Course description

  • Why Should you take Machine Learning Certification?

    • The companies are in an increasing requirement for the professionals to be aware of the ins and outs of Machine Learning as it is taking over the world.
    • There is an increasing need for professionals to be aware of Machine Learning concepts as this field is now taking over the world.
    • There is scope for the potential growth in the market size for Machine Learning 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.
    Why learn Machine Learning

  • What are the objectives of this course?

     

    Machine Learning is a form of artificial intelligence that is transforming the world of computing together with people’s digital interactions. Machine Learning has become critical to numerous new and future applications by making it possible to cheaply, quickly, and automatically process and analyze huge volumes of complex data. Machine learning empowers innovative automated technologies such as fraud protection, recommendation engines, facial recognition, and even self-driving cars.

    The Machine Learning course in Bangalore prepares data scientists, engineers, and other professionals with the practical skills and knowledge required for job competency and certification in Machine Learning. There is a growing demand for Machine Learning skills around the globe. The average salary of a Machine Learning Engineer according to payscale.com is $134,293 (USD).

  • What will you learn in this Machine Learning Training?

    On completion of this Machine Learning Course in Bangalore, you can:

    • Get a thorough knowledge of the heuristic and mathematical aspects of machine learning.
    • Understand the theoretical concepts and their relation with the practical aspects of machine learning.
    • Get an introduction to machine learning & real-time applications
    • Model a wide variety of robust machine learning algorithms including recommendation systems, deep learning, and clustering.
    • Gain practical mastery over the algorithms, principles, and applications of machine learning with the help of a practical learning approach which involves working on 28 projects and one capstone project.
    • Be skillful in the concepts of reinforcement, unsupervised, and supervised learning concepts and modeling.
    • Understand the concepts and operation of decision tree classifier, random forest classifier, naive Bayes, support vector machines, K-nearest neighbors, kernel SVM, logistic regression, K-means clustering and more.

  • Who should take this Machine Learning Training in Bangalore ?

    The demand for skilled Machine Learning engineers is increasing across all industries. This makes the Machine Learning certification course in Bangalore appropriate for candidates at an intermediate level of experience. Simplilearn particularly recommends this Machine Learning training in Bangalore for the following set of professionals:

    • Graduates eager to establish their career in machine learning and data science
    • Information architects who want to get skilled in the Machine Learning algorithms
    • Analytics managers who are leading a team of analysts
    • Developers aspiring to be a data scientist or machine learning engineer
    • Business analysts who want to understand data science techniques
    • Experienced professionals who would like to harness machine learning in their fields to get more insights
    • Analytics professionals who want to work in machine learning or artificial intelligence

  • What Projects are included in this Machine Learning Certification Training Course?

    Simplilearn's Machine Learning Training course is practical and code-driven. The mathematical problem formulation and the theoretical motivation must be provided only while introducing the concepts.

    This course has a primary capstone project and more than 25 ancillary exercises based on the 17 machine learning algorithms.

    Capstone Project Details:

    Project Name: Predicting house prices in California

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

    Concept covered: Techniques of Machine Learning

    Case Study 1: From the given dataset, predict whether the patrons will buy houses or not, given 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 is the estimated price of the houses with areas 1700 and 1900?

    Concept covered: Data Preprocessing

    Case Study 2: By using the information given in the dataset, illustrate the methods to handle categorical data, missing data, and data standardization.

    Project 3: Review the training dataset (Excel file). Observe that the weight for the fifth and eighth rows is missing. 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 the strategy parameter from “mean” to “median” and re-execute it. 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: Illustrate how you can reduce the data dimensions from 3D to 2D by making use of the given information

    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 an explanation.

    Concept Covered: Regression

    Case Study 4: Show how you can reduce the data dimensions from 3D to 2D by making use of the information given

    Project 8: Alter the degree of the polynomial from the 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 the yearly insurance premium based on a person’s age by using the Decision Trees and 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 dataset provided.

    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 result obtained.

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

    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 the average value represent these two situations? Use the information given

    Project 16: Alter 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 given information

    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 centers 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 the number of centers to 4, keeping n_clusters at 4. Check the result.

    Project 29: Modify the number of clusters k to 6, and note the observations.

  • What are the prerequisites for attending Simplilearn's 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

    • 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
    • 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
    • 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
    • Math Refresher

      30:36
      • Math Refresher
        30:36
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Exam & certification FREE PRACTICE TEST

  • When can you get Simplilearn's Machine Learning Training Completion Certificate?

    Some of the qualifications you must satisfy to be eligible for getting Simplilearn’s Machine Learning course completion certificate that is globally recognized and accepted are:

    Online Self-Learning:

    • Completion of one project.
    • Completion of 85% of the course.

    Online Classroom:

    • Completion of one project.
    • Attending a complete batch.

  • What are the pre-requisites for attending this Machine Learning Training in Bangalore?

     

    For candidates to attend this Machine Learning training in Bangalore, they must:

    • Possess knowledge of basic high school mathematics  
    • Have an understanding of the fundamentals of Python programming
    • Know the basics of statistics

    The concepts of statistics and mathematics that needed for Machine Learning is covered in this course. When you purchase Simplilearn’s Machine Learning course, a complimentary Python course is also provided.

  • Who provides the certification?

     

    The certification will be awarded to the candidates after successfully completing this Machine Learning course.

  • Is this course accredited?

     

    No. This Machine Learning Course is not accredited by any standard organization.

  • How can i become machine learning Engineer?

     

    From this Machine Learning course, you can learn to programme in R language or Python; learn about real-time machine learning applications, implement machine learning using Tensorflow framework etc. Once you possess the basic skill set, you can gain practical experience by taking part in Kaggle competitions, applying for a Machine Learning internship, and completing personal engineering projects etc.

