and Import Statements
  • Topics : Working with Modules and Handling Exceptions
  • Info@Edusoft' | +91-7701928515 | www.Edusoft'
  • Reload Function
  • Important Modules in Python
  • Sys Module
  • Os Module
  • Math Module
  • Date-Time Module
  • Random Module
  • JSON Module
  • Regular Expression
  • Exception Handling

Introduction to NumPy

  • Basics of Data Analysis
  • NumPy - Arrays
  • Operations on Arrays
  • Indexing Slicing and Iterating
  • NumPy ArrayAttributes
  • Matrix Product
  • NumPy Functions
  • Functions
  • Array Manipulation
  • File Handling Using NumPy
  • Array Creation and Logic Functions
  • File Handling Using Numpy

Data Manipulation using pandas

  • Introduction to pandas
  • Data structures in pandas
  • Series
  • Data Frames
  • Importing and Exporting Files in Python
  • Basic Functionalities of a Data Object
  • Merging of Data Objects
  • Concatenation of Data Objects
  • Types of Joins on Data Objects
  • Data Cleaning using pandas
  • Exploring Datasets

2. Data Science Primer and Statistics

Basics of Data Science

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Application of Data Science

Exploratory Data Analysis

  • Introduction
  • Stages of Analytics
  • CRISP DM Data Life Cycle
  • Data Types
  • Introduction to EDA
  • First Business Moment Decision
  • Second Business Moment Decision
  • Third Business Moment Decision
  • Fourth Business Moment Decision
  • Correlation

Feature Engineering

  • What is Feature
  • Feature Engineering
  • Feature Engineering Process
  • Benefit
  • Feature Engineering Techniques

Inferential Statistics & Hypothesis Testing

  • Basics Of Probability
  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Central Limit Theorem
  • Concepts Of Hypothesis Testing - I: Null And Alternate Hypothesis, Making
  • A Decision, And Critical Value Method
  • Concepts Of Hypothesis Testing - II: P-Value Method And Types Of Errors
  • Industry Demonstration Of Hypothesis Testing: Two-Sample Mean And
  • Proportion Test, A/B Testing

3. Machine Learning

Linear Regression

  • Simple Linear Regression
  • Simple Linear Regression In Python
  • Multiple Linear Regression
  • Multiple Linear Regression In Python
  • Industry Relevance Of Linear Regression

Linear Regression

  • Simple Linear Regression
  • Simple Linear Regression In Python
  • Multiple Linear Regression
  • Multiple Linear Regression In Python
  • Industry Relevance Of Linear Regression

Logistic Regression

  • Univariate Logistic Regression
  • Multivariate Logistic Regression: Model
  • Building And Evaluation
  • Logistic Regression:
  • Industry Applications

KNN classifier

  • Data mining classifier technique
  • Application of KNN classifier
  • Lazy learner classifier
  • Altering hyperparameter(k) for better accuracy

Support Vector classifier

  • Black box
  • SVM hyperplane
  • Max margin hyperplane
  • Kernel tricks for non linear spaces

Decision Tree Classifier

  • Rule based classification method
  • Different nodes for develop decision trees
  • Discretization
  • Entropy
  • Greedy approach
  • Information gain

Ensemble Learning

  • Challenges with standalone model
  • Reliability and performance of a standalone model
  • Homogeneous & Heterogeneous Ensemble Technique
  • Bagging & Boosting
  • Random forest
  • Stacking
  • Voting & Averaging technique

Time Series Analysis

  • Difference between cross sectional and time series data
  • Different component of time series data
  • Visualization techniques for time series data
  • Model based approach
  • Data driven based approach


  • Difference between Supervised and Unsupervised Learning
  • Prelims of clustering
  • Measuring distance between record and groups
  • Linkage functions
  • Dendrogram

Dimensionality Reduction

  • Dimension reduction
  • Application of PCA
  • PCA & its working
  • SVD & its working

Market Basket Analysis

  • Point of Sale
  • Application of Association rules
  • Measure of association rules
  • Drawback of measure of association rules
  • Condition probability
  • Lift ratio

4. Deep Learning

Introduction to Perceptron, Multilayer Perceptron/ANN

  • Black box techniques
  • Intution of neural networks
  • Perceptron algorithm
  • Calculation of new weights
  • Non linear boundaries in MLP
  • Integration function
  • Activation function
  • Error surface
  • Gradient descent algo

Deep Learning Black Box Technique – CNN, RNN

  • Imagenet classification challenges
  • Convolution network applications
  • Challenges in classifying the images using MLP
  • Parameter explosion
  • Pooling layers
  • Fully connected layers
  • Alexnet case study
  • Modelling sequence data
  • Vanishing/Gradient descent explode

