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What is Flexi Pass?
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.
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.
The candidates taking the Machine Learning course in Mumbai will be able to do the following:
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:
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.
The prerequisites for this Machine Learning course are:
Simplilearn provides a free Python course along with the Machine Learning course to help you brush up your knowledge of statistics and mathematics concepts.
Depending on the learning mode, Simplilearn's Machine Learning Course Completion Certificate can be obtained if:
The prerequisites for this Machine Learning course are:
Simplilearn provides a free Python course along with the Machine Learning course to help you brush up your knowledge of statistics and mathematics concepts.
The Machine Learning Certification will be provided by Simplilearn to the candidates who complete the course successfully.
No, the Machine Learning course offered by Simplilearn is not accredited officially.
Candidates need to spend 45 - 50 hours of learning for the successful completion of the Machine Learning certification course.
Simplilearn’s Machine Learning Certification is effective for the lifetime.
Online Classroom:
Online Self-Learning:
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.
Simplilearn 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 lessSimplilearn is a great start for the beginner as well for the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas on the concepts and exercises. I could finish my Machine Learning advance course very easily with good project exercise.
Read more Read lessSimplilearn'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 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.
The course material was very engaging and helpful. The Trainer's in-depth knowledge helped to understand Machine Learning better.
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!
Read more Read lessAs 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.
There are many roles belonging to the Machine Learning and Artificial Intelligence domain in Mumbai such as:
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.
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.
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.
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.
Candidates need to pay online to get themselves enrolled in our Machine Learning Course in Mumbai. The payment options available are:
An automatically generated receipt will be sent to the candidate via email once the payment is successful.
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.
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.
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.
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.
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)
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.
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.
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.
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.
Yes, for successful completion of the Machine Learning Certification, we provide state-of-the-art training and course material.
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.
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.
* The projects have been built leveraging real publicly available data-sets of the mentioned organizations.
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