Machine Learning Certification Course in Mumbai, India

Machine Learning Training FAQs

• Why Learn Machine Learning Certification?

   

  Machine Learning is the technology whose name can be now heard from all the corners of the world. The requirement for proficiency in this promising technology has never been so high.

  It is estimated that the market size of machine learning will increase 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 course objectives?

   

  Machine Learning takes Artificial Intelligence to the next level. It has changed the way how people look at data computing and digital transformations. Several innovative applications that will launch in the future are backed by machine learning technology. It is due to the fact that machine learning technology has the ability to process and interpret a large amount of complicated data automatically. Besides, it is cheap as well as fast technology. Machine Learning has found its applications in areas like self-driving cars, facial recognition, recommendation engines, and facial recognition.

  According to payscale.com, a Machine Learning Engineer earns an average salary of $134,293 (USD). As a rising demand has been observed for the skilled Machine Learning professionals, candidates can take up this Machine Learning course in Mumbai to get the job competency and hands-on experience with Machine Learning.

• What skills you learn in Machine Learning Certification Training?

  The candidates taking the Machine Learning course in Mumbai will be able to do the following:

  • Be prepared to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems
  • 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.
  • Enhance your knowledge of principles, algorithms, and applications of machine learning through a hands-on approach, which includes working on 28 projects and one Capstone project.
  • Learn the concepts of supervised, unsupervised, and reinforcement learning and modeling.
  • Explain the machine learning theoretical concepts with its practical aspects.
  • Explore the mathematical and heuristic aspects of machine learning.

• Who should take Machine Learning Course?

  In all of the major industries, there is a considerable requirement for professionals who are proficient in Machine Learning. This Machine Learning Course is, therefore, ideal for professionals who have an intermediate level of working experience. The following professionals will specifically benefit from the Machine course:

  • 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
  • Business analysts who want to understand data science techniques
  • Developers aspiring to be a data scientist or machine learning engineer
  • Graduates looking to build a career in data science and machine learning
  • Information architects who want to gain expertise in machine learning algorithms
  • Analytics managers who are leading a team of analysts

• What Machine Learning Real-time Projects you will complete during the course?

  We make sure that our Machine Learning course provides coding experience along with hands-on projects. While beginning with the concepts, we also provide theoretical motivation and mathematical problem formulation.

  This course includes one primary capstone project and more than 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 the houses will be purchased or not by the consumers, from the given dataset, provided with their salary and age

  Project 1: In reference to the above problem statement, what issues can be observed in the plot generated by the code?

  Project  2: What is the estimated cost of the houses with areas 1700 and 1900?

  Concept covered: Data Preprocessing

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

  Project 3: Review the training dataset (Excel file). Observe that weight is missing for the fifth and eighth rows. For the mentioned rows, what are the values computed by the imputer?

  Project 4: In the tutorial code, find the call to the Imputer class. Replace the strategy parameter from “mean” to “median” and rerun 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: 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 people over 55 years of age 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 the 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 result do you observe? Explain the reason.

  Case Study 7: Use Random Forests to predict insurance per year based on the age of a person.

  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 shows a learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Interpret these charts.

  Project 19: The program shows the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Try modifying the values to 0.001, 0.25, and 0.9 and observe the output. Give your interpretation.

  Concept Covered: Classification

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

  Project 20: Typically, the nearest_neighbors for testing class in KNN has the value 5. Modify the code with the value of nearest_neighbours to 2 and 20, and note down your 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 to linear from RBF to check the type of classifier that is produced for the XOR data in this program. Interpret the data.

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

  Case Study 11: Use Decision Trees to classify IRIS flower dataset. Use the information provided.

  Project 23: Run decision tree on the IRIS dataset with max depths of 3 and 4, and display 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:  Change the number of clusters k to 2, and record the observations.

  Project 27:  Modify the n_samples from 150 to 15000 and the number of centers to 4 with n_clusters as 3. Find the output, and record the observations.

  Project 28:  Change the code to set the n_samples from 150 to 15000 and the number of centers to 4, keeping n_clusters at 4. Find the output.

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

• What are the prerequisites for attending Machine Learning Training?

   

  The prerequisites for this Machine Learning course are:

  • Knowledge of basic high school mathematics
  • Clarity on the concepts of Python programming
  • Fundamental understanding of statistics

  Simplilearn provides a free Python course along with the Machine Learning course to help you brush up your knowledge of statistics and mathematics concepts.