Platforms for Deep Learning & Deep Learning Software Libraries

  • What is a Deep Learning Platform?
  • Dato GraphLab
  • What is a Deep Learning Library?
  • Theano
  • Deeplearning4j
  • Torch
  • Caffe

5. Data Visualization and Story Telling

Basic Visualization Tools

  • Bar Charts
  • Histograms
  • Pie Charts
  • Box Plots

Basic Visualization Tools Continued

  • Scatter Plots
  • Line Plots and Regression

Specialized Visualization Tools

  • Pair plot
  • Word Clouds
  • Radar Charts
  • Waffle Charts
  • 6. Natural Language Processing

    Text Mining & Natural Language Processing

    • Text data generating sources
    • How to give structure to text structure using bag of words
    • Terminology used in text data analysis
    • DTM & TDM
    • TFIDF & its usage
    • Word cloud and its interpretation

    7. SQL

    Getting Started and Creating, Selecting & Retrieving Data with SQL

    • Introduction to Databases
    • How to create a Database instance on Cloud?
    • Provision a Cloud hosted Database instance.
    • What is SQL?
    • Thinking About Your Data
    • Relational vs. Transactional Models ER Diagram
    • CREATE Table Statement and DROP tables
    • UPDATE and DELETE Statements
    • Retrieving Data with a SELECT Statement
    • Creating Temporary Tables
    • Adding Comments to SQL

    Filtering, Sorting, and Calculating Data with SQL

    • Basics of Filtering with SQL
    • Advanced Filtering: IN, OR, and NOT
    • Using Wildcards in SQL
    • Sorting with ORDER BY
    • Math Operations
    • Aggregate Functions
    • Grouping Data with SQL

    Subqueries and Joins in SQL

    • Using Subqueries
    • Subquery Best Practices and Considerations
    • Joining Tables
    • Cartesian (Cross) Joins
    • Inner Joins
    • Aliases and Self Joins
    • Advanced Joins: Left, Right, and Full Outer Joins
    • Unions

    Modifying and Analyzing Data with SQL

    • Working with Text Strings
    • Working with Date and Time Strings
    • Date and Time Strings Examples
    • Case Statements
    • Views
    • Data Governance and Profiling
    • Using SQL for Data Science

    Accessing Databases using Python

    • How to access databases using Python?
    • Writing code using DB-API
    • Connecting to a database using DB API
    • Create Database Credentials
    • Connecting to a database instance
    • Creating tables, loading, inserting, data and querying data
    • Analysing data with Python

    8. Excel

    Analyzing and Visualizing Data using Excel

    • Input data & handling large spreadsheets
    • Tricks to get your work done faster
    • Automating data analysis (Excel VLOOKUP, IF Function, ROUND and more)
    • Transforming messy data into shape
    • Cleaning, Processing and Organizing large data
    • Spreadsheet design principles
    • Drop-down lists in Excel and adding data validation to the cells.
    • Creating Charts & Interactive reports with Excel Pivot Tables, PivotCharts, Slicers and Timelines
    • Functions like: - COUNTIFS, COUNT, SUMIFS, AVERAGE and many more.
    • Excel features: - Sort, Filter, Search & Replace Go to Special etc...
    • Importing and Transforming data (with Power Query)
    • Customize the Microsoft Excel interface
    • Formatting correctly for professional reports.
    • Commenting on cells.
    • Automate data entry with Autofill and Flash-fill.
    • Writing Excel formulas & referencing to other workbooks / worksheets.
    • Printing options
    • Charts beyond column and bar charts: - Pareto chart, Histogram, Treemap, Sunburst
    • charts & more

    9. TABLEAU

    Analyzing and Visualizing Data using Tableau

    • Introduction to Data Visualization
    • Tableau Introduction and Tableau Architecture
    • Exploring Data using Tableau
    • Working with Data using Tableau including Data Extraction and
    • Blending
    • Various Charts in Tableau(Basics to Advanced)
    • Sorting-Quick Sort, Sort from Axis, Legends, Axis, Sort by Fields
    • Filtering- Dimension Filters, Measure Filters, Date Filters, Tableau

    Program Highlights

    350+ Live sessions

    15+ Industry Projects

    Life time accessibility

    Live project experience

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    All Trainer

    About Us

    Machine learning is a field of artificial intelligence that focuses on giving computers the ability to learn and improve their performance based on data. The goal is to enable computers to automatically analyze data, identify patterns and make decisions without being explicitly programmed to do so. Machine learning algorithms are designed to improve over time as they are exposed to more data. Some examples of machine learning include recommendation systems, image and speech recognition, and self-driving cars.
    A machine learner's job is pretty cool, here are some key responsibilities:

    What is a machine learning course and why should you take one?

