Machine Learning Certification Course in Delhi, India

2603 Learners

Simplilearn’s Machine Learning course in Bangalore will make you an expert in machine learning, a form of artificial intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will learn introduction to machine learning and Master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer.

Key features

MONEY BACK GUARANTEE

44 hours of instructor-led training with certification
Gain expertise with 25+ hands-on exercises
4 real-life Industry projects with integrated labs
Lifetime access to self-paced learning
Dedicated mentoring session from our faculty of industry experts
24*7 support with dedicated project mentoring sessions

For individuals

Self-Paced Learning

Lifetime access to high-quality self-paced e-learning content curated by industry experts
24x7 learner assistance and support

₹ {{osl_price|number}} ₹ ({{osl_disc}}% off)

₹ 18,999

Online Classroom Flexi-Pass

What is Flexi Pass?

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90 days of flexible access to instructor-led online training classes in Delhi

Lifetime access to high-quality self-paced e-learning content and live class recordings
24x7 learner assistance and support

Weekend class starting 22nd Jun
Weekend class starting 29th Jun

Show all classes View recording of previous class

For business

Corporate Training Solutions

Blended learning delivery model (self-paced eLearning and/or instructor-led options)
Flexible pricing options
Enterprise grade Learning Management System (LMS)
Enterprise dashboards for individuals and teams
24x7 learner assistance and support

Course description

Why Machine Learning Certification Course?
- Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning
- The machine learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period
What are the objectives of this course?

A form of artificial intelligence, machine learning is revolutionizing the world of computing as well as all people’s digital interactions. By making it possible to quickly, cheaply and automatically process and analyze huge volumes of complex data, machine learning is critical to countless new and future applications. Machine learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.

This Machine Learning online course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in machine learning. The demand for machine learning skills is growing quickly. The median salary of a Machine Learning Engineer is $134,293 (USD), according to payscale.com.
What skills will you learn with our Machine Learning Course?
By the end of this Machine Learning course, you will be able to accomplish the following:
- Introduction to Machine Learning, Real-time Applications
- Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
- Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
- Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
- Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
- Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.
- Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems
Who should take this Machine Learning Training Course?
There is an increasing demand for skilled machine learning engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning training course for the following professionals in particular:
- Developers aspiring to be a data scientist or machine learning engineer
- Analytics managers who are leading a team of analysts
- Business analysts who want to understand data science techniques
- Information architects who want to gain expertise in machine learning algorithms
- Analytics professionals who want to work in machine learning or artificial intelligence
- Graduates looking to build a career in data science and machine learning
- Experienced professionals who would like to harness machine learning in their fields to get more insights
What projects are included in this Machine Learning Training Course?

Simplilearn's Machine Learning Training course is very hands-on and code-driven. The theoretical motivation and Mathematical problem formulation must be provided only when introducing concepts.

This course consists of one primary capstone project and 25+ ancillary exercises based on 17 machine learning algorithms.

Capstone Project Details:
Project Name: Predicting house prices in California
Description: The project involves building a model that predicts median house values in Californian districts.You will be given metrics such as population, median income, median housing price and so on for each block group in California.Block groups are the smallest geographical unit for which the US Census Bureau publishes sample data (a lock group typically has a population of 600 to 3,000 people).The model you build should learn from this data and be able to predict the median housing price in any district.

Concept covered: Techniques of Machine Learning

Case Study 1: Predict whether consumers will buy houses or not, from the given dataset,

provided with their age and salary

Project 1: What issues do you see in the plot produced by the code in reference to the above problem statement?

Project 2: What are the approximate prices of the houses with areas 1700 and 1900?

Concept covered: Data Preprocessing

Case Study 2: Demonstrate methods to handle missing data, categorical data, and data standardization using the information provided in the dataset

Project 3: Review the training dataset (Excel file). Note that weight is missing for the fifth and eighth rows.What are the values computed by the imputer for these two missing rows?

Project 4: In the tutorial code, find the call to the Imputer class. Replace strategy parameter from “mean” to “median” and execute it again. What is the new value assigned to the blank fields Weight and Height for the two rows?

Project 5: In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as before?

Case Study 3: Demonstrate how to reduce data dimensions from 3D to 2D using the information provided

Project 6: What does the hyperplane shadow represent in the PCA output chart on random data?

