Machine Learning Training Course in Pune

Machine Learning Training FAQs

• Why learn 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.
  • With Machine learning taking over the world, the companies greatly need professionals to be aware of the ins and outs of machine learning.

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

  Machine learning, a form of artificial intelligence, is revolutionizing the world of computing as well as people’s digital interactions. Machine learning is critical to countless new and future applications by making it possible to quickly, automatically, and cheaply 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.

  This Machine Learning course in Pune gives data scientists, engineers, and other professionals the hands-on skills and the knowledge required for job competency and certification in machine learning. The demand for machine learning skills and knowledge is growing fast. According to payscale.com, the average salary of a Machine Learning Engineer is $134,293 (USD).

• What skills you learn in this Machine Learning course in Pune?

  On completing this Machine Learning course in Pune, you will:

  • Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems
  • Acquire a thorough knowledge of the mathematical and heuristic aspects of machine learning.
  • Master the practicalities of the algorithms, principles, and applications of machine learning by the hands-on learning approach which includes 28 projects to work on and one capstone project.
  • Be able to understand the theoretical concepts and how they relate to the practical aspects of machine learning.
  • Gain Mastery in the concepts of unsupervised, supervised, and reinforcement modeling and learning concepts.
  • Understand the operation and concepts of naive Bayes, decision tree classifier,  logistic regression, support vector machines, kernel SVM, K-nearest neighbors, random forest classifier, K-means clustering, and more.

• Who should take this Machine Learning course in Pune?

  Across industries, there is an increasing demand for skilled machine learning engineers. Skilled machine learning engineers are in demand throughout the globe and across all industries.

  This makes the Machine Learning course in Pune an ideal course for aspirants who have mid-level intermediate experience. We particularly recommend this Machine Learning training course for the following professionals:

  • Analytics professionals who wish to work in artificial intelligence or machine learning
  • Business analysts who want to understand data science techniques
  • Graduates eager to build a profession in machine learning and data science
  • Experienced professionals who desire to make use of machine learning in their fields to get more insights
  • Analytics managers who are leading a team of analysts
  • Developers aspiring to be a data scientist or machine learning engineer
  • Information architects who want to gain expertise in machine learning algorithms

• What projects are included in Machine Learning course in Pune?

  Simplilearn's Machine Learning course in Pune is code-driven and provides hands-on experience. Mathematical problem formulation and theoretical motivation must be provided only while introducing the concepts.

  In this Machine Learning course, one primary capstone project and 25+ ancillary exercises based on 17 machine learning algorithms have been included.

  Capstone Project Details:

  Project Name: Predicting house prices in California

  Description: The project involves building a model that predicts average house values in the Californian districts. The candidates will be given metrics such as average income, population, average housing price, and so on for each block group in California. 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 built by the candidates must learn from this data and be able to predict the average housing price in any district.

  Concept covered: Techniques of Machine Learning

  Case Study 1: From the given dataset, predict whether consumers will buy houses or not given their salary and age

  Project 1: What are the problems that you find in the plot produced by the code, on the basis of 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: Illustrate methods to deal with categorical data, missing data, and data standardization using the information given in the dataset

  Project 3: Review the training dataset (Excel file). Note that the weights are not available 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 earlier?

  Case Study 3: Show how to reduce data dimensions from 3D to 2D from 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: Demonstrate how to reduce data dimensions from 3D to 2D from the given information

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

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

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

  Case Study 5: Estimate the insurance premium per year based on a person’s age, by making use of the Decision Trees and the information provided in the dataset

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

  Case Study 6: Demonstrate the Decision Tree regression and generate the random quadratic data

  Project 12: Observe the output on modifying the max_depth from 2 to 3 or 4

  Project 13: Discern the output after modifying the max_depth to 20.

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

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

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

  Case Study 7: By making use of the Random Forests,  predict the insurance per year based on the person’s age.

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

  Case Study 8:  Establish various regression techniques over a random dataset by making use of 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 explanation for these charts.

  Project 19: The program represents the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Try to alter the estimations to 0.001, 0.25, and 0.9 and examine the results. Provide an explanation.

  Concept Covered: Classification

  Case Study 9: Given the consumers' age and salary, find out if they will buy the houses. Utilize the data given in the dataset

  Project 20: Typically, the value of nearest_neighbors for testing class in KNN is 5. Alter the code to transform the value of nearest_neighbours to 2 and 20, and record the observations.

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

  Project 21: Alter the kernel trick from RBF to linear to observe the type of classifier 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 the RBF kernel trick with gamma = 1.0. Explain the output.

  Case Study 11: Using Decision Trees classifies the IRIS flower dataset. Make use of the information given

  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 utilizing the various classification algorithms. Make use of the information given

  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 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. Check the output, and record your observations.

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

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

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

  Participants in this Machine Learning course in Pune 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 average salary companies pay for a Machine Learning professionals in Pune?

  Machine Learning engineers have a median salary of 8 lakhs per year in Pune, according to the analysis by Payscale and other top research firms like Glassdoor. However, an experienced Machine Learning professional can earn up to 18 lakhs per year. It varies depending on the total experience, city, and more. Simplilearn’s Machine Learning Engineer Training enables ML professionals to request for salary hike beyond the average when they shift in the Market or can increase their earning potential if they are already employed.

• What roles companies offer for Machine Learning professionals in Pune?

  Within the AI & Machine Learning space available in Pune, the other roles are:

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

• Which companies are hiring Machine Learning Engineers in Pune?

  The companies that hire skilled AI & Machine Learning experts in Pune are:

  • Qualys,
  • NVIDIA,
  • Han Digital Solution,
  • TOMTOM, and
  • Barclays.

• 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 execution of projects. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.

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

  Yes, cancellation of your enrolment is possible if necessary. Simplilearn will refund your course fee after deducting the administrative fee. Read our Refund Policy for more details.

• Are there any group discounts for classroom training programs?

   

  Yes, group discount options are available for our training programs. You can contact Simplilearn by selecting the Live Chat link or using the form on the right of any page on our website. You can also get additional details from our customer service representatives.

• 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.

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

  Yes, the Machine Learning course training in Pune 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 price of the Machine Learning course training in Pune?

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

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

  No matter which area of Pune you are in, be it Koregaon Park, Kalyani Nagar, Boat Club Road, Hadapsar, Wagholi, Sangamwadi, Shukrawar Peth, Ashok Nagar, Shivaji Nagar anywhere. You can access our Machine Learning course online sitting at home or office.

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

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

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

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

• 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.