    Our machine learning course in Laxmi Nagar is a type of educational program that teaches individuals how to develop, train, and deploy machine learning models. It typically covers topics such as:

    • Supervised learning, unsupervised learning, and reinforcement learning.
    • Model selection, evaluation, and optimization techniques.
    • Mathematical foundations of machine learning, including linear algebra, probability, and statistics.
    • Data pre-processing and feature engineering.
    • Deep learning techniques such as neural networks, convolutional neural networks, and natural language processing.
    • Practical application of machine learning in domains such as computer vision, natural language processing, and predictive analytics.

    Basically, it's a one-stop shop for learning how to create intelligent machines that can make predictions, classifications, and recommendations based on data. Here are a few reasons why you should take machine learning training in Laxmi Nagar:

    • To develop new career opportunities: Machine learning is one of the fastest-growing and most in-demand fields in tech, with a wide range of career opportunities in industries such as healthcare, finance, manufacturing, and more.
    • To stay current: As machine learning continues to evolve and become more advanced, it's important to stay up-to-date with the latest techniques and tools.
    • To gain hands-on experience: A machine learning course typically includes hands-on exercises and projects, giving students the opportunity to apply what they've learned to real-world problems.
    • To build a portfolio: Completing a machine learning course and creating a portfolio of projects can help someone stand out in the job market and showcase their skills to potential employers.

    Why should you choose Edusoft's machine learning course?

    Edusoft's machine learning course in Laxmi Nagar, is a solid choice for a few reasons:

    • Edusoft's curriculum is designed to be practical and applied, with real-world case studies and projects to give students hands-on experience.
    • Edusoft's instructors are experienced industry professionals who bring real-world experience and insights to the course.
    • Edusoft's offers a flexible learning environment, with both online and in-person options to accommodate different learning styles and schedules.
    • Edusoft's course covers a wide range of machine learning techniques, from classical algorithms to deep learning, giving students a well-rounded understanding of the field.
    • The course is updated regularly to keep pace with the latest developments in the field, ensuring that students are learning the most relevant and cutting-edge techniques.
    • Students receive personalized feedback and support throughout the course, helping them to identify areas for improvement and make progress more quickly.
    • Edusoft's course is designed to be accessible to learners of all levels, from beginners to experienced professionals looking to deepen their understanding of machine learning.
    • Edusoft's offers a variety of learning resources, including video lectures, tutorials, code examples, and reading materials, providing students with a rich learning environment.

    So basically, Edusoft's machine learning training course in Laxmi Nagar offers a comprehensive and hands-on learning experience with real-world relevance and expert guidance.

    Assured advantages of our out-standing machine learning course

    Here are some machine learning course benefits in Laxmi Nagar, one can relish:

    • Confidence boost: After completing the course, students will feel confident in their machine learning skills and be ready to tackle real-world projects with ease.
    • Career advancement: The skills and knowledge gained from the course can help students advance in their current roles or pivot to a new career path in machine learning.
    • Problem-solving skills: The course emphasizes critical thinking and problem-solving, equipping students with the skills to identify and solve complex problems in a variety of contexts.
    • Collaboration and networking: The course provides opportunities to connect with peers and industry professionals, fostering collaboration and networking opportunities.
    • Industry recognition: Edusoft's machine learning course is recognized by industry leaders and is widely respected as a top-tier program.
    • Adaptability: Machine learning is constantly evolving, and our course prepares students to adapt to new technologies and techniques as they emerge.
    • Creativity: Machine learning fosters creativity, allowing students to explore new possibilities and approach problems from unique perspectives.
    • Lifelong learning: The course instills a passion for lifelong learning, inspiring students to continue learning and growing throughout their careers.

    What is the ideal course curriculum for machine learning course?

    A solid machine learning course curriculum in Laxmi Nagar might include:

    • Mathematical foundations: Linear algebra, calculus, probability theory, and statistics.
    • Algorithms: Supervised learning (linear regression, logistic regression, decision trees, support vector machines), unsupervised learning (k-means clustering, principal component analysis, autoencoders), and reinforcement learning.
    • Pre-processing techniques: Data cleaning, feature selection, and feature engineering.
    • Deep learning: Neural networks, convolutional neural networks, recurrent neural networks, and transfer learning.
    • Model deployment: How to deploy and maintain machine learning models in production environments.
    • Ethics: Discussion of the ethical implications of machine learning and best practices for responsible development and deployment.
    • Natural language processing: Techniques for processing and analyzing text data, such as tokenization, stemming, and sentiment analysis
    • Computer vision: Approaches for analyzing and understanding visual data, including object detection, image classification, and image segmentation.
    • Time series analysis: Techniques for analyzing and forecasting time-series data, such as trend detection, seasonality, and ARIMA modeling.
    • Explainability: How to understand and explain the decisions made by machine learning models, which is crucial for ensuring transparency and accountability.
    • Big data processing: How to handle large and complex data sets, including distributed computing, scalability, and streaming data.
    • Fairness and bias: Understanding and mitigating biases in machine learning models, including techniques such as adversarial training and fairness-aware algorithms.
    • Domain-specific machine learning: The application of machine learning to specialized domains, such as healthcare, finance, and autonomous vehicles.
    • Reinforcement learning: Techniques for training agents to take actions in an environment in order to maximize a reward, such as in games, robotics, and control systems.
    • Model optimization: Techniques for improving the efficiency, interpretability, and generalizability of machine learning models, including regularization, feature engineering, and model compression.

