Thanks to the skills you learn from this machine learning course in Calicut you gain the chance to develop a useful, working understanding of a bunch of valuable, career-enhancing topics like employing real-time data in the system and modeling time series. This machine learning course in Calicut, along with machine learning training in Calicut, can provide you with the resources to achieve these skills.

- Gain expertise with 25+ hands-on exercises
- 4 real-life industry projects with integrated labs
- Dedicated mentoring sessions from industry experts
- 58 hours of Applied Learning

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

This machine learning course in Calicut has tremendous future potential. Availing yourself of the machine learning course in Calicut and successfully finishing the machine learning training in Calicut will help you improve your career prospects by becoming one of the many engineers needed to fulfill the approaching high demands.

- Designation
- Annual Salary
- Hiring Companies

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

If you already have intermediate-level knowledge in Artificial Intelligence and Machine Learning, something characteristic of developers, data analysts, analytics managers, and data scientists, then you are eligible to take part in this machine learning training in Calicut. You can also do this course if you have a graduate degree in data science or computer science.

The chief necessary requirement you need to take advantage of this machine learning course in Calicut is having a fully developed understanding of mathematics and fundamental statistics at the college or university level. If you know programming languages like Python, it will benefit you a lot during the course. If you want to gain the full benefits from this AI and Machine Learning course in Calicut, it's mandatory that you already have taken and finished some prerequisite foundation courses like Essential Statistics or Math Refresher.

