#### Course Objective – Machine Learning

This course has been designed by our professional Data Scientists so that we share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

#### Job Opportunities

Looking at the Machine learning market forecast, it looks promising and the upward trend will keep progressing with time. Hence, the job trend or Market is not a short lived phenomenon as machine learning is here to stay. Machine learning has the potential to improve job prospects whether you are a fresher or an experienced professional. The average salary for big data analytic professionals in the non-managerial role is 8.5 lakhs INR, whilst managers can earn an average of whopping 16 lakhs.

#### Course Duration

Total duration: 32 hours

#### Detailed course content

**Module 1 :Data Preprocessing**

- Welcome to Part 1 – Data Preprocessing
- Get the dataset
- Importing the Libraries
- Importing the Dataset
- For Python learners, summary of Object-oriented programming: classes & objects
- Missing Data
- Categorical Data
- Splitting the Dataset into the Training set and Test set
- Feature Scaling
- And here is our Data Preprocessing Template!
- Data Preprocessing

**Module 2: Regression: Simple Linear Regression, Multiple Linear Regression**

Simple linear regression

- How to get the dataset
- Dataset + Business Problem Description
- Simple Linear Regression Intuition – Step 1
- Simple Linear Regression Intuition – Step 2
- Simple Linear Regression in Python – Step 1
- Simple Linear Regression in Python – Step 2
- Simple Linear Regression in Python – Step 3
- Simple Linear Regression in Python – Step 4
- Simple Linear Regression in R – Step 1
- Simple Linear Regression in R – Step 2
- Simple Linear Regression in R – Step 3
- Simple Linear Regression in R – Step 4

Multiple linear regression

- How to get the dataset
- Dataset + Business Problem Description
- Multiple Linear Regression Intuition – Step 1
- Multiple Linear Regression Intuition – Step 2
- Multiple Linear Regression Intuition – Step 3
- Multiple Linear Regression Intuition – Step 4
- Multiple Linear Regression Intuition – Step 5
- Multiple Linear Regression in Python – Step 1
- Multiple Linear Regression in Python – Step 2
- Multiple Linear Regression in Python – Step 3
- Multiple Linear Regression in Python – Backward Elimination – Preparation
- Multiple Linear Regression in Python – Backward Elimination – HOMEWORK !
- Multiple Linear Regression in Python – Backward Elimination – Homework Solution
- Multiple Linear Regression in R – Step 1
- Multiple Linear Regression in R – Step 2
- Multiple Linear Regression in R – Step3
- Multiple Linear Regression in R – Backward Elimination – HOMEWORK
- Multiple Linear Regression in R – Backward Elimination – Homework Solution

**Module 3: Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression**

Polynomial Regression Intuition

- How to get the dataset
- Polynomial Regression in Python – Step 1
- Polynomial Regression in Python – Step 2
- Polynomial Regression in Python – Step 3
- Polynomial Regression in Python – Step 4
- Python Regression Template
- Polynomial Regression in R – Step 1
- Polynomial Regression in R – Step 2
- Polynomial Regression in R – Step 3
- Polynomial Regression in R – Step 4
- R Regression Template

SVR

- How to get the dataset
- SVR in Python
- SVR in R

Decision Tree Regression

- Decision Tree Regression Intuition
- How to get the dataset
- Decision Tree Regression in Python
- Decision Tree Regression in R
- Random Forest Regression
- Random Forest Regression Intuition
- How to get the dataset
- Random Forest Regression in Python
- Random Forest Regression in R

**Module 4: Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification**

Logistic Regression

- Logistic Regression Intuition
- How to get the dataset
- Logistic Regression in Python – Step 1
- Logistic Regression in Python – Step 2
- Logistic Regression in Python – Step 3
- Logistic Regression in Python – Step 4
- Logistic Regression in Python – Step 5

Python Classification Template

- Logistic Regression in R – Step 1
- Logistic Regression in R – Step 2
- Logistic Regression in R – Step 3
- Logistic Regression in R – Step 4
- Logistic Regression in R – Step 5
- R Classification Template

K-NN

- K-Nearest Neighbor Intuition
- How to get the dataset
- K-NN in Python
- K-NN in R
- K-Nearest Neighbor

SVM Virtual Machine

- SVM Intuition
- How to get the dataset
- SVM in Python
- SVM in R

Kernel SVM Intuition

- The Kernel Trick
- Types of Kernel Functions
- How to get the dataset
- Kernel SVM in Python
- Kernel SVM in R

Naïve Bayes

- Naive Bayes Intuition
- Naive Bayes Intuition (Challenge Reveal)
- Naive Bayes Intuition (Extras)
- How to get the dataset
- Naive Bayes in Python
- Naive Bayes in R

Decision Tree Classification

- Decision Tree Classification Intuition
- How to get the dataset
- Decision Tree Classification in Python
- Decision Tree Classification in R

Random Forest Classification

- Random Forest Classification Intuition
- How to get the dataset
- Random Forest Classification in Python
- Random Forest Classification in R

**Module 5 : Clustering: K-Means, Hierarchical Clustering**

K-Means Clustering

- K-Means Clustering Intuition
- K-Means Random Initialization Trap
- K-Means Selecting The Number Of Clusters
- How to get the dataset
- K-Means Clustering in Python
- K-Means Clustering in R
- K-Means Clustering

Hierarchial Clustering

- Hierarchical Clustering Intuition
- Hierarchical Clustering How Dendrograms Work
- Hierarchical Clustering Using Dendrograms
- How to get the dataset
- HC in Python – Step 1
- HC in Python – Step 2
- HC in Python – Step 3
- HC in Python – Step 4
- HC in Python – Step 5
- HC in R – Step 1
- HC in R – Step 2
- HC in R – Step 3
- HC in R – Step 4
- HC in R – Step 5

**Module 6 : Association Rule Learning: Apriori, Eclat**

Apriori

- Apriori Intuition
- How to get the dataset
- Apriori in R – Step 1
- Apriori in R – Step 2
- Apriori in R – Step 3
- Apriori in Python – Step 1
- Apriori in Python – Step 2
- Apriori in Python – Step 3

Eclat

- Eclat Intuition
- How to get the dataset
- Eclat in R

**Module 7 : Reinforcement Learning: Upper Confidence Bound, ThompsonSampling**

Welcome to Part 7 – Reinforcement Learning

- Upper Confidence Bound
- The Multi-Armed Bandit Problem
- Upper Confidence Bound (UCB) Intuition
- How to get the dataset
- Upper Confidence Bound in Python – Step 1
- Upper Confidence Bound in Python – Step 2
- Upper Confidence Bound in Python – Step 3
- Upper Confidence Bound in Python – Step 4
- Upper Confidence Bound in R – Step 1
- Upper Confidence Bound in R – Step 2
- Upper Confidence Bound in R – Step 3
- Upper Confidence Bound in R – Step 4

ThompsonSampling

- Thompson Sampling Intuition
- Algorithm Comparison: UCB vs Thompson Sampling
- How to get the dataset
- Thompson Sampling in Python – Step 1
- Thompson Sampling in Python – Step 2
- Thompson Sampling in R – Step 1
- Thompson Sampling in R – Step 2

#### Trainer Profile

The trainer for this course has 7+ years experience in the IT industry. He has been working on machine learning field for more than 5 years and has handson experience in dealing with it.

For training inquiries please mail to [email protected] or call +91 **8008 048 446.**