    Who is eligible for this machine learning course?

    Generally, anyone with a solid foundation in math, programming, and computer science concepts is a good fit for a machine learning course for beginners in Laxmi Nagar. Here are some more specific eligibility criteria:

    • Bachelor's degree in computer science, mathematics, statistics, or a related field.
    • Proficiency in programming languages like Python, R, or Java.
    • Familiarity with statistical concepts such as probability, regression, and hypothesis testing.
    • Basic knowledge of machine learning concepts like supervised and unsupervised learning, classification, and regression.
    • An interest in data analysis, data science, and artificial intelligence.

    These criteria are not strict requirements, and individuals from diverse backgrounds can excel in machine learning with the right mindset and a willingness to learn.

    Why is it beneficial to take a machine learning course?

    Let’s discuss some of the machine learning course benefits in Laxmi Nagar one can enjoy:

    • Machine learning is a rapidly growing field with immense potential for solving complex problems in various industries.
    • Machine learning skills are highly in demand and can lead to well-paid jobs in fields such as finance, healthcare, manufacturing, and marketing.
    • Machine learning can help you become more efficient and productive by automating tasks, making predictions, and identifying patterns in data.
    • Machine learning enables you to work with large and complex data sets, extract valuable insights, and make informed decisions based on data.
    • Machine learning can help you gain a deeper understanding of data, improve analytical skills, and develop critical thinking abilities.
    • Machine learning can help you build smarter and more intelligent systems, such as self-driving cars, virtual assistants, and predictive maintenance systems.
    • You'll gain a competitive edge in the job market by being proficient in a cutting-edge technology.
    • You'll become part of a community of like-minded professionals who share a passion for using data and algorithms to solve problems.
    • Machine learning can help you automate repetitive and time-consuming tasks, freeing up time for more creative and strategic work.
    • It's a fascinating field with endless opportunities for exploration and discovery!

    What incentives one can relish after finishing the course?

    The job opportunities for machine learning professionals are vast! Here are a few popular ones:

    • Machine Learning Engineer: Develops and deploys machine learning models for various applications.
    • Data Scientist: Analyzes and extracts insights from large and complex data sets using machine learning algorithms.
    • AI Researcher: Conducts research and experiments to advance the field of AI and machine learning.
    • Natural Language Processing (NLP) Engineer: Develops and trains algorithms to understand and analyze natural language data.
    • Computer Vision Engineer: Builds and trains algorithms to extract insights from visual data, such as images and videos.
    • Machine Learning Product Manager: Oversees the development and deployment of machine learning products and ensures they align with business goals and customer needs.
    • Machine Learning Consultant: Provides guidance and support to organizations looking to implement machine learning solutions.
    • Robotics Engineer: Develops and implements machine learning algorithms for robotic systems, such as self-driving cars and industrial robots.
    • Healthcare Data Scientist: Uses machine learning to analyze medical data and develop predictive models for improving patient outcomes and healthcare systems.
    • FinTech Data Scientist: Uses machine learning to analyze financial data and develop algorithms for risk management, fraud detection, and investment decision-making.
    • Cybersecurity Analyst: Uses machine learning algorithms to detect and prevent cyber threats and security breaches.
    • Supply Chain Optimization Specialist: Uses machine learning to optimize logistics and supply chain operations, reducing costs and increasing efficiency.
    • Social Media Analyst: Uses machine learning to analyze social media data and insights, helping organizations better understand their customers and improve their social media strategies.
    • Marketing Analytics Specialist: Applies machine learning algorithms to analyze customer data and improve marketing campaigns, increasing conversion rates and ROI.
    • Human Resources Analytics Specialist: Uses machine learning to analyze employee data and improve hiring, retention, and performance management.

    Perks of getting a machine learning course

    There are many benefits to our machine learning certification course in Laxmi Nagar, kindly go through the below mentioned bullets to get the finest idea.

