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Machine Learning Course

80,146 Ratings

Advance your career with this comprehensive Machine Learning Course to become a Certified Machine Learning Engineer.

  • Get trained by eminent IIT Faculty and Industry Experts
  • Master Python, Machine Learning Algorithms, Statistics, and AI skills via hands-on projects and case studies
  • Learn Machine Learning Algorithms, Generative AI, prompt engineering, and ChatGPT
  • 3 Guaranteed Interviews upon movement to the Placement Pool
  • Machine Learning Certification by Intellipaat (Recognized across 1000+ MNCs)

Ranked #1 Data Science Program by India TV

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Key Features

50+ Live sessions across 7 months
218 Hrs Self-paced Videos
200 Hrs Project & Exercises
Learn from Top Industry Practitioners
1:1 with Industry Mentors
Resume Preparation and LinkedIn Profile Review
24*7 Support
No-cost EMI Option
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Machine Learning Online Course Overview

What is Machine Learning?

Machine Learning is the process of collecting real-world data, extracting useful information from it, and then taking action to perform specific tasks without manual programming. It helps systems improve over time on their own by exploring various types of real-world data. It also allows organizations to enhance their business strategies by knowing the insights extracted from the given business data.

As part of this Machine Learning training, you will master the skills mentioned below, and you will become a successful Machine Learning Engineer:

  1. Microsoft Excel for data analysis and data transformation
  2. Techniques of evaluation, experimentation, project life cycle, and deployment
  3. Analysis segmentation using clustering and the method of prediction
  4. Machine Learning algorithms
  5. Data science at scale with PySpark, AI with TensorFlow
  6. Deploying Machine Learning models on clouds (MLOps)
  7. Data visualization with Power BI and Python
  8. Natural language processing and its applications

Experts have designed our ML course so that anyone, from a fresher/undergraduate or undergrad to a professional seeking a career change, can take it and benefit from it.

No, there are no mandatory prerequisites for participating in the Machine Learning Course program. While the training covers all the essential aspects of Machine Learning, having prior experience in this field can certainly be an advantage during this Machine Learning course.

Machine Learning is one of the most sought-after courses by data companies globally, owing to the immense pace at which the world is shifting towards AI and automation.
Here are a few essential pointers that may make you think seriously about ML:

Metric India USA
Number of ML Jobs (LinkedIn) 13000+ 60,000
Average Income (Annual) ₹ 2000000 $ 15888
Growth Rate for ML Jobs (Approximate) 350% 350%
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Machine Learning engineers rank among the top emerging jobs. - LinkedIn
The global Machine Learning market is expected to reach USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% - MarketsandMarkets

Machine Learning Career Transitions

57% Average Salary Hike

$1,14,000 Highest Salary

12000+ Career Transitions

300+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Meet the Machine Learning Mentors

Skills Covered under this Machine Learning Course

Python

Data Science

Data Analysis

AI

GIT

MLOps

Data Wrangling

SQL

Story Telling

Machine Learning

Prediction algorithms

NLP

PySpark

Model

Data visualization

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Machine Learning Tools Covered

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Machine Learning Course Fees

Online Classroom Preferred

  • Everything in Self-Paced Learning, plus
  • 50+ Live sessions across 7 months of Instructor-led Training
  • One to one doubt resolution sessions
  • Attend as many batches as you want for Lifetime
  • Job Assistance
25 May

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

01 Jun

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

08 Jun

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

15 Jun

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

$1,229 10% OFF Expires in

Corporate Training

  • Customized Learning
  • Enterprise Grade Learning Management System (LMS)
  • 24x7 Support
  • Enterprise Grade Reporting

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Machine Learning Course Syllabus

Live Course Industry Expert Academic Faculty

Module 1 – Preparatory Session - Python

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Python 

  • Introduction to Python and IDEs – The basics of the Python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 
  • Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
  • Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  • Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding. 
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Module 2 – Data Wrangling with SQL

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SQL Basics – 

  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables 

Advanced SQL –  

  • SQL Functions, Subqueries, Rules, Views
  • Nested Queries, string functions, pattern matching
  • Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

  • Types of UDFs, Inline table value, multi-statement table. 
  • Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimization and Performance

  • Record grouping, searching, sorting, etc. 
  • Clustered indexes, common table expressions.

