This Machine Learning course in Pune offers an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling. Learn how to use Python in this Machine Learning training course to draw predictions from data.

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At Simplilearn, we value the trust of our patrons immensely. But, if you feel that a course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!

- Gain expertise with 25+ hands-on exercises
- 4 real-life industry projects with integrated labs
- Dedicated mentoring sessions from industry experts
- 44 hours of instructor-led training with certification

- Supervised and unsupervised learning
- Time series modeling
- Linear and logistic regression
- Kernel SVM
- Decision trees
- KMeans clustering
- Naive Bayes
- Decision tree
- Random forest classifiers
- Boosting and Bagging techniques
- Deep Learning fundamentals

The Machine Learning market is expected to reach USD $8.81 Billion by 2022, at a growth rate of 44.1-percent, indicating the increased adoption of Machine Learning among companies. By 2020, the demand for Machine Learning engineers is expected to grow by 60-percent.

- Designation
- Annual Salary
- Hiring Companies

- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed
- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed

The Machine Learning course in Pune is well-suited for participants at the intermediate level including, analytics managers, business analysts, information architects, developers looking to become data scientists, and graduates seeking a career in Data Science and Machine Learning.

This Machine Learning course in Pune requires an understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial. You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science, before getting into the Machine Learning online course.

