Machine Learning Certification Course in Pune, India

On completing this Machine Learning course, you will:

• Be introduced to Machine Learning
• 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.

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:

• 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

The price of Simplilearn’s Machine Learning Certification training is:

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

Simplilearn’s Machine Learning Certification Course in Pune has two learning methodologies:

1. The first is the Self-paced e-Learning methodology with a validity of 180 days (6 months). This enables the students to work at their own pace through high-quality e-learning video modules.
2. The second methodology is the Online Classroom Flexi-Pass with a validity of 180 days (6 months). This consists of high-quality e-learning videos along with access to 8+ instructor-led online training classes for 90 days.

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.