    • A comprehensive and high-quality course curriculum.
    • You'll have the opportunity to communicate with industry experts to create a personalized curriculum tailored to your needs and interests.
    • Gain valuable real-world experience with 15+ industry projects across 6+ different domains.
    • Our live interactive sessions and recorded videos give you the flexibility to learn at your own pace.
    • This course is designed to be completed in just five months, but you'll have lifetime access to the materials and resources.
    • Our simulated interview sessions help you prepare for the real world and give you the confidence to succeed in a job interview.
    • You'll earn two certifications - one from Microsoft and one from a leading company. These certifications will validate your skills and give you a competitive edge in the job market.
    • You'll receive guaranteed job referrals to help you land your dream job.
    • You can choose between an online or an offline course, depending on your preferences and availability.
    • This course is designed to be completed in just five months, but you'll have lifetime access to the materials and resources.

    What core skills and projects will be focused upon in this machine learning course?

    The core skills and projects covered in an advanced machine learning course in Laxmi Nagar include:

    • Data preprocessing: cleaning, transforming, and organizing data to make it suitable for machine learning algorithms.
    • Exploratory data analysis: using visualizations and statistical methods to identify patterns and relationships in data.
    • Model selection: choosing the right machine learning algorithm for a given problem.
    • Hyperparameter tuning: optimizing the performance of machine learning models by adjusting hyperparameters such as learning rate and regularization.
    • Model evaluation: assessing the performance of machine learning models using metrics such as accuracy, precision, recall, and F1 score.
    • Model deployment: integrating machine learning models into production systems.
    • Cross-validation: evaluating the generalizability of machine learning models by splitting the data into training and validation sets.
    • Feature engineering: transforming raw data into meaningful features that can improve model performance.
    • Dimensionality reduction: reducing the number of features in a data set to improve model performance and reduce computational complexity.
    • Ensemble methods: combining multiple machine learning models to improve performance.
    • Unsupervised learning: identifying patterns in unlabeled data and clustering similar data points.
    • Reinforcement learning: training agents to make decisions in an environment through trial and error and reward feedback.

    How long this machine learning course will stretch?

    Machine learning course duration in Laxmi Nagar is 4-months along with a special provision of special batches where the students who want to finish this course as soon as possible and want to maintain work life balance can go for this particular option:

    • A 4-month machine learning course would likely cover the fundamentals of machine learning algorithms, such as linear and logistic regression, decision trees, and support vector machines.
    • The course may also touch upon more advanced topics such as natural language processing, computer vision, and deep learning.
    • You can expect to learn about data preprocessing, model evaluation, and model deployment during the 4-month course.
    • The course will likely require you to complete projects and assignments to solidify your understanding of machine learning concepts.

    How much a machine learning course cost?

    Machine learning course fees in Laxmi Nagar is ₹30K excluding the special offers:

    • Such a course could provide you with a solid foundation in machine learning, covering core concepts and techniques.
    • The course might include lecture videos, readings, and interactive exercises to help you practice and apply what you learn.
    • At this price point, the course might not include personalized mentorship or one-on-one support, but it could offer access to a community of other learners and instructors for discussion and collaboration.
    • It's likely that the course will cover practical applications of machine learning in various industries such as finance, healthcare, and retail.

    Prediction of the future of machine learning industry

    Here's what some experts are saying about the future of machine learning:

    • Increased adoption in healthcare: ML will be used to improve diagnosis, treatment, and disease prediction.
    • Autonomous vehicles: ML will enable self-driving cars to navigate safely and efficiently.
    • Natural language processing: ML will improve language translation, speech recognition, and chatbot development.
    • Cybersecurity: ML will play a key role in identifying and preventing cyber attacks.
    • Financial services: ML will be used for fraud detection, investment analysis, and risk management.
    • Personalization: ML will enable more personalized products and services, such as personalized medicine and personalized education.
    • Smart cities: ML will be used to optimize transportation, energy usage, and public services in smart cities.
    • Manufacturing: ML will be used to optimize production processes, predict maintenance needs, and improve quality control.
    • Entertainment: ML will be used to create more immersive gaming and virtual reality experiences, and to personalize content recommendations.
    • Robotics: ML will enable robots to perform more complex tasks, such as assembly, inspection, and logistics.
    • Agriculture: ML will be used to optimize crop yield, improve soil health, and reduce water usage.
    • Aerospace: ML will be used to optimize aircraft design, improve navigation and autonomous flight, and monitor aircraft performance.
    • Retail: ML will be used for personalized product recommendations, inventory management, and demand forecasting.
    • Energy: ML will be used to optimize energy production and distribution, and to improve the efficiency of renewable energy sources.
    • Healthcare: ML will be used for drug discovery, medical imaging analysis, and disease diagnosis.
    • Government: ML will be used for predictive policing, disaster response, and public health management.
    • Education: ML will be used to personalize learning and provide real-time feedback to students.
    • Law: ML will be used for legal research, contract analysis, and predictive litigation.
    • Transportation: ML will be used to optimize traffic flow, improve safety, and manage autonomous vehicles.
    • Insurance: ML will be used to improve underwriting, claims processing, and risk assessment.
    • Climate change: ML will be used to predict extreme weather events, monitor carbon emissions, and optimize renewable energy sources
    • Gaming: ML will be used for game design, character development, and interactive gameplay.
    • Construction: ML will be used for site planning, predictive maintenance, and safety monitoring.
    • Art and design: ML will be used to create digital art, automate design tasks, and predict consumer preferences.
    • Cybersecurity: ML will be used to detect and respond to cyber threats, including phishing, malware, and data breaches.
    • Space exploration: ML will be used to analyze data from space missions, plan spacecraft trajectories, and monitor astronaut health.