### Machine Learning

Preview#### Lesson 01: Course Introduction

09:19Preview##### 1.01 Course Introduction

06:08##### 1.02 Demo: Jupyter Lab Walk - Through

03:11

#### Lesson 02: Introduction to Machine Learning

08:40Preview##### 2.01 Learning Objectives

00:42##### 2.02 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A

02:46##### 2.03 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B

01:23##### 2.04 Definition and Features of Machine Learning

01:30##### 2.05 Machine Learning Approaches

01:46##### 2.06 Key Takeaways

00:33

#### Lesson 03: Supervised Learning Regression and Classification

02:10:59Preview##### 3.01 Learning Objectives

00:46##### 3.02 Supervised Learning

02:18##### 3.03 Supervised Learning: Real Life Scenario

00:55##### 3.04 Understanding the Algorithm

00:54##### 3.05 Supervised Learning Flow

01:51##### 3.06 Types of Supervised Learning: Part A

01:57##### 3.07 Types of Supervised Learning: Part B

02:05##### 3.08 Types of Classification Algorithms

01:03##### 3.09 Types of Regression Algorithms: Part A

03:23##### 3.10 Regression Use Case

00:36##### 3.11 Accuracy Metrics

01:24##### 3.12 Cost Function

01:49##### 3.13 Evaluating Coefficients

00:55##### 3.14 Demo: Linear Regression

13:48##### 3.15 Challenges in Prediction

01:47##### 3.16 Types of Regression Algorithms: Part B

02:40##### 3.17 Demo: Bigmart

37:29##### 3.18 Logistic Regression: Part A

02:01##### 3.19 Logistic Regression: Part B

01:41##### 3.20 Sigmoid Probability

02:07##### 3.21 Accuracy Matrix

01:28##### 3.22 Demo: Survival of Titanic Passengers

13:17##### 3.23 Overview of Classification

02:03##### 3.24 Classification: A Supervised Learning Algorithm

00:52##### 3.25 Use Cases

02:34##### 3.26 Classification Algorithms

00:17##### 3.27 Performance Measures: Confusion Matrix

02:21##### 3.28 Performance Measures: Cost Matrix

02:07##### 3.29 Naive Bayes Classifier

01:16##### 3.30 Steps to Calculate Posterior Probability: Part A

01:41##### 3.31 Steps to Calculate Posterior Probability: Part B

02:22##### 3.32 Support Vector Machines: Linear Separability

01:05##### 3.33 Support Vector Machines: Classification Margin

02:06##### 3.34 Linear SVM: Mathematical Representation

02:05##### 3.35 Non linear SVMs

01:07##### 3.36 The Kernel Trick

01:19##### 3.37 Demo: Voice Classification

10:42##### 3.38 Key Takeaways

00:48

#### Lesson 04: Decision Trees and Random Forest

18:09Preview##### 4.01 Learning Objectives

00:37##### 4.02 Decision Tree: Classifier

02:17##### 4.03 Decision Tree: Examples

01:44##### 4.04 Decision Tree: Formation

00:46##### 4.05 Choosing the Classifier

02:56##### 4.06 Overfitting of Decision Trees

01:01##### 4.07 Random Forest Classifier Bagging and Bootstrapping

02:19##### 4.08 Decision Tree and Random Forest Classifier

01:07##### 4.09 Demo: Horse Survival

04:57##### 4.10 Key Takeaways

00:25

#### Lesson 05: Unsupervised Learning

32:41Preview##### 5.01 Learning Objectives

00:36##### 5.02 Overview

01:47##### 5.03 Example and Applications of Unsupervised Learning

02:17##### 5.04 Clustering

01:46##### 5.05 Hierarchical Clustering

02:30##### 5.06 Hierarchical Clustering: Example

02:02##### 5.07 Demo: Clustering Animals

05:40##### 5.08 K-means Clustering

03:54##### 5.09 Optimal Number of Clusters

03:27##### 5.10 Demo: Cluster Based Incentivization

08:18##### 5.11 Key Takeaways

00:24

#### Lesson 06: Time Series Modelling

38:57Preview##### 6.01 Learning Objectives

00:24##### 6.02 Overview of Time Series Modeling

02:16##### 6.03 Time Series Pattern Types: Part A

02:16##### 6.04 Time Series Pattern Types: Part B

01:19##### 6.05 White Noise

01:06##### 6.06 Stationarity

02:13##### 6.07 Removal of Non Stationarity

02:13##### 6.08 Demo: Air Passengers I

14:26##### 6.09 Time Series Models: Part A

02:14##### 6.10 Time Series Models: Part B

01:28##### 6.11 Time Series Models: Part C

01:51##### 6.12 Steps in Time Series Forecasting

00:37##### 6.13 Demo: Air Passengers II

06:14##### 6.14 Key Takeaways

00:20

#### Lesson 07: Ensemble Learning

39:35Preview##### 7.01 Learning Objectives

00:24##### 7.02 Overview

02:41##### 7.03 Ensemble Learning Methods: Part A

02:49##### 7.04 Ensemble Learning Methods: Part B

04:09##### 7.05 Working of AdaBoost

01:43##### 7.06 AdaBoost Algorithm and Flowchart

02:28##### 7.07 Gradient Boosting

04:37##### 7.08 XGBoost