Hands-on exercise: 

Writing comparison data between the past year and the present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

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Extract Transform Load

  • Web Scraping, Interacting with APIs

Data Handling with NumPy

  • NumPy Arrays, CRUD Operations, etc.
  • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

  • Loading the data, data frames, series, CRUD operations, splitting the data, etc.

Data Preprocessing

  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross-validation techniques, train-test split, etc.

Data Visualization

  • Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
  • Regression plots, categorical plots, area plots, etc, with Python seaborn.
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Descriptive Statistics – 

  • Measure of central tendency, the measure of spread, five points summary, etc. 

Probability 

  • Probability Distributions, Bayes’ theorem, central limit theorem. 

Inferential Statistics –  

  • Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.
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Introduction to Machine Learning 

  • Supervised, Unsupervised Learning.
  • Introduction to scikit-learn, Keras, etc.

Regression 

  • Introduction classification problems, Identification of a regression problem, dependent and independent variables.
  • How to train the model in a regression problem.
  • How to evaluate the model for a regression problem.
  • How to optimize the efficiency of the regression model.

Classification 

  • Introduction to classification problems, Identification of a classification problem, and dependent and independent variables.
  • How to train the model in a classification problem.
  • How to evaluate the model for a classification problem.
  • How to optimize the efficiency of the classification model.

Clustering 

  • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
  • How to train the model in a clustering problem.
  • How to evaluate the model for a clustering problem.
  • How to optimize the efficiency of the clustering model.
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Supervised Learning 

  • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
  • Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach.
  • Support Vector Machine – SVM or support vector machines for regression and classification problems.
  • Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding the local minimum and maximum of a given function.
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

Unsupervised Learning 

  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
  • Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
  • Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
  • Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.
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Artificial Intelligence Basics 

  • Introduction to keras API and TensorFlow

Neural Networks

  • Neural networks
  • Multi-layered Neural Networks
  • Artificial Neural Networks 

Deep Learning

  • Introduction to Deep Learning
  • Deep neural networks
  • Convolutional Neural Networks 
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders, Restricted Boltzmann machine
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The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals: 

  • Extracting, loading and transforming data into usable format to gather insights. 
  • Data manipulation and handling to pre-process the data.
  • Feature engineering and scaling the data for various problem statements. 
  • Model selection and model building on various classification, regression problems using supervised/unsupervised Machine Learning algorithms.
  • Assessment and monitoring of the model created using the Machine Learning models.
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Electives:

Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX 

  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics  

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity
  • Embedded Power BI API and Power BI Mobile
  • Power BI Advance and Power BI Premium

Hands-on Exercise:

Creating a dashboard to depict actionable insights in sales data.

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  • Job Search Strategy
  • Resume Building
  • Linkedin Profile Creation
  • Interview Preparation Sessions by Industry Experts
  • Mock Interviews
  • Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.
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Excel Fundamentals 

  • Reading the Data, Referencing in formulae , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
  • Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security
  • VBA Macros, Ranges and Worksheet in VBA
  • IF conditions, loops, Debugging, etc.

Excel For Data Analytics 

  • Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.

Data Visualization with Excel

  • Charts, Pie charts, Scatter and bubble charts
  • Bar charts, Column charts, Line charts, Maps
  • Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Excel Power Tools 

  • Power Pivot, Power Query and Power View

Classification Problems using Excel

  • Binary Classification Problems, Confusion Matrix, AUC and ROC curve
  • Multiple Classification Problems  

Information Measure in Excel

  • Probability, Entropy, Dependence
  • Mutual Information

Regression Problems Using Excel

  • Standardization, Normalization, Probability Distributions
  • Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
  • Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

Hands-on Exercise:

Classification problem using excel on sales data, and statistical tests on various samples from the population.

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Machine Learning Projects

Career Services

resume

Career Oriented Sessions

Throughout the course

Over 10+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions that will help you stay on track with your upskilling.

linkedin

Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & Linkedin Profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage

interview

Mock Interview Preparation

After 80% of the course completion.

Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

expert

1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learner’s educational background, past experience, and future career aspirations.

guaranteed

Placement Assistance

Upon movement to the Placement Pool

Placement opportunities are provided once the learner is moved to the placement pool upon clearing Placement Readiness Test (PRT)

job_portal

Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Machine Learning Certification

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What should I do to unlock my Machine Learning Course Certification?

