This Machine Learning course in Mumbai 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 Mumbai 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 Mumbai 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

14:30Preview##### 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

14:27Preview##### 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

12:36##### 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

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

06:31

#### Lesson 13 The fundamentals of regression analysis

18:32##### 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

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

14:42

### What do I need to do to unlock my Simplilearn certificate?

Depending on the learning mode, Simplilearn's Machine Learning course completion certificate can be obtained if:

- The candidate completes one batch of Machine Learning course in Mumbai and finishes one project
- The candidate completes 85% of the self-paced course and finishes one project

### What are the prerequisites for learning Machine Learning course in Mumbai?

The prerequisites for this Machine Learning course in Mumbai are:

- Knowledge of basic high school mathematics
- Clarity on the concepts of Python programming
- Fundamental understanding of statistics

Simplilearn provides a free Python course along with the Machine Learning course to help you brush up your knowledge of statistics and mathematics concepts.

### Who provides the certification?

The Machine Learning course in Mumbai will be provided by Simplilearn to the candidates who complete the course successfully.

### Is this course accredited?

No, the Machine Learning course offered by Simplilearn is not accredited officially.

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

Candidates need to spend 45 - 50 hours of learning for the successful completion of the Machine Learning certification course.

### How long does Simplilearn's certificate for Machine Learning course valid for?

Simplilearn’s Machine Learning certification is valid for a lifetime.

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.

### Is this Machine Learning course training in Mumbai suitable for freshers?

Yes, the Machine Learning course training in Mumbai is suitable for freshers, and this course helps you learn Machine Learning topics including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling.

### What is the price of the Machine Learning course training in Mumbai?

The price of the Machine Learning course training in Mumbai starts from Rs. 18,999/-.

### In which areas of Mumbai is the Machine Learning training conducted?

No matter which area of Mumbai you are in, be it Bandra, Malabar Hill, Juhu, Worli, Altamount Road, Andheri, Powai, Versova anywhere. You can access our Machine Learning course online sitting at home or office.

### Do you provide this Machine Learning training in Mumbai with placement?

No, currently, we do not provide any placement assistance with the Machine Learning course.

### Why do I need to choose Simplilearn to learn Machine Learning in Mumbai?

Simplilearn provides instructor-led training, lifetime access to self-paced learning, training from industry experts, and real-life industry projects with multiple video lessons.

### Why Learn Machine Learning Certification?

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.

Simplilearn's Machine Learning Training Course in Mumbai Address: 74/II, “C” Cross Road, Opp Gate No 2, Seepz, Andheri East, Mumbai- 400093, Maharashtra, India Call us @ 1800-212-7688

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