02:23##### 7.09 XGBoost Parameters: Part A

03:15##### 7.10 XGBoost Parameters: Part B

02:30##### 7.11 Demo: Pima Indians Diabetes

03:11##### 7.12 Model Selection

02:55##### 7.13 Common Splitting Strategies

01:45##### 7.14 Demo: Cross Validation

04:18##### 7.15 Key Takeaways

00:27

#### Lesson 08: Recommender Systems

26:11Preview##### 8.01 Learning Objectives

00:27##### 8.02 Introduction

02:16##### 8.03 Purposes of Recommender Systems

00:45##### 8.04 Paradigms of Recommender Systems

02:45##### 8.05 Collaborative Filtering: Part A

02:14##### 8.06 Collaborative Filtering: Part B

01:58##### 8.07 Association Rule: Mining

01:47##### 8.08 Association Rule: Mining Market Basket Analysis

01:42##### 8.09 Association Rule: Generation Apriori Algorithm

00:53##### 8.10 Apriori Algorithm Example: Part A

02:13##### 8.11 Apriori Algorithm Example: Part B

01:17##### 8.12 Apriori Algorithm: Rule Selection

02:52##### 8.13 Demo: User Movie Recommendation Model

04:12##### 8.14 Key Takeaways

00:50

#### Lesson 09: Level Up Sessions

10:31Preview##### Session 01

05:22##### Session 02

05:09

#### Practice Project

##### California Housing Price Prediction

##### Phishing Detector with LR

- Free Course
### Statistics Essential for Data Science

Preview#### Lesson 01: Course Introduction

07:05Preview##### 1.01 Course Introduction

05:19##### 1.02 What Will You Learn

01:46

#### Lesson 02: Introduction to Statistics

18:41Preview##### 2.01 Learning Objectives

01:16##### 2.02 What Is Statistics

01:50##### 2.03 Why Statistics

02:06##### 2.04 Difference between Population and Sample

01:21##### 2.05 Different Types of Statistics

02:42##### 2.06 Importance of Statistical Concepts in Data Science

03:20##### 2.07 Application of Statistical Concepts in Business

02:11##### 2.08 Case Studies of Statistics Usage in Business

03:09##### 2.09 Recap

00:46

#### Lesson 03: Understanding the Data

17:29Preview##### 3.01 Learning Objectives

01:12##### 3.02 Types of Data in Business Contexts

02:11##### 3.03 Data Categorization and Types of Data

03:13##### 3.03 Types of Data Collection

02:14##### 3.04 Types of Data

02:01##### 3.05 Structured vs. Unstructured Data

01:46##### 3.06 Sources of Data

02:17##### 3.07 Data Quality Issues

01:38##### 3.08 Recap

00:57

#### Lesson 04: Descriptive Statistics

32:48Preview##### 4.01 Learning Objectives

01:26##### 4.02 Mathematical and Positional Averages

03:15##### 4.03 Measures of Central Tendancy: Part A

02:17##### 4.04 Measures of Central Tendancy: Part B

02:41##### 4.05 Measures of Dispersion

01:15##### 4.06 Range Outliers Quartiles Deviation

02:30##### 4.07 Mean Absolute Deviation (MAD) Standard Deviation Variance

03:37##### 4.08 Z Score and Empirical Rule

02:14##### 4.09 Coefficient of Variation and Its Application

02:06##### 4.10 Measures of Shape

02:39##### 4.11 Summarizing Data

02:03##### 4.12 Recap

00:54##### 4.13 Case Study One: Descriptive Statistics

05:51

#### Lesson 05: Data Visualization

20:55Preview##### 5.01 Learning Objectives

00:57##### 5.02 Data Visualization

02:15##### 5.03 Basic Charts

01:52##### 5.04 Advanced Charts

02:19##### 5.05 Interpretation of the Charts

02:57##### 5.06 Selecting the Appropriate Chart

02:25##### 5.07 Charts Do's and Dont's

02:47##### 5.08 Story Telling With Charts

01:29##### 5.09 Recap

00:50##### 5.10 Case Study Two: Data Visualization

03:04

#### Lesson 06: Probability

19:49Preview##### 6.01 Learning Objectives

00:55##### 6.02 Introduction to Probability

03:10##### 6.03 Key Terms in Probability

02:25##### 6.04 Conditional Probability

02:11##### 6.05 Types of Events: Independent and Dependent

02:59##### 6.06 Addition Theorem of Probability

01:58##### 6.07 Multiplication Theorem of Probability

02:08##### 6.08 Bayes Theorem

03:10##### 6.09 Recap

00:53

#### Lesson 07: Probability Distributions

23:20Preview##### 7.01 Learning Objectives

00:52##### 7.02 Random Variable

02:21##### 7.03 Probability Distributions Discrete vs.Continuous: Part A

01:44##### 7.04 Probability Distributions Discrete vs.Continuous: Part B

01:45##### 7.05 Commonly Used Discrete Probability Distributions: Part A

03:18##### 7.06 Discrete Probability Distributions: Poisson

03:16##### 7.07 Binomial by Poisson Theorem

02:28##### 7.08 Commonly Used Continuous Probability Distribution

03:22##### 7.09 Applicaton of Normal Distribution