    How distinctive is to get the online machine learning course than an offline setting?

    Fair point! Here are some key distinctions between the online machine learning course in Laxmi Nagar versus machine learning classes in Laxmi Nagar:

    • Physical location: Online courses are virtual, while in-person classes require students to be physically present in a classroom setting.
    • Instructor interaction: In-person classes allow for direct interaction between students and instructors, while online courses may rely more on asynchronous communication or pre-recorded lectures.
    • Peer interaction: In-person classes provide opportunities for in-person collaboration and networking, while online courses may offer virtual collaboration tools but lack the same level of personal interaction.
    • Assessment methods: Online courses may use automated grading tools or online exams, while in-person classes may have more traditional methods such as written exams, presentations, or group projects.
    • Technology requirements: Online courses require a reliable internet connection and a device with appropriate hardware and software, while in-person classes may only require a pen and paper.
    • Flexibility: Online courses can be taken at any time of day, while in-person classes are typically scheduled at specific times.
    • Cost of materials: Online courses may require digital materials, such as e-books or online resources, while in-person classes may require physical textbooks or other materials.
    • Cultural experience: In-person classes may provide opportunities to interact with a diverse group of people and experience different cultures, while online courses may lack this type of exposure.

    How huge the hunger is there in India for machine learning course?

    The demand for machine learning courses in India is huge! Here's why:

    • The country has a large and fast-growing tech sector that is heavily focused on innovation and technological advancement, which creates a high demand for skilled professionals in fields like machine learning.
    • The government has also made significant investments in AI and machine learning, creating new opportunities for professionals in the field.
    • India has a large pool of talented and tech-savvy youth who are interested in pursuing careers in machine learning and related fields.
    • With the rise of big data, there's a growing demand for professionals who can analyze and extract insights from large datasets using machine learning algorithms.
    • The growth of the e-commerce industry in India has led to an increased demand for machine learning experts who can improve customer experience and personalization.
    • The adoption of machine learning in healthcare, finance, and other industries is driving demand for skilled professionals in these fields.
    • India's demographic dividend - its large and young population - makes it a prime location for machine learning talent development.
    • The rise of automation and robotics is driving demand for machine learning experts who can develop algorithms for autonomous systems.

    How much a machine learner can earn nationally and internationally?

    Nationally, machine learning engineers in India can earn anywhere from $20,000 to $100,000 annually, depending on their skills, experience, and location.
    Internationally, machine learning engineers can earn even more, with salaries ranging from $100,000 to $200,000 or more in countries like the United States, Canada, and the United Kingdom.
    The specific salary range depends on factors such as the company size, industry, and job responsibilities, as well as the employee's level of education, skills, and experience.
    Machine learning experts who have advanced degrees and specialize in specific areas, like natural language processing or computer vision, can command higher salaries.

    What machine learning course is all about?

    Machine learning courses syllabus in Laxmi Nagar, covers a wide range of topics, but here are some of the key areas:

    • Mathematics: This includes linear algebra, calculus, probability, and statistics, which are used to understand data and make predictions.
    • Algorithms: Students learn different algorithms like regression, decision trees, and neural networks, and how to use them to train and test machine learning models.
    • Data preprocessing: This includes techniques like data cleaning, feature engineering, and feature selection to transform raw data into a format that's suitable for machine learning models.
    • Model evaluation: This includes using metrics like accuracy, precision, recall, and F1 score to measure the performance of machine learning models and improve them.
    • Machine learning frameworks: These include libraries like TensorFlow, PyTorch, and scikit-learn that provide pre-built tools and algorithms to make machine learning easier.