  • How do I pass Simplilearn's Machine Learning Course Certification exam?

    Some of the qualifications you must satisfy to be eligible for getting Simplilearn’s Machine Learning course completion certificate are:

    Online Self-Learning:

    • Complete one project.
    • Complete 85% of the course.

    Online Classroom:

    • Complete one project.
    • Attend a complete batch.

  • If I fail the Simplilearn's Certification Exam after completing Machine learning Course, how soon can I retake it?

     

    If you fail Simplilearn’s certification exam, you can retake it immediately.

  • Do you offer a money back guarantee for the training course?

    Yes.  For many training programs, Simplilearn offers a cash-back guarantee. You can submit refund requests through our Help and Support portal and also refer to our Refund Policy.

  • What do I need to do to unlock my Simplilearn certificate?

    Online Classroom:

    • Attend one complete batch
    • Submit at least one completed project.

    Online Self-Learning:

    • Complete 85% of the course
    • Submit at least one completed project.

Course advisor

Mike Tamir
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
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
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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Roveena Sebastian
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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Happy Snehal
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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Leela Krishna
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.

Anuvrat Kulkarni
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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Somil Gadhwal
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
Afrid Mondal Nagpur

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

Deboleena Paul
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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Aditi Dalal
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
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
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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Rajendra Kumar Rana
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.

Deboleena Paul
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
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
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 Salary of Machine Learning Engineer in Bangalore?

    The median annual salary of the Machine Learning engineer in Bangalore is Rs 12,28,665, according to PayScale.com. Individuals who have undergone the Machine Learning training have the potential to earn more.

  • Which Companies/ Startups in Bangalore are hiring Machine Learning Professionals?

    Companies that are looking for skilled AI & Machine Learning experts in Bangalore are:

    • Siemens
    • Microsoft
    • JP Morgan
    • Amazon
    • Samsung R&D
    • Oracle
    • Chase
    • Netapp

  • If I need to cancel my enrollment, can I get a refund?

     

    Yes, you can revoke your enrolment if it is necessary. After deducting the course fee we will provide a refund of your course fee. Please read our Refund Policy to learn more.

  • Are there any group discounts for classroom training programs?

    Yes, Simplilearn provides group discount options for the training programs. For further details, you can contact Simplilearn by selecting the Live Chat link or by making use of the form on the right side of any page on our website or get in touch with our customer service representatives.

  • What are different payment options available for enrolling this course now?

     

    The enrollment for this training program can be done on the Simplilearn website and an online payment can be made through any one of the following methods:

    • PayPal
    • American Express
    • MasterCard
    • Diner’s Club
    • Visa Credit or Debit Card

    Once the payment is received, you will automatically get an email of the payment receipt along with the access information.

  • Who can I contact to learn more about this course?

     

    Please contact Simplilearn by selecting the Live Chat link or by making use of the form on the right side of any page on our website. You can get more details from our customer service representatives.

  • What is Global Teaching Assistance?

    We have a dedicated group of subject matter experts as our teaching staff who will help you get certified in your first attempt. They proactively engage students and make sure that the course path is being followed. Our teaching assistants can also help in enriching your learning experience from class onboarding and project mentoring to job assistance. Teaching assistance is available during business hours.

  • What is Simplilearn's 24/7 Support promise?

     

    Simplilearn provides 24/7 support through telephone, chat, and email. Our dedicated team can provide you with an on-demand assistance through Simplilearn’s community forum. You will get a lifetime access to our community forum even after you complete the course with us.

  • What if I miss a class?

     

    With Simplilearn’s Flexi-pass, you can attend the classes without hampering your busy schedule. With this course, you will get an advantage of being trained by renowned trainers with an industry experience of many years. These trainers combine the best of self-paced learning with online classroom training. This Flexi-pass also provides you with access to 15 sessions for a duration of 90 days.

  • How will I execute projects during the course?

     

    Simplilearn offers CloudLab, which is a cloud-based Python environment lab along with this Machine Learning course for smooth execution of the practical projects. With this platform, candidates don’t have to install or maintain Python and it’s libraries on a virtual machine. They can instead access the pre-configured environment on CloudLab on your browser.

    From the Simplilearn Learning Management System (LMS), you will be given access to our online CloudLab platform for the duration of the course.

    You can complete the projects by making use of Simplilearn’s CloudLab

  • I am not able to access the online course. Who can help me?

     

    You can contact Simplilearn by selecting the Live Chat link or by making use of the form on the right of any page on our website. You can also contact our Help & Support.

  • What is online classroom training?

    The online classroom training conducted for the Machine Learning Certification Course is through live online streaming. A Machine Learning certified instructor with training and working experience of more than 15 years handles the classes.

  • Is this live training, or will I watch pre-recorded videos?

    • If candidates register for the online classroom Flexi Pass, they will be given access to the pre-recorded videos along with the live training that is conducted online.
    • If candidates enroll for self-paced e-learning, they can only access the pre-recorded videos.
    •  

  • Are the training and course material effective in preparing me for the Machine Learning certification?

     

    Yes, Simplilearn’s course materials and training are effective as they guarantee success in the Machine Learning certification exam.

  • Who are the instructors and how are they selected?

     

    Simplilearn’s highly qualified instructors are all industry experts with decades of relevant teaching experience. All the instructors have undergone a meticulous selection process including a training demo, technical evaluation, and profile screening before getting certified to train for us. It is also assured that instructors with a high alumni rating continue as our faculty.

  • 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.

    Our Bangalore Correspondence / Mailing address

    # 53/1 C, Manoj Arcade, 24th Main, Harlkunte, 2nd Sector, HSR Layout, Bangalore - 560102, Karnataka, India.

    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.