02:49##### 7.10 Recap

01:25

#### Lesson 08: Sampling and Sampling Techniques

30:53Preview##### 8.01 Learnning Objectives

00:51##### 8.02 Introduction to Sampling and Sampling Errors

03:05##### 8.03 Advantages and Disadvantages of Sampling

01:31##### 8.04 Probability Sampling Methods: Part A

02:32##### 8.05 Probability Sampling Methods: Part B

02:27##### 8.06 Non-Probability Sampling Methods: Part A

01:42##### 8.07 Non-Probability Sampling Methods: Part B

01:25##### 8.08 Uses of Probability Sampling and Non-Probability Sampling

02:08##### 8.09 Sampling

01:08##### 8.10 Probability Distribution

02:53##### 8.11 Theorem Five Point One

00:52##### 8.12 Center Limit Theorem

02:14##### 8.13 Recap

01:07##### 8.14 Case Study Three: Sample and Sampling Techniques

05:16##### 8.15 Spotlight

01:42

#### Lesson 09: Inferential Statistics

33:59Preview##### 9.01 Learning Objectives

01:04##### 9.02 Hypothesis and Hypothesis Testing in Businesses

03:24##### 9.03 Null and Alternate Hypothesis

01:44##### 9.04 P Value

03:22##### 9.05 Levels of Significance

01:16##### 9.06 Type One and Two Errors

01:37##### 9.07 Z Test

02:24##### 9.08 Confidence Intervals and Percentage Significance Level: Part A

02:52##### 9.09 Confidence Intervals: Part B

01:20##### 9.10 One Tail and Two Tail Tests

04:43##### 9.11 Notes to Remember for Null Hypothesis

01:02##### 9.12 Alternate Hypothesis

01:51##### 9.13 Recap

00:56##### 9.14 Case Study 4: Inferential Statistics

06:24##### Hypothesis Testing

#### Lesson 10: Application of Inferential Statistics

27:20Preview##### 10.01 Learning Objectives

00:50##### 10.02 Bivariate Analysis

02:01##### 10.03 Selecting the Appropriate Test for EDA

02:29##### 10.04 Parametric vs. Non-Parametric Tests

01:54##### 10.05 Test of Significance

01:38##### 10.06 Z Test

04:27##### 10.07 T Test

00:54##### 10.08 Parametric Tests ANOVA

03:26##### 10.09 Chi-Square Test

02:31##### 10.10 Sign Test

01:58##### 10.11 Kruskal Wallis Test

01:04##### 10.12 Mann Whitney Wilcoxon Test

01:18##### 10.13 Run Test for Randomness

01:53##### 10.14 Recap

00:57

#### Lesson 11: Relation between Variables

18:08Preview##### 11.01 Learning Objectives

01:06##### 11.02 Correlation

01:54##### 11.03 Karl Pearson's Coefficient of Correlation

02:36##### 11.04 Karl Pearsons: Use Cases

01:30##### 11.05 Spearmans Rank Correlation Coefficient

02:14##### 11.06 Causation

01:47##### 11.07 Example of Regression

02:28##### 11.08 Coefficient of Determination

01:12##### 11.09 Quantifying Quality

02:29##### 11.10 Recap

00:52

#### Lesson 12: Application of Statistics in Business

17:25Preview##### 12.01 Learning Objectives

00:53##### 12.02 How to Use Statistics In Day to Day Business

03:29##### 12.03 Example: How to Not Lie With Statistics

02:34##### 12.04 How to Not Lie With Statistics

01:49##### 12.05 Lying Through Visualizations

02:15##### 12.06 Lying About Relationships

03:31##### 12.07 Recap

01:06##### 12.08 Spotlight

01:48

#### Lesson 13: Assisted Practice

11:47Preview##### Assisted Practice: Problem Statement

02:10##### Assisted Practice: Solution

09:37

### Who provides the certificate, and how long is it valid for?

When you successfully complete our machine learning course in Calicut, you get accreditaton as well as a valuable, industry-recognized certificate that attests to your machine learning training in Calicut. This certificate is valid for all institutions and companies around the world and stays valid for your entire lifetime. Rest assured, when you complete our machine learning course in Calicut, it will prove to be beneficial for the rest of your life.### How do I become an AI and Machine Learning Engineer?

Becoming a proficient and capable AI and Machine Learning Engineer is extremely easy with our machine learning course in Calicut. The course gives you actual industry experience, which proves beneficial later on in your jobs. By successfully finishing the machine learning training in Calicut, you demonstrate and verify your acumen in AI and Machine Learning, valuable skills that many employers are looking for! Our machine learning training in Calicut provides you with all the resources and skills like classification of data, its analysis, utilization of real-time data for your system, etc., that you need to be a successful AI and Machine Learning Engineer.