### Machine Learning

Preview#### Lesson 01 Course Introduction

06:41Preview##### Course Introduction

05:31##### Accessing Practice Lab

01:10

#### Lesson 02 Introduction to AI and Machine Learning

19:36Preview##### 2.1 Learning Objectives

00:43##### 2.2 Emergence of Artificial Intelligence

01:56##### 2.3 Artificial Intelligence in Practice

01:48##### 2.4 Sci-Fi Movies with the Concept of AI

00:22##### 2.5 Recommender Systems

00:45##### 2.6 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A

02:47##### 2.7 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B

01:23##### 2.8 Definition and Features of Machine Learning

01:30##### 2.9 Machine Learning Approaches

01:48##### 2.10 Machine Learning Techniques

02:21##### 2.11 Applications of Machine Learning: Part A

01:34##### 2.12 Applications of Machine Learning: Part B

02:11##### 2.13 Key Takeaways

00:28##### Knowledge Check

#### Lesson 03 Data Preprocessing

36:19Preview##### 3.1 Learning Objectives

00:38##### 3.2 Data Exploration Loading Files: Part A

02:52##### 3.2 Data Exploration Loading Files: Part B

01:56##### 3.3 Demo: Importing and Storing Data

01:27##### Practice: Automobile Data Exploration - A

##### 3.4 Data Exploration Techniques: Part A

02:56##### 3.5 Data Exploration Techniques: Part B

02:47##### 3.6 Seaborn

02:18##### 3.7 Demo: Correlation Analysis

02:38##### Practice: Automobile Data Exploration - B

##### 3.8 Data Wrangling

01:27##### 3.9 Missing Values in a Dataset

01:55##### 3.10 Outlier Values in a Dataset

01:49##### 3.11 Demo: Outlier and Missing Value Treatment

04:18##### Practice: Data Exploration - C

##### 3.12 Data Manipulation

00:47##### 3.13 Functionalities of Data Object in Python: Part A

01:49##### 3.14 Functionalities of Data Object in Python: Part B

01:33##### 3.15 Different Types of Joins

01:32##### 3.16 Typecasting

01:23##### 3.17 Demo: Labor Hours Comparison

01:54##### Practice: Data Manipulation

##### 3.18 Key Takeaways

00:20##### Knowledge Check

##### Storing Test Results

#### Lesson 04 Supervised Learning

01:21:04Preview##### 4.1 Learning Objectives

00:31##### 4.2 Supervised Learning

02:17##### 4.3 Supervised Learning- Real-Life Scenario

00:53##### 4.4 Understanding the Algorithm

00:52##### 4.5 Supervised Learning Flow

01:50##### 4.6 Types of Supervised Learning: Part A

01:54##### 4.7 Types of Supervised Learning: Part B

02:03##### 4.8 Types of Classification Algorithms

01:01##### 4.9 Types of Regression Algorithms: Part A

03:20##### 4.10 Regression Use Case

00:34##### 4.11 Accuracy Metrics

01:23##### 4.12 Cost Function

01:48##### 4.13 Evaluating Coefficients

00:53##### 4.14 Demo: Linear Regression

13:47##### Practice: Boston Homes - A

##### 4.15 Challenges in Prediction

01:45##### 4.16 Types of Regression Algorithms: Part B

02:40##### 4.17 Demo: Bigmart

21:55##### Practice: Boston Homes - B

##### 4.18 Logistic Regression: Part A

01:58##### 4.19 Logistic Regression: Part B

01:38##### 4.20 Sigmoid Probability

02:05##### 4.21 Accuracy Matrix

01:36##### 4.22 Demo: Survival of Titanic Passengers

14:07##### Practice: Iris Species

##### 4.23 Key Takeaways

00:14##### Knowledge Check

##### Health Insurance Cost

#### Lesson 05 Feature Engineering

27:52Preview##### 5.1 Learning Objectives

00:27##### 5.2 Feature Selection

01:28##### 5.3 Regression

00:53##### 5.4 Factor Analysis

01:57##### 5.5 Factor Analysis Process

01:05##### 5.6 Principal Component Analysis (PCA)