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

  As per the survey conducted by Payscale, it is found that the average salary of Machine Learning engineers in Mumbai is Rs 1,047,479 per annum. It can increase significantly for professionals taking a Machine Learning training program.

• What are different Machine Learning jobs & roles available in Mumbai?

  There are many roles belonging to the Machine Learning and Artificial Intelligence domain in Mumbai such as:

  • Senior Software Engineer ML
  • Data Scientist
  • ML Engineer
  • ML Specialist
  • Automation and Tool Development

• Who are the top Employers Hiring Machine Learning Engineers in Mumbai?

   

  Machine Learning professionals are in high demand in Mumbai nowadays with companies like Morgan Stanley, Accenture, Ubisoft, Sutherland, JP Morgan Chase among the many creating opportunities for them.  

• What is Machine Learning?

   

  Machine Learning is the next step of Artificial Intelligence in which systems are designed to learn from past experiences and provide improved results without programming it specifically. Machine Learning has the ability to analyze huge amounts of data and generate results with high speed and better accuracy giving profitable advantages. Basically, machine learning involves observation of data patterns, gathering crucial information, and making decisions that are efficient for the growth of any company.

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

   

  After a deduction of an amount equivalent to the administration fees, the remaining amount is refunded to the candidate on canceling the enrollment. For further queries, read our Refund Policy.

• Are there any group discounts for classroom training programs?

   

  Yes, Simplilearn provides group discounts for its training programs. To get more details, visit the Simplilearn website and contact our support team via the Contact Us form or Live Chat option.

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

   

  Candidates need to pay online to get themselves enrolled in our Machine Learning Course in Mumbai. The payment options available are:

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

  An automatically generated receipt will be sent to the candidate via email once the payment is successful.

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

   

  We provide a Contact Us form on the right of every page of our website. There is also an option of Live Chat where candidates can contact our support team or use the Help & Support portal.

• What is Global Teaching Assistance?

   

  The faculty at Simplilearn enrich the learning experience of the students through interactive sessions and making sure that students have a firm grasp of the subject being taught. Teaching assistance is provided during business hours by our faculty who support the candidates right from class onboarding to project completion and job assistance.

• What is covered under the 24/7 Support promise?

   

  Our support team is available for the candidates via calls, chat, or email throughout the day. There is a community forum too that can be accessed 24/7 by the candidates. Moreover, it comes with permanent access.

• What if I miss a class?

   

  Simplilearn makes it easier for its candidates to complete the Machine Learning course by providing the Flexi-pass. The Flexi-pass gives candidates an option to access 15 training sessions for 90 days that are conducted by highly experienced faculty. The candidates will get the benefit from both online classroom training and self-paced learning.

• How will I execute projects in this Machine Learning training course?

   

  The Machine Learning Course offered by Simplilearn involves completion of industry-oriented projects. To execute the projects, Simplilearn provides a cloud-based Python environment called as CloudLab. Candidates do not require a virtual machine to install and maintain Python and its libraries. They can use the browser to access a pre-configured environment on CloudLab.

  The CloudLab platform is accessible throughout the course completion via the Simplilearn Learning Management System (LMS)

   

   

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

   

  Candidates can either fill the Contact Us form or select the Live Chat option to get in touch with the support team. Both of the options are available on Simplilearn website. You can also click on Help & Support option.

• Do you provide a money back guarantee for the training programs?

   

  Yes, Simplilearn has a money-back guarantee for most of its training programs. Read our Refund Policy to know more. You can submit refund requests through our Help & Support portal.

• What is online classroom training?

   

  Simplilearn conducts the Machine Learning Certification classes via live online streaming. These are the interactive sessions led by trainers with over 15 years of relevant experience, and the candidates can communicate with them to get their queries resolved.

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

   

  Simplilearn provides two learning methodologies. For self-paced e-learning, pre-recorded videos are provided to the candidates. With Flexi Pass learning, the candidates get the advantage of instructor-led live online training sessions along with pre-recorded videos.

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

   

  Yes, for successful completion of the Machine Learning Certification, we provide state-of-the-art training and course material.

• Who are the instructors and how are they selected?

   

  We select trainers only after a strict recruitment procedure along with a high alumni rating. The process involves technical evaluation, profile screening as well as training demo. We ensure that the domain experts having several years of relevant teaching experience are only allowed to become the mentors.

• 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.