    Benefits of getting the machine learning course

    There are several benefits to getting a machine learning course, including:

    • Increased job opportunities in the growing field of machine learning and artificial intelligence.
    • Higher salary potential in machine learning roles compared to other fields in the tech industry.
    • The ability to apply machine learning techniques to real-world problems and create impactful solutions.
    • Enhanced data analysis skills and the ability to extract valuable insights from large and complex data sets.
    • Improved critical thinking and problem-solving skills, as well as the ability to think creatively and analytically.
    • Opportunities for lifelong learning and professional growth, as the field of machine learning continues to evolve and new technologies emerge.

    How huge is the scope for machine learning course?

    Internationally- the scope for machine learning is massive. Some estimates suggest that the global machine learning market size will reach over $209 billion by 2028. The demand for machine learning expertise is only going to increase as more and more industries begin to adopt machine learning techniques to improve efficiency, reduce costs, and enhance customer experience. The rise of big data, the Internet of Things (IoT), and cloud computing is creating new opportunities for machine learning to be applied in a wide range of applications, from self-driving cars to personalized medicine. Overall, the future of machine learning looks bright, with plenty of opportunities for those who have the skills and knowledge to make the most of this exciting field.
    Here's a quick rundown of the machine learning scope in India:

    • India has a large and growing pool of talented machine learning professionals, with many engineers and data scientists having expertise in the field.
    • The Indian government has recognized the potential of machine learning and has launched initiatives to promote its adoption in various sectors, including healthcare, agriculture, and transportation.
    • Indian startups and companies are increasingly investing in machine learning, creating new job opportunities for professionals in the field.
    • There is a growing demand for machine learning in the e-commerce and retail sectors in India, as companies look to improve their recommendation systems and personalize the customer experience.
    • Overall, the future of machine learning in India looks bright, with many opportunities for professionals to contribute to this rapidly evolving field.
    • The use of machine learning in healthcare is on the rise in India, with applications in areas such as medical imaging, disease diagnosis, and drug discovery.
    • The Indian government has launched several initiatives to promote the use of machine learning in agriculture, including the development of precision farming techniques and crop yield prediction.
    • Machine learning is being used in the financial services sector to detect fraud, identify anomalies, and make better investment decisions.
    • - The Indian government has also launched the National Artificial Intelligence Portal, which is designed to promote the development and adoption of AI and machine learning technologies in India.

    How to get admission in the machine learning course?

    Edusoft's offers a cutting-edge machine learning course in Laxmi Nagar, and the admission procedure is designed to be both swift and professional:

    • Potential students can submit their application through the institution's online portal.
    • Subsequent to submission, an admissions representative will arrange a screening call with the applicant to appraise their qualifications and suitability for the program.
    • Following the call, the candidate will be requested to provide their transcripts, resume, and any additional pertinent materials for review.
    • The admissions committee will meticulously assess the provided materials and make a final decision.
    • Selected candidates will be notified of their admission and can begin their journey toward becoming proficient machine learners.

    Edusoft's offers a cutting-edge machine learning course in Laxmi Nagar, and the admission procedure is designed to be both swift and professional:

    Frequently Asked Questions

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    Frequently Asked Questions

    Edusoft's is a great option for Machine Learning in Delhi. We offer an online and in person learning platform that covers a range of topics, including Machine Learning, Artificial Intelligence, Robotics, Coding, and more. We also provide academic support for schools and offer workshops for teachers. Our approach is to make learning fun and interactive, which can help you learn more effectively and retain the knowledge for longer. Overall, Edusoft's is a great option for anyone looking to learn Machine Learning in Delhi.

    The cost of the Edusoft's Machine Learning course is around 30,000 Indian Rupees (or about $400 USD). This may seem like a lot, but it's actually quite reasonable when you consider the quality of the course and the expertise of the instructors. You'll be learning from some of the best in the business, and you'll be equipped with the knowledge and skills you need to succeed in the field of Machine Learning. Plus, the gamified learning experience is sure to make the journey fun and engaging!

    The demand for Machine Learning professionals in Delhi is on the rise! Here are some job trends to watch out for:

    • The demand for ML professionals is expected to increase by 60% by 2022.
    • Companies in various industries are investing in ML, including healthcare, finance, retail, and more.
    • Roles like ML engineers, data scientists, AI developers, and NLP specialists are in high demand.
    • The salaries for ML professionals in Delhi are also quite competitive, with an average salary of around ₹15 lakhs per year.

    Completing a Machine Learning course can take anywhere from a few weeks to several months, depending on the level of experience you have and the intensity of the course. Here's a general timeline:

    • For beginners with little to no programming experience, a basic Machine Learning course may take around 4-6 weeks, with a focus on the fundamentals.
    • For those with some programming experience, an intermediate course may take around 3-4 months, with a focus on more advanced techniques like deep learning and neural networks.
    • For experienced programmers looking to specialize in Machine Learning, a more in-depth course could take 6 months or more, covering cutting-edge techniques like reinforcement learning and generative models.

