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For Self-Paced Learning :
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Raise refund request within 7 days of commencement of the first batch you are eligible to attend. Money back guarantee is void if the participant has accessed more than 25% content of an e-learning course or has attended Online Classrooms for more than 1 day.
What is Flexi Pass?
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 a 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).
On completing this Machine Learning course, you will:
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 certification 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:
The price of Simplilearn’s Machine Learning Certification training is:
Simplilearn’s Machine Learning Certification Course in Pune has two learning methodologies:
Simplilearn's Machine Learning Training course 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 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 the 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 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 classification for RBF kernel trick with gamma = 1.0. Explain the output.
Case Study 11: Using Decision Trees classify 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 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.
Participants in this Machine Learning online course should have:
Some of the qualifications you must satisfy to gain eligibility for the globally recognize Simplilearn’s course completion Certificate are:
Online Classroom:
Online Self-Learning:
Candidates in this Machine Learning online course should:
This course covers the concepts of statistics and mathematics that are necessary for machine learning and Simplilearn will provide you with a free Python course on purchasing our Machine Learning Certification course.
Simplilearn will award you the machine learning certification after successfully completing this course.
No. This Machine Learning Certification course is not certified officially by any standard organization.
The successful completion time for this Machine Learning certification course is about 40-50 hours.
You can become a machine learning engineer by learning to code in R language or Python, implementation of machine learning using the Tensorflow framework, real-time machine learning applications, etc. By making use of this online machine learning course you can consider getting a Programming language certification and the degree to become a more valuable candidate. Once you have a basic skill set, you can get experienced by completing personal engineering projects, taking up the machine learning internship and participating in Kaggle competitions etc.
Some of the qualifications you must satisfy to gain eligibility for the globally recognize Simplilearn’s course completion Certificate are:
Online Classroom:
Online Self-Learning:
No, this course is not officially accredited by any standard or organization.
Machine Learning Course Certificate by Simplilearn has a lifelong validity.
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 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.
The course material was very engaging and helpful. The Trainer's in-depth knowledge helped to understand Machine Learning better.
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.
Read more Read lessIt 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.
Read more Read lessMy 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.
Read more Read lessI 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.
Read more Read lessSimplilearn 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.
Read more Read lessI 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.
Read more Read lessThe course was very informative. The study material provided by the trainer was extremely helpful and very easy to understand.
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!
Read more Read lessThe 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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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.
Within the AI & Machine Learning space available in Pune, the other roles are:
The companies that hire skilled AI & Machine Learning experts in Pune are:
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.
Our teaching assistants are a dedicated team of subject matter experts here to help you get certified 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.
Yes. We do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support portal.
You can enrol for this training on our website and make an online payment using any of the following options:
Once payment is received you will automatically receive a payment receipt and access information via email.
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.
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.
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