Project 7: What is the reconstruction error after PCA transformation? Give interpretation.

Concept Covered: Regression

Case Study 4: Demonstrate how to reduce data dimensions from 3D to 2D using the information provided

Project 8: Modify the degree of the polynomial from Polynomial Features (degree = 1) to 1, 2, 3, and interpret the resulting regression plot. Specify if it is under fitted, right-fitted, or overfitted?

Project 9: Predict the insurance claims for age 70 with polynomial regression n with degree 2 and linear regression.

Project 10: In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as before?

Case Study 5: Predict insurance premium per year based on a person’s age using Decision Trees using the information provided in the dataset

Project 11: Modify the code to predict insurance claim values for anyone above the age of 55 in the given dataset.

Case Study 6: Generate random quadratic data and demonstrate Decision Tree regression

Project 12: Modify the max_depth from 2 to 3 or 4, and observe the output.

Project 13: Modify the max_depth to 20, and observe the output

Project 14: What is the class prediction for petal_length = 3 cm and petal_width = 1 cm for the max_depth = 2?

Project 15: Explain the Decision Tree regression graphs produced when max_depths are 2 and 3. How many leaf nodes exist in the two cases? What does average value represent these two situations? Use the information provided

Project 16: Modify the regularization parameter min_sample_leaf from 10 to 6, and check the output of Decision Tree regression. What is the result and why?

Case Study 7: Predict insurance per year based on a person’s age using Random Forests.

Project 17: What is the output insurance value for individuals aged 60 and with n_estimators = 10?

Case Study 8: Demonstrate various regression techniques over a random dataset using the information provided in the dataset

Project 18: The program depicts a learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Give your interpretation of these charts?

Project 19: The program depicts the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Try changing the values to 0.001, 0.25, and 0.9 and check the results? Provide interpretation.

Concept Covered: Classification

Case Study 9: Predict if the consumers will buy houses, given their age and salary. Use the information provided in the dataset

Project 20: Typically, the value of nearest_neighbors for testing class in KNN is 5. Modify the code to change the value of nearest_neighbours to 2 and 20, and note the observations.

Case Study 10: Classify IRIS dataset using SVM, and demonstrate how Kernel SVMs can help classify non-linear data.

Project 21: Modify the kernel trick from RBF to linear to see the type of classifier that is produced for the XOR data in this program. Interpret the data.

Project 22: For the Iris dataset, add a new code at the end of this program to produce classification for RBF kernel trick with gamma = 1.0. Explain the output.

Case Study 11: Classify IRIS flower dataset using Decision Trees. Use the information provided

Project 23: Run decision tree on the IRIS dataset with max depths of 3 and 4, and show the tree output.

Project 24: Predict and print class probability for Iris flower instance with petal_len 1 cm and petal_width 0.5 cm.

Case Study 12: Classify the IRIS flower dataset using various classification algorithms. Use the information provided

Project 25: Add Logistic Regression classification to the program and compare classification output to previous algorithms?

Concept Covered: Unsupervised Learning with Clustering

Case Study 13: Demonstrate Clustering algorithm and the Elbow method on a random dataset.

Project 26: Modify the number of clusters k to 2, and note the observations.

Project 27: Modify the n_samples from 150 to 15000 and the number of centres to 4 with n_clusters as 3. Check the output, and note your observations.

Project 28: Modify the code to change the n_samples from 150 to 15000 and number of centres to 4, keeping n_clusters at 4. Check the output.

Project 29: Modify the number of clusters k to 6, and note the observations.
What are the prerequisites for this Machine Learning course?
Participants in this Machine Learning online course should have:
- Familiarity with the fundamentals of Python programming
- Fair understanding of the basics of statistics and mathematics