02:31##### 5.7 First Principal Component

02:43##### 5.8 Eigenvalues and PCA

02:32##### 5.9 Demo: Feature Reduction

05:47##### Practice: PCA Transformation

##### 5.10 Linear Discriminant Analysis

02:27##### 5.11 Maximum Separable Line

00:44##### 5.12 Find Maximum Separable Line

03:12##### 5.13 Demo: Labeled Feature Reduction

01:53##### Practice: LDA Transformation

##### 5.14 Key Takeaways

00:13##### Knowledge Check

##### Simplifying Cancer Treatment

#### Lesson 06 Supervised Learning Classification

55:43Preview##### 6.1 Learning Objectives

00:34##### 6.2 Overview of Classification

02:05##### Classification: A Supervised Learning Algorithm

00:52##### 6.4 Use Cases of Classification

02:37##### 6.5 Classification Algorithms

00:16##### 6.6 Decision Tree Classifier

02:17##### 6.7 Decision Tree Examples

01:45##### 6.8 Decision Tree Formation

00:47##### 6.9 Choosing the Classifier

02:55##### 6.10 Overfitting of Decision Trees

01:00##### 6.11 Random Forest Classifier- Bagging and Bootstrapping

02:22##### 6.12 Decision Tree and Random Forest Classifier

01:06##### Performance Measures: Confusion Matrix

02:21##### Performance Measures: Cost Matrix

02:06##### 6.15 Demo: Horse Survival

08:30##### Practice: Loan Risk Analysis

##### 6.16 Naive Bayes Classifier

01:28##### 6.17 Steps to Calculate Posterior Probability: Part A

01:44##### 6.18 Steps to Calculate Posterior Probability: Part B

02:21##### 6.19 Support Vector Machines : Linear Separability

01:05##### 6.20 Support Vector Machines : Classification Margin

02:05##### 6.21 Linear SVM : Mathematical Representation

02:04##### 6.22 Non-linear SVMs

01:06##### 6.23 The Kernel Trick

01:19##### 6.24 Demo: Voice Classification

10:42##### Practice: College Classification

##### 6.25 Key Takeaways

00:16##### Knowledge Check

##### Classify Kinematic Data

#### Lesson 07 Unsupervised Learning

28:26Preview##### 7.1 Learning Objectives

00:29##### 7.2 Overview

01:48##### 7.3 Example and Applications of Unsupervised Learning

02:17##### 7.4 Clustering

01:49##### 7.5 Hierarchical Clustering

02:28##### 7.6 Hierarchical Clustering Example

02:01##### 7.7 Demo: Clustering Animals

05:39##### Practice: Customer Segmentation

##### 7.8 K-means Clustering

01:46##### 7.9 Optimal Number of Clusters

01:24##### 7.10 Demo: Cluster Based Incentivization

08:32##### Practice: Image Segmentation

##### 7.11 Key Takeaways

00:13##### Knowledge Check

##### Clustering Image Data

#### Lesson 08 Time Series Modeling

37:44Preview##### 8.1 Learning Objectives

00:24##### 8.2 Overview of Time Series Modeling

02:16##### 8.3 Time Series Pattern Types: Part A

02:16##### 8.4 Time Series Pattern Types: Part B

01:19##### 8.5 White Noise

01:07##### 8.6 Stationarity

02:13##### 8.7 Removal of Non-Stationarity

02:13##### 8.8 Demo: Air Passengers - A

14:33##### Practice: Beer Production - A

##### 8.9 Time Series Models: Part A

02:14##### 8.10 Time Series Models: Part B

01:28##### 8.11 Time Series Models: Part C

01:51##### 8.12 Steps in Time Series Forecasting

00:37##### 8.13 Demo: Air Passengers - B

05:01##### Practice: Beer Production - B

##### 8.14 Key Takeaways

00:12##### Knowledge Check

##### IMF Commodity Price Forecast

#### Lesson 09 Ensemble Learning

35:41Preview##### 9.01 Ensemble Learning

00:24##### 9.2 Overview

02:41##### 9.3 Ensemble Learning Methods: Part A

02:28##### 9.4 Ensemble Learning Methods: Part B

02:37##### 9.5 Working of AdaBoost

01:43##### 9.6 AdaBoost Algorithm and Flowchart

02:28##### 9.7 Gradient Boosting

02:36##### 9.8 XGBoost

02:23##### 9.9 XGBoost Parameters: Part A

03:15##### 9.10 XGBoost Parameters: Part B

02:30##### 9.11 Demo: Pima Indians Diabetes

04:14##### Practice: Linearly Separable Species

##### 9.12 Model Selection

02:08##### 9.13 Common Splitting Strategies

01:45##### 9.14 Demo: Cross Validation

04:18##### Practice: Model Selection

##### 9.15 Key Takeaways

00:11##### Knowledge Check

##### Tuning Classifier Model with XGBoost

#### Lesson 10 Recommender Systems

25:45Preview##### 10.1 Learning Objectives

00:28##### 10.2 Introduction

02:17##### 10.3 Purposes of Recommender Systems

00:45##### 10.4 Paradigms of Recommender Systems

02:45##### 10.5 Collaborative Filtering: Part A

02:14##### 10.6 Collaborative Filtering: Part B

01:58##### 10.7 Association Rule Mining

01:47##### Association Rule Mining: Market Basket Analysis

01:43##### 10.9 Association Rule Generation: Apriori Algorithm

00:53##### 10.10 Apriori Algorithm Example: Part A

02:11##### 10.11 Apriori Algorithm Example: Part B

01:18##### 10.12 Apriori Algorithm: Rule Selection

02:52##### 10.13 Demo: User-Movie Recommendation Model

04:19##### Practice: Movie-Movie recommendation

##### 10.14 Key Takeaways

00:15##### Knowledge Check

##### Book Rental Recommendation

#### Lesson 11 Text Mining

43:58Preview##### 11.1 Learning Objectives

00:22##### 11.2 Overview of Text Mining

02:11##### 11.3 Significance of Text Mining

01:26##### 11.4 Applications of Text Mining

02:23##### 11.5 Natural Language ToolKit Library

02:35##### 11.6 Text Extraction and Preprocessing: Tokenization

00:33##### 11.7 Text Extraction and Preprocessing: N-grams

00:55##### 11.8 Text Extraction and Preprocessing: Stop Word Removal

01:24##### 11.9 Text Extraction and Preprocessing: Stemming

00:44##### 11.10 Text Extraction and Preprocessing: Lemmatization

00:35##### 11.11 Text Extraction and Preprocessing: POS Tagging

01:17##### 11.12 Text Extraction and Preprocessing: Named Entity Recognition

00:54##### 11.13 NLP Process Workflow

00:53##### 11.14 Demo: Processing Brown Corpus

10:05##### Practice: Wiki Corpus

##### 11.15 Structuring Sentences: Syntax

01:54##### 11.16 Rendering Syntax Trees

00:55##### 11.17 Structuring Sentences: Chunking and Chunk Parsing

01:38##### 11.18 NP and VP Chunk and Parser

01:39##### 11.19 Structuring Sentences: Chinking

01:44##### 11.20 Context-Free Grammar (CFG)

