Machine Learning Certification Course in Bangalore, India

On completion of this Machine Learning Course in Bangalore, you can:

• Get a thorough knowledge of the heuristic and mathematical aspects of machine learning.
• Understand the theoretical concepts and their relation with the practical aspects of machine learning.
• Get an introduction to machine learning & real-time applications
• Model a wide variety of robust machine learning algorithms including recommendation systems, deep learning, and clustering.
• Gain practical mastery over the algorithms, principles, and applications of machine learning with the help of a practical learning approach which involves working on 28 projects and one capstone project.
• Be skillful in the concepts of reinforcement, unsupervised, and supervised learning concepts and modeling.
• Understand the concepts and operation of decision tree classifier, random forest classifier, naive Bayes, support vector machines, K-nearest neighbors, kernel SVM, logistic regression, K-means clustering and more.

The cost of Simplilearn’s Machine Learning Certification training in Bangalore will be:

• INR Rs.20,999/- for candidates applying for the Online Classroom Flexi-Pass mode and
• INR. RS.12999/- for candidates applying for the Self Paced Learning module.

There are two learning methodologies for Simplilearn’s Machine Learning Course in Bangalore:

• The first is the Self-paced e-Learning methodology with 6 months (180 days) validity. This provides candidates with high-quality e-learning video modules that let them work at their own pace.
• The second methodology is the Online Classroom Flexi-Pass. Here, learners will get access to high-quality e-learning videos for a duration of 6 months (180 days) along with an access to 8+ instructor-led online training classes for a duration of 90 days.

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:

• Graduates eager to establish their career in machine learning and data science
• Information architects who want to get skilled in the Machine Learning algorithms
• Analytics managers who are leading a team of analysts
• Developers aspiring to be a data scientist or machine learning engineer
• Business analysts who want to understand data science techniques
• Experienced professionals who would like to harness machine learning in their fields to get more insights
• Analytics professionals who want to work in machine learning or artificial intelligence

The different job roles offered for the Machine Learning trained professionals in Bangalore are:

• Core AI Engineers
• Machine Learning scientists
• Machine Learning Engineer
• Data analysts
• Data scientists

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.