Course preview

Machine Learning
- Lesson 1: Introduction to Artificial Intelligence and Machine Learning
  32:24
  - 1.01 Introduction to AI and Machine Learning
    32:24
- Lesson 2: Techniques of Machine Learning
  24:01
  - 2.01 Techniques of Machine Learning
    24:01
- Lesson 3: Data Preprocessing
  1:15:56
  - 3.01 Data Preprocessing
    1:15:56
- Lesson 4: Math Refresher
  30:40
  - 4.01 Math Refresher
    30:40
- Lesson 5: Regression
  55:25
  - 5.01 Regression
    55:25
- Lesson 6: Classification
  1:03:41
  - 6.01 Classification
    1:03:41
- Lesson 7: Unsupervised learning - Clustering
  13:05
  - 7.01 Unsupervised Learning with Clustering
    13:05
- Lesson 8: Introduction to Deep Learning
  10:03
  - 8.01 Introduction to Deep Learning
    10:03
- Practice Projects
  - Uber Fare Prediction
  - Amazon.com Employee Access
  - Phishing detector with KNN
  - MNIST Classifier
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Data Science with Python
- Lesson 00 - Course Overview
  04:34
  - 0.001 Course Overview
    04:34
- Lesson 01 - Data Science Overview
  20:27
  - 1.001 Introduction to Data Science
    08:42
  - 1.002 Different Sectors Using Data Science
    05:59
  - 1.003 Purpose and Components of Python
    05:02
  - 1.4 Quiz
  - 1.005 Key Takeaways
    00:44
- Lesson 02 - Data Analytics Overview
  18:20
  - 2.001 Data Analytics Process
    07:21
  - 2.2 Knowledge Check
  - 2.3 Exploratory Data Analysis(EDA)
  - 2.4 EDA-Quantitative Technique
  - 2.005 EDA - Graphical Technique
    00:57
  - 2.006 Data Analytics Conclusion or Predictions
    04:30
  - 2.007 Data Analytics Communication
    02:06
  - 2.8 Data Types for Plotting
  - 2.009 Data Types and Plotting
    02:29
  - 2.10 Knowledge Check
  - 2.11 Quiz
  - 2.012 Key Takeaways
    00:57
- Lesson 03 - Statistical Analysis and Business Applications
  23:53
  - 3.001 Introduction to Statistics
    01:31
  - 3.2 Statistical and Non-statistical Analysis
  - 3.003 Major Categories of Statistics
    01:34
  - 3.4 Statistical Analysis Considerations
  - 3.005 Population and Sample
    02:15
  - 3.6 Statistical Analysis Process
  - 3.007 Data Distribution
    01:48
  - 3.8 Dispersion
  - 3.9 Knowledge Check
  - 3.010 Histogram
    03:59
  - 3.11 Knowledge Check
  - 3.012 Testing
    08:18
  - 3.13 Knowledge Check
  - 3.014 Correlation and Inferential Statistics
    02:57
  - 3.15 Quiz
  - 3.016 Key Takeaways
    01:31
- Lesson 04 - Python Environment Setup and Essentials
  23:58
  - 4.001 Anaconda
    02:54
  - 4.2 Installation of Anaconda Python Distribution (contd.)
  - 4.003 Data Types with Python
    13:28
  - 4.004 Basic Operators and Functions
    06:26
  - 4.5 Quiz
  - 4.006 Key Takeaways
    01:10
- Lesson 05 - Mathematical Computing with Python (NumPy)
  30:31
  - 5.001 Introduction to Numpy
    05:30
  - 5.2 Activity-Sequence it Right
  - 5.003 Demo 01-Creating and Printing an ndarray
    04:50
  - 5.4 Knowledge Check
  - 5.5 Class and Attributes of ndarray
  - 5.006 Basic Operations
    07:04
  - 5.7 Activity-Slice It
  - 5.8 Copy and Views
  - 5.009 Mathematical Functions of Numpy
    05:01
  - 5.10 Assignment 01
  - 5.011 Assignment 01 Demo
    03:55
  - 5.12 Assignment 02
  - 5.013 Assignment 02 Demo
    03:16
  - 5.14 Quiz
  - 5.015 Key Takeaways
    00:55
- Lesson 06 - Scientific computing with Python (Scipy)
  23:35
  - 6.001 Introduction to SciPy
    06:57
  - 6.002 SciPy Sub Package - Integration and Optimization
    05:51
  - 6.3 Knowledge Check
  - 6.4 SciPy sub package
  - 6.005 Demo - Calculate Eigenvalues and Eigenvector
    01:36
  - 6.6 Knowledge Check
  - 6.007 SciPy Sub Package - Statistics, Weave and IO
    05:46
  - 6.8 Assignment 01
  - 6.009 Assignment 01 Demo
    01:20
  - 6.10 Assignment 02
  - 6.011 Assignment 02 Demo
    00:55
  - 6.12 Quiz
  - 6.013 Key Takeaways
    01:10
- Lesson 07 - Data Manipulation with Pandas
  47:34
  - 7.001 Introduction to Pandas
    12:29
  - 7.2 Knowledge Check
  - 7.003 Understanding DataFrame
    05:31
  - 7.004 View and Select Data Demo
    05:34
  - 7.005 Missing Values
    03:16
  - 7.006 Data Operations
    09:56
  - 7.7 Knowledge Check
  - 7.008 File Read and Write Support
    00:31
  - 7.9 Knowledge Check-Sequence it Right
  - 7.010 Pandas Sql Operation
    02:00
  - 7.11 Assignment 01
  - 7.012 Assignment 01 Demo
