Machine Learning automates the data analysis process and allows computers to learn and adapt through experience. Simplilearn’s Machine Learning course in Bangalore will make you proficient in the techniques and concepts involved in Machine Learning. These concepts include mathematical and heuristic aspects, supervised and unsupervised learning and hands-on modeling to develop algorithms. With these concepts, you can be well-prepared for the role of a Machine Learning Engineer.
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What is Flexi Pass?
Machine Learning is a form of artificial intelligence that is transforming the world of computing together with people’s digital interactions. Machine Learning has become critical to numerous new and future applications by making it possible to cheaply, quickly, and automatically 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.
The Machine Learning course in Bangalore prepares data scientists, engineers, and other professionals with the practical skills and knowledge required for job competency and certification in Machine Learning. There is a growing demand for Machine Learning skills around the globe. The average salary of a Machine Learning Engineer according to payscale.com is $134,293 (USD).
On completion of this Machine Learning Course in Bangalore, you can:
The demand for skilled Machine Learning engineers is increasing across all industries. This makes the Machine Learning certification course in Bangalore appropriate for candidates at an intermediate level of experience. Simplilearn particularly recommends this Machine Learning training in Bangalore for the following set of professionals:
Simplilearn's Machine Learning Training course is practical and code-driven. The mathematical problem formulation and the theoretical motivation must be provided only while introducing the concepts.
This course has a primary capstone project and more than 25 ancillary exercises based on the 17 machine learning algorithms.
Capstone Project Details:
Project Name: Predicting house prices in California
Description: The project involves building a model that predicts average house values in Californian districts. Metrics such as median housing price, population, median income, and so on for each block group in California will be given to you. 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 that you build must be able to learn and predict the median housing price in any district.
Concept covered: Techniques of Machine Learning
Case Study 1: From the given dataset, predict whether the patrons will buy houses or not, given 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 is the estimated price of the houses with areas 1700 and 1900?
Concept covered: Data Preprocessing
Case Study 2: By using the information given in the dataset, illustrate the methods to handle categorical data, missing data, and data standardization.
Project 3: Review the training dataset (Excel file). Observe that the weight for the fifth and eighth rows is missing. 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 re-execute 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: Illustrate how you can reduce the data dimensions from 3D to 2D by making use of 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: Show how you can reduce the data dimensions from 3D to 2D by making use of the information given
Project 8: Alter the degree of the polynomial from the 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 the yearly insurance premium based on a person’s age by using the Decision Trees and 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 dataset provided.
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 result obtained.
Project 13: Modify the max_depth to 20, and observe the result
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 given
Project 16: Alter 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 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 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: Classify 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 given information
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 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 result.
Project 29: Modify the number of clusters k to 6, and note the observations.
Participants in this Machine Learning online course should have:
Some of the qualifications you must satisfy to be eligible for getting Simplilearn’s Machine Learning course completion certificate that is globally recognized and accepted are:
Online Self-Learning:
Online Classroom:
For candidates to attend this Machine Learning training in Bangalore, they must:
The concepts of statistics and mathematics that needed for Machine Learning is covered in this course. When you purchase Simplilearn’s Machine Learning course, a complimentary Python course is also provided.
The certification will be awarded to the candidates after successfully completing this Machine Learning course.
No. This Machine Learning Course is not accredited by any standard organization.
From this Machine Learning course, you can learn to programme in R language or Python; learn about real-time machine learning applications, implement machine learning using Tensorflow framework etc. Once you possess the basic skill set, you can gain practical experience by taking part in Kaggle competitions, applying for a Machine Learning internship, and completing personal engineering projects etc.
Some of the qualifications you must satisfy to be eligible for getting Simplilearn’s Machine Learning course completion certificate are:
Online Self-Learning:
Online Classroom:
If you fail Simplilearn’s certification exam, you can retake it immediately.
Yes. For many training programs, Simplilearn offers a cash-back guarantee. You can submit refund requests through our Help and Support portal and also refer to our Refund Policy.
Online Classroom:
Online Self-Learning:
Yes, we provide 1 practice test as part of our course to help you prepare for the actual certification exam. You can try this Machine Learning Multiple Choice Questions - Free Practice Test to understand the type of tests that are part of the course curriculum.
​Named by Onalytica as the No.1 influencer in AI and 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 a 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 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 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 lessThe course was very informative. The study material provided by the trainer was extremely helpful and very easy to understand.
I have enrolled in Data Science using Python and Machine Learning courses on Simplilearn. Course content is very well structured and relates to the current trends in technology and markets, as well. Each session is designed with real-time examples. Instructor-led training has given us excellent exposure. The trainer provides an understanding of all topics through practical exercises. The support team is very responsive. Thanks to Simplilearn team for the wonderful platform provided.
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 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 material was very engaging and helpful. The Trainer's in-depth knowledge helped to understand Machine Learning better.
The median annual salary of the Machine Learning engineer in Bangalore is Rs 12,28,665, according to PayScale.com. Individuals who have undergone the Machine Learning training have the potential to earn more.
Companies that are looking for skilled AI & Machine Learning experts in Bangalore are:
Yes, you can revoke your enrolment if it is necessary. After deducting the course fee we will provide a refund of your course fee. Please read our Refund Policy to learn more.
The enrollment for this training program can be done on the Simplilearn website and an online payment can be made through any one of the following methods:
Once the payment is received, you will automatically get an email of the payment receipt along with the access information.
Please contact Simplilearn by selecting the Live Chat link or by making use of the form on the right side of any page on our website. You can get more details from our customer service representatives.
Simplilearn provides 24/7 support through telephone, chat, and email. Our dedicated team can provide you with an on-demand assistance through Simplilearn’s community forum. You will get a lifetime access to our community forum even after you complete the course with us.
With Simplilearn’s Flexi-pass, you can attend the classes without hampering your busy schedule. With this course, you will get an advantage of being trained by renowned trainers with an industry experience of many years. These trainers combine the best of self-paced learning with online classroom training. This Flexi-pass also provides you with access to 15 sessions for a duration of 90 days.
Simplilearn offers CloudLab, which is a cloud-based Python environment lab along with this Machine Learning course for smooth execution of the practical projects. With this platform, candidates don’t have to install or maintain Python and it’s libraries on a virtual machine. They can instead access the pre-configured environment on CloudLab on your browser.
From the Simplilearn Learning Management System (LMS), you will be given access to our online CloudLab platform for the duration of the course.
You can complete the projects by making use of Simplilearn’s CloudLab
You can contact Simplilearn by selecting the Live Chat link or by making use of the form on the right of any page on our website. You can also contact our Help & Support.
The online classroom training conducted for the Machine Learning Certification Course is through live online streaming. A Machine Learning certified instructor with training and working experience of more than 15 years handles the classes.
Yes, Simplilearn’s course materials and training are effective as they guarantee success in the Machine Learning certification exam.
Simplilearn’s highly qualified instructors are all industry experts with decades of relevant teaching experience. All the instructors have undergone a meticulous selection process including a training demo, technical evaluation, and profile screening before getting certified to train for us. It is also assured that instructors with a high alumni rating continue as our faculty.
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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