Machine Learning Training Course in Mumbai

Machine Learning Training FAQs

• 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.
  

• Why learn Machine Learning?

  • 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.

• How will the labs be conducted?

  Simplilearn provides Integrated labs for all the hands-on execution of projects. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.

• How do beginners learn Machine Learning?

  Machine learning is in high demand. But before you jump into certification training, it’s essential for beginners to get familiar with the basics of machine learning first. Simplilearn’s free resources articles, tutorials, and YouTube videos will help you get a handle on the concepts and techniques of machine learning. Start your learning with our free courses that serve as a foundation for this exciting and dynamic field: Statistics Essentials for Data Science, Math Refresher, and Data Science with Python.

• Is this Machine Learning course training in Mumbai suitable for freshers?

  Yes, the Machine Learning course training in Mumbai is suitable for freshers, and this course helps you learn Machine Learning topics including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling.

• What is the course fee of the Machine Learning training in Mumbai?

  The course fee of the Machine Learning training in Mumbai starts from Rs. 18,999/-.

• In which areas of Mumbai is the Machine Learning training conducted?

  No matter which area of Mumbai you are in, be it Bandra, Malabar Hill, Juhu, Worli, Altamount Road, Andheri, Powai, Versova anywhere. You can access our Machine Learning course online sitting at home or office.

• Do you provide this Machine Learning training in Mumbai with placement?

  No, currently, we do not provide any placement guarantee with the Machine Learning course.

• Why do I need to choose Simplilearn to learn Machine Learning in Mumbai?

  Simplilearn provides instructor-led training, lifetime access to self-paced learning, training from industry experts, and real-life industry projects with multiple video lessons.

• What are the objectives of Machine Learning course in Mumbai?

  This Machine Learning course in Mumbai will provide you with insights into the vital roles played by machine learning engineers and data scientists. Upon completion of this course, you will be able to uncover the hidden value in data using Python programming for futuristic inference. You will work with real-time data across multiple domains including e-commerce, automotive, social media and more. You will learn how to develop machine learning algorithms using concepts of regression, classification, time series modelling and much more.

• What skills you learn from Machine Learning course in Mumbai?

  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 Mumbai?

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

  We make sure that our Machine Learning course in Mumbai 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 the 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 a new code at the end of this program to produce a classification for RBF kernel trick with gamma = 1.0. Discuss the result.

   

  Case Study 11: Use Decision Trees to classify the 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 the 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 the Machine Learning course in Mumbai?

  The prerequisites for this Machine Learning course in Mumbai 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 certification training.

• 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
  • Data Scientist
  • Machine Learning Engineer
  • Machine Learning 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.  

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

  If you enroll for self-paced e-learning, you will have access to pre-recorded videos. If you enroll for the Online Classroom Flexi-Pass, you will have access to live Machine Learning training conducted online as well as the pre-recorded videos.

• What if I miss a class?

  Simplilearn provides recordings of each Machine Learning 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 Machine Learning trainers are industry AI 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 the Machine Learning 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 the 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 Machine Learning online course?

  You can enroll in this Machine Learning online certification course on our website and make an online payment using any of the following options:

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

  Once payment is received you will automatically receive a payment receipt and access information via email.

• 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.