    You bet it is! Machine Learning is only becoming more and more important, and in 2023, it's going to be in high demand across a wide range of industries. Here are some of the reasons why:

    • As data continues to grow at an unprecedented rate, the need for Machine Learning algorithms to process and analyze that data will increase.
    • With the rise of automation and the increasing adoption of smart devices, there will be a growing need for ML algorithms to power these technologies.
    • The healthcare industry is increasingly using ML to improve diagnosis and treatment of diseases.
    • Financial institutions are using ML to detect fraud and improve risk management.

    In short, ML is on the rise, and the future looks bright for those with the skills to work with it.

    Anyone with a background in math, computer science, or statistics can be eligible for a Machine Learning course. Here are some common qualifications:

    • A degree in computer science, mathematics, engineering, or a related field.
    • Proficiency in programming languages like Python, R, or Java.
    • Knowledge of statistics and probability theory.
    • Experience with data analysis and machine learning algorithms.
    • Familiarity with data visualization tools like matplotlib, seaborn, or ggplot2.
    • Strong analytical and problem-solving skills.

    However, some beginner-level courses may not require all of these qualifications and can be a great starting point for those with a basic knowledge of programming and mathematics.

    Edusoft's is a great choice for learning Machine Learning in Delhi! Here's what you can expect from their course:

    • A hands-on approach to learning, with practical assignments and projects to apply your new skills.
    • Experienced instructors who are passionate about the field and are dedicated to helping you succeed.
    • A flexible schedule, with online and in-person options to fit your needs.
    • A comprehensive curriculum that covers all the essential topics in Machine Learning, from basic concepts to advanced techniques.
    • A supportive learning environment, with access to mentors, tutors, and a community of fellow learners.

    With Edusoft's, you'll not only gain new skills, but also build confidence and expertise in this exciting and in-demand field.

    The syllabus of a Machine Learning course provided by Edusoft's covers the following topics:

    • Introduction to Machine Learning
    • Data preprocessing and feature engineering
    • Supervised learning algorithms (like linear regression, logistic regression, decision trees, and random forests)
    • Unsupervised learning algorithms (like k-means clustering, principal component analysis, and neural networks)
    • Natural Language Processing (NLP) techniques (like text classification, sentiment analysis, and named entity recognition)
    • Reinforcement learning techniques (like Q-learning and policy gradients)
    • Deep learning frameworks (like TensorFlow and PyTorch)
    • Model deployment and scaling (like model serving and distributed training)

    This is just a general overview, but you can expect most courses to cover these core topics!

    If you're looking for a top-notch Machine Learning course, Edusoft's is definitely worth considering. Our course covers all the core topics in Machine Learning, from the basics of supervised and unsupervised learning to advanced techniques like deep learning and neural networks. The instructors are experienced and knowledgeable, and they do a great job of breaking down complex concepts in a way that's easy to understand. The course is also well-structured and the assignments are challenging but not overwhelming. Overall, Edusoft's Machine Learning course is one of the best out there, and it's definitely worth checking out if you're serious about learning this exciting field!

    Machine Learning can be both easy and challenging, depending on your background and experience level. If you have a solid foundation in mathematics and programming, you'll likely find the concepts and techniques relatively straightforward. However, if you're new to math and programming, you'll need to spend some time getting up to speed before diving into the meat of the course. That said, the concepts in Machine Learning can be complex and require a fair amount of practice and hands-on work to really master them. So while it may not be "easy," with the right attitude and effort, you can definitely learn and excel in Machine Learning.

    In India, the demand for Machine Learning professionals is skyrocketing. The country has a growing tech ecosystem and is home to many leading tech companies and start-ups that are investing heavily in AI and ML. The average salary for a Machine Learning Engineer in India is around ₹12-15 lakhs per year, but can go much higher for experienced professionals with specialized skills. Generative AI, in particular, is an emerging area with high demand and lucrative salaries, with some companies offering up to ₹1 crore for top talent. The demand for Machine Learning professionals is expected to continue growing in the years to come, so it's definitely a promising field to consider in India.

    Here are a few reasons why Machine Learning is a great choice for a career:

    • It's a high-growth field with lots of job opportunities.
    • It's challenging and intellectually stimulating, with plenty of opportunities to learn and grow.
    • It's well-paid and provides good job security.
    • It's a versatile field with applications in various industries, including healthcare, finance, e-commerce, and robotics.
    • It's a dynamic field with new technologies and advancements happening all the time.

    Basically, if you're looking for a career that's both exciting and rewarding, Machine Learning is a pretty stellar choice!

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