Machine Learning Online Training

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.

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