    04:09
  - 7.13 Assignment 02
  - 7.014 Assignment 02 Demo
    02:34
  - 7.15 Quiz
  - 7.016 Key Takeaways
    01:34
- Lesson 08 - Machine Learning with Scikit–Learn
  1:02:10
  - 8.001 Machine Learning Approach
    03:57
  - 8.002 Steps 1 and 2
    01:00
  - 8.3 Steps 3 and 4
  - 8.004 How it Works
    01:24
  - 8.005 Steps 5 and 6
    01:54
  - 8.006 Supervised Learning Model Considerations
    00:30
  - 8.7 Knowledge Check
  - 8.008 Scikit-Learn
    02:10
  - 8.9 Knowledge Check
  - 8.010 Supervised Learning Models - Linear Regression
    11:19
  - 8.011 Supervised Learning Models - Logistic Regression
    08:43
  - 8.012 Unsupervised Learning Models
    10:40
  - 8.013 Pipeline
    02:37
  - 8.014 Model Persistence and Evaluation
    05:45
  - 8.15 Knowledge Check
  - 8.16 Assignment 01
  - 8.017 Assignment 01
    05:45
  - 8.18 Assignment 02
  - 8.019 Assignment 02
    05:14
  - 8.20 Quiz
  - 8.021 Key Takeaways
    01:12
- Lesson 09 - Natural Language Processing with Scikit Learn
  49:03
  - 9.001 NLP Overview
    10:42
  - 9.2 NLP Applications
  - 9.3 Knowledge check
  - 9.004 NLP Libraries-Scikit
    12:29
  - 9.5 Extraction Considerations
  - 9.006 Scikit Learn-Model Training and Grid Search
    10:17
  - 9.7 Assignment 01
  - 9.008 Demo Assignment 01
    06:32
  - 9.9 Assignment 02
  - 9.010 Demo Assignment 02
    08:00
  - 9.11 Quiz
  - 9.012 Key Takeaway
    01:03
- Lesson 10 - Data Visualization in Python using matplotlib
  32:46
  - 10.001 Introduction to Data Visualization
    08:02
  - 10.2 Knowledge Check
  - 10.3 Line Properties
  - 10.004 (x,y) Plot and Subplots
    10:01
  - 10.5 Knowledge Check
  - 10.006 Types of Plots
    09:34
  - 10.7 Assignment 01
  - 10.008 Assignment 01 Demo
    02:23
  - 10.9 Assignment 02
  - 10.010 Assignment 02 Demo
    01:47
  - 10.11 Quiz
  - 10.012 Key Takeaways
    00:59
- Lesson 11 - Web Scraping with BeautifulSoup
  52:27
  - 11.001 Web Scraping and Parsing
    12:50
  - 11.2 Knowledge Check
  - 11.003 Understanding and Searching the Tree
    12:56
  - 11.4 Navigating options
  - 11.005 Demo3 Navigating a Tree
    04:22
  - 11.6 Knowledge Check
  - 11.007 Modifying the Tree
    05:38
  - 11.008 Parsing and Printing the Document
    09:05
  - 11.9 Assignment 01
  - 11.010 Assignment 01 Demo
    01:55
  - 11.11 Assignment 02
  - 11.012 Assignment 02 demo
    04:57
  - 11.13 Quiz
  - 11.014 Key takeaways
    00:44
- Lesson 12 - Python integration with Hadoop MapReduce and Spark
  40:39
  - 12.001 Why Big Data Solutions are Provided for Python
    04:55
  - 12.2 Hadoop Core Components
  - 12.003 Python Integration with HDFS using Hadoop Streaming
    07:20
  - 12.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count
    08:52
  - 12.5 Knowledge Check
  - 12.006 Python Integration with Spark using PySpark
    07:43
  - 12.007 Demo 02 - Using PySpark to Determine Word Count
    04:12
  - 12.8 Knowledge Check
  - 12.9 Assignment 01
  - 12.010 Assignment 01 Demo
    02:47
  - 12.11 Assignment 02
  - 12.012 Assignment 02 Demo
    03:30
  - 12.13 Quiz
  - 12.014 Key takeaways
    01:20
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Programming with Python 3.X
- Getting Started with Python
  20:04
  - Installation
    09:31
  - Print and Strings
    07:47
  - Math
    02:46
- Variables, Loops and Statements
  36:54
  - Variables
    04:49
  - While Loops
    06:00
  - For Loops
    05:00
  - If Statements
    06:43
  - If Else Statements
    04:01
  - If Elif Else Statements
    10:21
- Functions and Global and Local Variables
  28:20
  - Functions
    05:03
  - Function Parameters
    14:04
  - Global and Local Variables
    09:13
- Understanding Error Detection
  11:35
  - Common Python Errors
    11:35
- Working with Files and Classes
  15:49
  - Writing to a File
    04:29
  - Appending to a File
    03:23
  - Reading From a File
    03:34
  - Classes
    04:23
- Intermediate Python
  39:09
  - Input and Statistics
    07:22
  - Import Syntax
    06:39
  - Making Modules
    06:20
  - Lists vs Tuples and List Manipulation
    10:34
  - Dictionaries
    08:14
- Project
  26:15
  - Problem Statement
  - Solution
    26:15
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- Math Refresher
  30:36
  - Math Refresher
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Course advisor