01:56##### 11.21 Demo: Structuring Sentences

07:46##### Practice: Airline Sentiment

##### 11.22 Key Takeaways

00:09##### Knowledge Check

##### FIFA World Cup

#### Lesson 12 Project Highlights

02:40##### Project Highlights

02:40##### Uber Fare Prediction

##### Amazon - Employee Access

#### Practice Projects

##### California Housing Price Prediction

##### Phishing Detector with LR

- Free Course
### Statistics Essential for Data Science

Preview#### Lesson 1 Introduction

02:55Preview##### 1.1 Introduction

02:55

#### Lesson 2 Sample or population data

03:56Preview##### 2.1 Sample or population data

03:56

#### Lesson 3 The fundamentals of descriptive statistics

21:18Preview##### 3.1 The fundamentals of descriptive statistics

03:18##### 3.2 Levels of measurement

02:57##### 3.3 Categorical variables. Visualization techniques for categorical variables

04:06##### 3.4 Numerical variables. Using a frequency distribution table

03:24##### 3.5 Histogram charts

02:27##### 3.6 Cross tables and scatter plots

05:06

#### Lesson 4 Measures of central tendency, asymmetry, and variability

25:17Preview##### 4.1 Measures of central tendency, asymmetry, and variability

04:24##### 4.2 Measuring skewness

02:43##### 4.3 Measuring how data is spread out calculating variance

05:58##### 4.4 Standard deviation and coefficient of variation

04:54##### 4.5 Calculating and understanding covariance

03:31##### 4.6 The correlation coefficient

03:47

#### Lesson 5 Practical example descriptive statistics

14:30##### 5.1 Practical example descriptive statistics

14:30

#### Lesson 6 Distributions

16:17Preview##### 6.1 Distributions

01:02##### 6.2 What is a distribution

03:40##### 6.3 The Normal distribution

03:45##### 6.4 The standard normal distribution

02:51##### 6.5 Understanding the central limit theorem

03:40##### 6.6 Standard error

01:19

#### Lesson 7 Estimators and Estimates

23:36Preview##### 7.1 Estimators and Estimates

02:36##### 7.2 Confidence intervals - an invaluable tool for decision making

06:31##### 7.3 Calculating confidence intervals within a population with a known variance

02:30##### 7.4 Student’s T distribution

03:14##### 7.5 Calculating confidence intervals within a population with an unknown variance

04:07##### 7.6 What is a margin of error and why is it important in Statistics

04:38

#### Lesson 8 Confidence intervals advanced topics

14:27##### 8.1 Confidence intervals advanced topics

04:47##### 8.2 Calculating confidence intervals for two means with independent samples (part One)

04:36##### 8.3 Calculating confidence intervals for two means with independent samples (part two)

03:40##### 8.4 Calculating confidence intervals for two means with independent samples (part three)

01:24

#### Lesson 9 Practical example inferential statistics

09:37##### 9.1 Practical example inferential statistics

09:37

#### Lesson 10 Hypothesis testing Introduction

12:36Preview##### 10.1 Hypothesis testing Introduction

04:56##### 10.2 Establishing a rejection region and a significance level

04:20##### 10.3 Type I error vs Type II error

03:20

#### Lesson 11 Hypothesis testing Let's start testing!

26:39Preview##### 11.1 Hypothesis testing Let's start testing!