You can earn this Machine Learning Course Certification once you:

  • Complete the Machine Learning Online Course Videos in the LMS and score at least 60% in the quiz conducted by Intellipaat if you have chosen our self-paced Machine Learning course.
  • Complete all the assigned projects and score 60% or more in the quiz if you have opted for the live instructor-led Machine Learning Course online

With successful online ML course completion and after securing the desired percentage, you will get the certificate via our learning management system. You can download the certificate and use it for future endeavors.

Yes, these Machine Learning Courses certification is recognized worldwide by more than 500 MNCs, including Sony, IBM, Infosys, Mu Sigma, Hexaware, Cisco, Standard Chartered Bank, Ericsson, and many others.

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Machine Learning Course Reviews

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Machine Learning Course FAQs

Why should I join this Machine Learning Online Course at Intellipaat?

Machine Learning Courses, offers hands-on projects and in-depth Machine Learning training covering ML concepts, Python, classification, linear regression, and more. Get a valued certificate, real-world projects, and lifetime access to resources. Elevate your career with 24/7 support.

Some top sectors for AI and Machine Learning professionals include technology, finance, healthcare, retail, automotive, marketing, manufacturing, and energy. These sectors utilize AI for various purposes like optimization, prediction, and automation to stay competitive, and deliver more customized services.

Yes. Intellipaat provides practice tests for all learners to help them get familiar with the format of the exam and the types of questions asked in Machine Learning Course examination.

Here are some of the top job roles that you can consider after completing Intellipaat’s best ML courses online:

  • Machine Learning Engineer
  • Human-centered Machine Learning Designer
  • Data Scientist
  • Business Intelligence Developer
  • NLP Scientist

A little bit of coding skill is essential for a Machine Learning Course. You should look into OOP concepts, data structures, and algorithms, which will all be covered in this Machine learning online course by Intellipaat. Some of the popular programming languages used in Machine Learning are R, Python, C++, and Java.

The duration of an online Machine Learning Course typically takes seven months of instructor-led training.

Intellipaat’s Machine Learning course, developed by industry experts, which includes both basic and advanced concepts of some popular technology. Here, the learners also learn by using real-life examples, which cover topics like mathematics, statistics, and more. Perfect for beginners aspiring for a career in ML. So, if you are aspiring to become one, and new in this field, this is the best course for you.

A Machine Learning Course provides complete training in Machine Learning concepts, covering data preprocessing, feature engineering, supervised and unsupervised learning, deep learning, and model evaluation. It also includes practical projects for hands-on experience.

We encourage you to explore the fee section for specific details for the cost for the Machine Learning online course at Intellipaat. Additionally, feel free to speak with our academic counselors to discuss scholarship opportunities and flexible EMI options available for enrollment.

Intellipaat’s teaching assistant team consists of technical experts who help learners in various aspects, including doubt clearance, assignment sessions, and project evaluation in this Machine Learning Course.

For more information about the Machine Learning Course, you can contact us at

  • IND: +91-7022374614
  • US: 1-800-216-8930 (Toll-Free)

Intellipaat has a strong network of top hiring companies along with strong alumni network. We provide career services that include mock interviews, resume building and LinkedIn profile development. Also, our dedicated job portal connects you with over 500 recruiters, including top companies like Amazon, Flipkart, etc. offering Machine Learning job opportunities.

Yes, Intellipaat offers several group discounts for Machine Learning Course, depending on the group size and type. To avail of the group discount for our Machine Learning advanced course, you need to contact our course advisors, who will help you with all your doubts regarding the discount offers we provide.

Intellipaat offers the facility of integrated labs that act as a platform for you to execute our industry-based Machine Learning Course projects. You will be guided through the steps to quickly deploy all the necessary tools and further complete the hands-on exercises successfully.

This Machine Learning Course Certification is recognized by over 500 top MNCs worldwide, including companies such as Sony, Mu Sigma, Cisco, IBM, Standard Chartered Bank, TCS, Infosys, Ericsson, Genpact, Cognizant, etc.

Intellipaat offers query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail yourself of email support for all your queries. We can also arrange one-on-one sessions with our support team If your query does not get resolved through email. However, 1:1 session support is given for 6 months from the start date of your course.

Intellipaat provides placement assistance to all learners who have completed the training and moved to the placement pool after clearing the PRT (Placement Readiness Test). More than 500+ top MNCs and startups hire Intellipaat learners. Our alumni work with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.

Apparently, no. Our job assistance is aimed at helping you land your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final hiring decision will always be based on your performance in the interview and the requirements of the recruiter.

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