Mike Tamir No. 1 AI & Machine Learning Influencer, Head of Data Science - Uber ATG

Named by Onalytica as the No.1 influencer in AI & Machine Learning space, Mike serves as Head of Data Science for Uber ATG self-driving engineering team and as UC Berkeley data science faculty.

Vivek Singhal Co-Founder & Chief Data Scientist, CellStrat

Vivek is an entrepreneur and thought Leader in Artificial Intelligence and deep-tech industries. He is a leading data scientist and researcher with expertise in AI, Machine Learning, and Deep Learning.

Reviews

Mahesh Gaonkar Software Engineer, Bangalore

Simplilearn is a great start for the beginner as well for the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas on the concepts and exercises. I could finish my Machine Learning advance course very easily with good project exercise.

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Somil Gadhwal Application Engineer, Hyderabad

Simplilearn's course content is designed in a way that every session is closely connected to the next. There is no need to mug up the lessons. The instructors put thought into training and motivating students. I am really happy I joined the course.

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Afrid Mondal Nagpur

The training was fantastic. Thank you for providing a great platform to learn.

Roveena Sebastian R & D Manager, Bangalore

It was a great learning experience. The hands-on assignments and the various resources provided by Simplilearn is excellent. The live sessions were simply awesome. The trainer was so successful in keeping the remote class active, covered all topics in such a short span and majorly ensured that the entire class was moving forward together in spite of the known time constraints. My special thanks to the trainer for his interest and dedication. Thanks once again to Simplilearn.

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Deboleena Paul Solution Architect, Meerut

I really liked the trainer. He is very patient, well organized, and interactive. I had an awesome learning experience with Simplilearn that was beyond my expectation for an online classroom.

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Happy Snehal Data Science Intern at ABB, Bangalore

My experience with Simplilearn has been amazing. This is the fifth course that I have done from here and all the courses provide the best quality knowledge. Machine Learning course content was wide and deep. It covered algorithms, Python programming, Mathematics, and Statistics. It also provided project support. My customer support experience has been fantastic as within seconds or minutes, I have been provided with solutions and all my issues have been resolved to my full satisfaction. The faculties are well educated, well experienced, humble, kind and eager to teach things.

