Machine Learning Training Course in Delhi

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 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

• 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 Delhi suitable for freshers?

  Yes, the Machine Learning course training in Delhi 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 fees structure of the Machine Learning course training in Delhi?

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

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

  No matter which area of Delhi you are in, be it Saket, Greater Kailash, Vasant Kunj, Lajpat Nagar, Gurgaon, Sunder Nagar, Dwarka, Shanti Niketan, Mayur Vihar, Golf Links, Vasant Vihar, Anand Lok, Udyog Vihar anywhere. You can access our Machine Learning course online sitting at home or office.

• Do you provide this Machine Learning training in Delhi 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 Delhi?

  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 this Machine Learning course in Delhi?

  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 as innovative automated technologies as recommendation engines, facial recognition, fraud protection, and even self-driving cars.
  
  This Machine Learning course in Delhi prepares engineers, data scientists, and other professionals with the 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 INR 926K in Delhi, according to Glassdoor.com.

   

• What skills will you learn with our Machine Learning course in Delhi?

  By the end of this Machine Learning course in Delhi, 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 course in Delhi?

  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 course in Delhi for the following professionals in particular:

  • Developers aspiring to be data scientists or machine learning engineers
  • 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 course in Delhi?

  Simplilearn's Machine Learning course in Delhi 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 the 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 the 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 new code at the end of this program to produce a classification for the RBF kernel trick with gamma = 1.0. Explain the output.

   

  Case Study 11: Classify the 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 the 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 output.

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

• What are the prerequisites for this Machine Learning course in Delhi?

  Participants in this Machine Learning course in Delhi should have:

  • Familiarity with the fundamentals of Python programming 
  • Fair understanding of the basics of statistics and mathematics

  And, here are some of the fundamental courses that participants need to know before they get into Machine Learning online course:

  • Python for Data Science
  • Math Refresher
  • Statistics Essential for Data Science

• 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 certification training.

• 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.

• 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.

• 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.

• 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.

    

• 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.