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

**If you are learning the machine learning course in Calicut through an Online Classroom:****Ensure that you sit and attend an entire batch of machine learning training in Calicut.****Submit at least one completed project to our experts for skill verification.**

**If you are learning the machine learning course in Calicut through Online Self-Learning:****Finish at least 85% of the AI and Machine Learning course.****Deliver at least one finished project to our teaching experts.**

### Do you provide any practice tests as part of this AI and Machine Learning course?

If you take our valuable machine learning course in Calicut, you will gain the benefit of the practice certification test offered as a perk in the course, getting you ready for the real certification examination, and giving you the confidence and peace of mind you need to pass! If you want to get an idea of the type of tests you will be getting for your machine learning training in Calicut, you can check out our Machine Learning Free Practice Test. This practice test will give you an idea of the Multiple Choice Questions you can expect at the end of your machine learning training in Calicut.

### What is the salary of a machine learning certification training course in Calicut?

Machine learning professionals looking for a job in Calicut need to have a certificate. Since machine learning is advancing with each passing day, jobs are increasing. Machine learning will play an important role in the future. The average salary for a machine learning engineer in Calicut, India, is around 700,000 INR. To become a professional, it is necessary to take up the machine learning certification training course Calicut.

### What are the major companies hiring for Machine Learning certification training courses in Calicut?

Machine Learning engineers are in high demand in Calicut. For this reason, a significant population is considering taking up the Machine Learning certification course Calicut. Some tech giants hiring Machine Learning engineers in Calicut include Career Infosystem, Cranberry, I3 Technologies, Navidium India Private Limited, and Heidelsoft Technologies Pvt Ltd.

### What are the major industries in Calicut?

Calicut is one of the most technologically flourishing cities in India. Over the years, several companies have flourished in this region. Some of the major industries creating job opportunities for Machine Learning in Calicut include IT, Wire netting, Software applications, latex processing and more. All these industries belong to different niches.

### How to become a Machine Learning engineer in Calicut?

To become a professional Machine Learning engineer in Calicut, it is advisable to appear for the examinations. Several institutes offer training in the same field. It is essential to understand the basics of Machine Learning and become certified in the software.

### How to find a Machine Learning certification training course Calicut?

There are several institutions offering Machine Learning certification training courses in Calicut. Before choosing a particular course, it is advisable to do proper research. Depending on your requirement, you can either start with an offline or online course.

### What is Machine Learning?

Machine learning is nothing but an implementation of Artificial Intelligence that allows systems to simultaneously learn and improve from past experiences without the need of being explicitly programmed. It is a process of observing data patterns, collecting relevant information, and making effective decisions for a better future of any organization. Machine learning facilitates the analysis of huge quantities of data, usually delivering faster and accurate results to extract profitable benefits and opportunities.

Calicut is also known as Kozhikode and is one of the main coastal cities in Southern India. It is situated in Kerala and has a land area of 118 km square. Kozhikode boasts a total population of 550,440 as registered within the Municipal Corporation Limits.

It is a multi-ethnic city welcoming a population of Hindus, Muslims and Christians with a majority of Hindus. Comparatively, Calicut has had a rich history with several rulers ruling over the city. Kozhikode is one of the major economies of Kerala. It is the home to one of Kerala's oldest banks, Nedungadi bank. The IT sectors in Kozhikode create more than 100,000 job opportunities. Media, entertainment and IT sectors are the major contributors to Calicut's economy. The per capita income of Calicut is around 186,660 INR. The Government of India identifies Kozhikode as Tier 2 city.

Kozhikode is one of the most popular and historically rich places in Kerala. As of the largest IT hubs in Kerala, Kozhikode is also the home to lush green forests, rivers, hills and wildlife sanctuaries. Kozhikode is one of the best places to visit to enjoy connecting with nature. Some of the prominent things to do in Kozhikode include:

- Witness the true beauty of Kerala backwaters in Kozhikode
- Go for a swim across the Kozhikode beach
- Have fun with your children at Lion's Park
- Explore the oldest building of the town, Mishkal Mosque
- Relish the local sweets and delicacies at SM Street

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