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Aditi Dalal Analyst (Data Analytics) at The Smart Cube, Noida

I have enrolled in Machine Learning from Simplilearn. The content of the course is elaborate and easy to understand. The faculty has clarity in his way of explaining, maintains a very good balance between theory and the practical process. It has been a great learning experience for me.

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Siddhant Vibhute M.Tech Scholar at VJTI, Mumbai

Simplilearn provides a platform to explore the subject in depth. The way it connects every problem with the real world makes the subject even more interesting. The trainers and support staff act promptly to each query with every possible help. Machine Learning course is definitely one of my best experiences and is highly recommended for every data scientist aspirant.

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Ujjwal Seth Data Analyst at Hewlett Packard Enterprise, Chennai

I have completed Machine Learning certification recently from Simplilearn. It felt amused how I was able to this skill when I was finding it super-hard when learning through some of the other online platforms. No Doubt that I feel Simplilearn is the Best Online Platform for learning Computer Science Skills! The Online Lab access gives complete tech resources using which you can execute Computer code and don't need to install the software on your laptop. The whole system is both simplistic and 1uality wise absolutely to the point and makes the user experience simple and beautiful. The content of the course was interesting and it used a lot of real-life application which helped me to understand better. The customer support was very supportive and always ready to help us. In fact, they always assured that our problem will be solved and the response was quick. Hence, A curious mind should not miss a chance to enroll in his preferred course at Simplilearn.

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Leela Krishna Senior Operations Professional at IBM INDIA PVT. LTD., Bangalore

The course was very informative. The study material provided by the trainer was extremely helpful and very easy to understand.

Rajendra Kumar Rana Senior Software Engineer RPA at Tech Mahindra, Pune

The course material was very engaging and helpful. The Trainer's in-depth knowledge helped to understand Machine Learning better.

Anuvrat Kulkarni Development Analyst in Social Media at Accenture, Bangalore

My experience with Simplilearn has been very enriching. The faculty was quite experienced and had a deep knowledge of the subject. I am happy with Simplilearn and would definitely recommend others.

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Deboleena Paul Senior Technical Lead at HCL Technologies, Lucknow

My experience while doing machine learning certification from Simplilearnwas was beyond my expectation for an online classroom. The trainer was great. He was very patient and interactive.

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Parichay Bose Solutions Architect at Ericsson, Mississauga

I have been taking multiple courses from Simplilearn including Big Data Hadoop, Machine Learning, MEAN Stack. Apart from awesome content and trainer, they have amazing support executive that makes me feel cared. The customer support is helpful and is always there whenever you need help. That is where other online training programs are lagging behind. Well done Simplilearn!

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Vijay Marupadi Project Manager at Canadas Best Store Fixtures, Mississauga

The Simplilearn learning experience was beyond my expectation. The professionalism with which the training was carried out is worth commending. I would readily recommend Simplilearn to anyone who wants to pursue a career through online learning. It's worth the money. Happy learning with Simplilearn!

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FAQs

What is the average salary for a Machine Learning Engineer in Delhi?

According to PayScale, Machine Learning Engineers in Delhi can earn an average salary of Rs 959,706 a year. The earning potential can increase for individuals who have undergone a Machine Learning program.
What are other types of roles within the AI & Machine Learning space available in Delhi?
Other roles within the AI & Machine Learning space available in Delhi are
- Data Scientist
- Research Scientist
- Analyst Machine Learning
Which companies are hiring Machine Learning Engineers in Delhi?

Companies like Untrodden Labs, Fareportal, AMEX, Infogain, Amazon, Gartner, Fidelity are looking for skilled AI & Machine Learning experts in Delhi.
What is Machine Learning?

Machine learning is nothing but an implementation of Artificial Intelligence that allows systems to simultaneously learn and improve from past experiences without the need of being explicitly programmed. It is a process of observing data patterns, collecting relevant information, and making effective decisions for a better future of any organization. Machine learning facilitates the analysis of huge quantities of data, usually delivering faster and accurate results to extract profitable benefits and opportunities.
How will the labs be conducted?