06:07##### 11.2 What is the p-value and why is it one of the most useful tool for statisticians

03:55##### 11.3 Test for the mean. Population variance unknown

04:26##### 11.4 Test for the mean. Dependent samples

04:45##### 11.5 Test for the mean. Independent samples (Part One)

03:38##### 11.6 Test for the mean. Independent samples (Part Two)

03:48

#### Lesson 12 Practical example hypothesis testing

06:31##### 12.1 Practical example hypothesis testing

06:31

#### Lesson 13 The fundamentals of regression analysis

18:32Preview##### 13.1 The fundamentals of regression analysis

01:02##### 13.2 Correlation and causation

04:06##### 13.3 The linear regression model made easy

05:02##### 13.4 What is the difference between correlation and regression

01:28##### 13.5 A geometrical representation of the linear regression model

01:18##### 13.6 A practical example - Reinforced learning

05:36

#### Lesson 14 Subtleties of regression analysis

23:25Preview##### 14.1 Subtleties of regression analysis

02:04##### 14.2 What is Rsquared and how does it help us

05:00##### 14.3 The ordinary least squares setting and its practical applications

02:08##### 14.4 Studying regression tables

04:34##### 14.5 The multiple linear regression model

02:42##### 14.6 Adjusted R-squared

04:57##### 14.7 What does the F-statistic show us and why we need to understand it

02:00

#### Lesson 15 Assumptions for linear regression analysis

19:16Preview##### 15.1 Assumptions for linear regression analysis

02:11##### 15.2 Linearity

01:40##### 15.3 No endogeneity

03:43##### 15.4 Normality and homoscedasticity

05:09##### 15.5 No autocorrelation

03:11##### 15.6 No multicollinearity

03:22

#### Lesson 16 Dealing with categorical data

05:20##### 16.1 Dealing with categorical data

05:20

#### Lesson 17 Practical example regression analysis

14:42Preview##### 17.1 Practical example regression analysis

14:42

### How will you get Simplilearn's Machine Learning Course Completion Certificate?

Some of the qualifications you must satisfy to gain eligibility for the globally recognize Simplilearn’s course completion Certificate are:

**Online Classroom:**- Completion of one project.
- Attending one complete batch of Machine Learning course training in Pune.

**Online Self-Learning:**- Completion of one project.
- Completing 85% of the course.

### What are the prerequisites for Machine Learning course in Pune?

Candidates in this Machine Learning course in Pune should:

- Have an understanding of the basics of statistics
- Be aware of basic high school mathematics
- Be familiar with the fundamentals of Python programming

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.

### How do I become a Machine Learning Engineer?

This course will give you a complete overview of Machine Learning methodologies, enough to prepare you to excel in your next role as a Machine Learning Engineer. You will earn Simplilearn’s Machine Learning certification that will attest to your new skills and on-the-job expertise. Get familiar with regression, classification, time series modelling, and clustering.

### Is this course accredited?

No. This Machine Learning Certification course is not certified officially by any standard organization.

### How long does it to take to complete the Machine Learning certification course?

The successful completion time for this Machine Learning certification course is about 40-50 hours.

### Is this course accredited?

No, this course is not officially accredited by any standard or organization.

Simplilearn’s Blended Learning model brings classroom learning experience online with its world-class LMS. It combines instructor-led training, self-paced learning and personalized mentoring to provide an immersive learning experience.

### Why learn Machine Learning?

- The machine learning market size is expected to grow 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.
- With Machine learning taking over the world, the companies greatly need professionals to be aware of the ins and outs of machine learning.

### What are the objectives of this Machine Learning course in Mumbai?

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 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).

### What skills you learn in this Machine Learning course in Pune?

On completing this Machine Learning course in Pune, you will:

- 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.

### Who should take this Machine Learning course in Pune?

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 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

### What projects are included in Machine Learning course in Pune?

Simplilearn's Machine Learning course in Pune 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 an 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 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 the 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 a classification for the RBF kernel trick with gamma = 1.0. Explain the output.**Case Study 11: Using Decision Trees classifies the 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 the 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.### What are the prerequisites for this Machine Learning course in Pune?

Participants in this Machine Learning course in Pune should have:

- Familiarity with the fundamentals of Python programming
- Fair understanding of the basics of statistics and mathematics

And, here are some of the fundamental courses that participants need to know before they get into Machine Learning online course:

- Python for Data Science
- Math Refresher
- Statistics Essential for Data Science

Simplilearn's Machine Learning Training Course in Pune Address: Sky Loft, Creaticity, Off, Airport Rd, opp. Golf Course, Shastrinagar, Yerawada, Pune - 411006, Maharashtra, India Call us @ 1800-212-7688

- Disclaimer
- PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.