Simplilearn provides Integrated labs for all the hands-on and projects execution. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.
Is this live training, or will I watch pre-recorded videos?
If you enrol for self-paced e-learning, you will have access to pre-recorded videos. If you enrol for the online classroom Flexi Pass, you will have access to live training conducted online as well as the pre-recorded videos.
What if I miss a class?

Simplilearn provides recordings of each class so you can review them as needed before the next session. With Flexi-pass, Simplilearn gives you access to all classes for 90 days so that you have the flexibility to choose sessions as per your convenience.
Who are the instructors and how are they selected?
All of our highly qualified trainers are industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.
What is Global Teaching Assistance?
Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.
What is online classroom training?

Online classroom training for Machine Learning Certification is conducted via online live streaming of each class. The classes are conducted by a Machine Learning certified trainer with more than 15 years of work and training experience.
What is covered under the 24/7 Support promise?
We offer 24/7 support through email, chat and telephone. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.
How do I enroll in this online training?
You can enroll in this training on our website and make an online payment using any of the following options:
- Visa Credit or Debit Card
- MasterCard
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If I need to cancel my enrollment, can I get a refund?

Yes, you can cancel your enrolment if necessary. We will refund the course price after deducting an administrative fee. To learn more, please read our Refund Policy.
Do you provide a money back guarantee for the training programs?
Yes. We do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support portal.
Who can I contact to learn more about this Machine Learning course?
Please contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives will be able to give you more details.
* Disclaimer
* The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

Machine Learning Certification Course in Delhi, India

Key features

Self-Paced Learning

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Corporate Training Solutions

Course description

Why Machine Learning Certification Course?

What are the objectives of this course?

What skills will you learn with our Machine Learning Course?

Who should take this Machine Learning Training Course?

What projects are included in this Machine Learning Training Course?

What are the prerequisites for this Machine Learning course?

Course preview

Machine Learning

Lesson 1: Introduction to Artificial Intelligence and Machine Learning

1.01 Introduction to AI and Machine Learning

Lesson 2: Techniques of Machine Learning

2.01 Techniques of Machine Learning

Lesson 3: Data Preprocessing

3.01 Data Preprocessing

Lesson 4: Math Refresher

4.01 Math Refresher

Lesson 5: Regression

5.01 Regression

Lesson 6: Classification

6.01 Classification

Lesson 7: Unsupervised learning - Clustering

7.01 Unsupervised Learning with Clustering

Lesson 8: Introduction to Deep Learning

8.01 Introduction to Deep Learning

Practice Projects

Uber Fare Prediction

Amazon.com Employee Access

Phishing detector with KNN

MNIST Classifier

Data Science with Python

Lesson 00 - Course Overview

0.001 Course Overview

Lesson 01 - Data Science Overview

1.001 Introduction to Data Science

1.002 Different Sectors Using Data Science

1.003 Purpose and Components of Python

1.4 Quiz

1.005 Key Takeaways

Lesson 02 - Data Analytics Overview

2.001 Data Analytics Process

2.2 Knowledge Check

2.3 Exploratory Data Analysis(EDA)

2.4 EDA-Quantitative Technique

2.005 EDA - Graphical Technique

2.006 Data Analytics Conclusion or Predictions

2.007 Data Analytics Communication

2.8 Data Types for Plotting

2.009 Data Types and Plotting

2.10 Knowledge Check

2.11 Quiz

2.012 Key Takeaways

Lesson 03 - Statistical Analysis and Business Applications

3.001 Introduction to Statistics

3.2 Statistical and Non-statistical Analysis

3.003 Major Categories of Statistics

3.4 Statistical Analysis Considerations

3.005 Population and Sample

3.6 Statistical Analysis Process

3.007 Data Distribution

3.8 Dispersion

3.9 Knowledge Check

3.010 Histogram

3.11 Knowledge Check

3.012 Testing

3.13 Knowledge Check

3.014 Correlation and Inferential Statistics

3.15 Quiz

3.016 Key Takeaways

Lesson 04 - Python Environment Setup and Essentials

4.001 Anaconda

4.2 Installation of Anaconda Python Distribution (contd.)

4.003 Data Types with Python

4.004 Basic Operators and Functions

4.5 Quiz

Online Classroom Flexi-Pass

What is Flexi Pass?

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