Machine Learning & Deep Learning

Machine Learning & Deep Learning

Chapter 1

Introduction

  • What is machine learning?

  • What is Deep learning?

  • What is data science

  • Supervised learning

  • Unsupervised learning

  • Difference between DS and ML and AI and DL

  • A sample programming Example

Chapter -2

Basics of python

  • Installing python

  • Different IDES

  • Variables

  • Data types

  • Loop

  • function

  • module and package

  • object oriented programming

  • python packages numpy,sklearn,matplotlib,pandas

  • Working with ANACONDA

 

Chapter -3

Numpy

  • Introduction to Numpy

  • Creating numpy array

  • Attributes of numpy array

  • Advantage of Numpy array over List

  • Mathematical operation on numpy array

  • Different ways to create numpy array

  • Reshaping numpy array

  • Concatenation and splitting operation

  • Trigonometric functions

  • Random sample generation


 


 

Chapter -4

Data analysis with Pandas

  • Pandas series

  • Pandas data frame

  • Reading CSV files

  • Parameters of read_csv()

  • Read excel files

  • Handling missing values

  • categorical data

  • Data cleaning and pre processing

 

Chapter -5

Data Visualization

Matplotlib

Seaborn


 

Chapter-6

Regression

  • Linear Regression

  • Multiple linear regression

  • Polynomial Regression

  • Logistic regression

 

Chapter-7

Logistic Regression

  • Introduction

  • Logistic function or sigmoid function

  • Types of logistic regression

  • Implementation

 

Chapter-8

K-Nearest Neighbors Algorithm

  • How KNN works

  • KNN classifier

  • Confusion Matrix

  • KNN Regressor

  • How to choose k value


 

Chapter -9

Naïve Bayes Algorithm

  • Bayes Theorem

  • Types of naïve bayes classifier

  • Bernoulli naïve bayes

  • Gaussian Naïve

  • Multinomial NB

  • Text Processing

 

Chapter -10

Decision Tree Algorithm

  • Why to use decision trees?

  • Decision Tree Terminologies

  • How a decision tree works

  • Advantages and disadvantages


 

Chapter -11

Random Forest Algorithm

  • What is random forest

  • How random forest works

  • Ensemble learning

  • Bagging and boosting

  • Advantages and disadvantages

 

Chapter -12

Support vector machine

  • What is support vector machine?

  • Types of SVM

  • Hyper plane and support vectors

  • How support vector works?

 

Chapter-13

Unsupervised Learning

  • What is unsupervised learning

  • Types of unsupervised learning

  • Applications of unsupervised learning

  • K-means clustering

 

Chapter 14

Feature engineering and Dimensional Reduction

  • feature extraction

  • feature selection

  • dummy variable and one hot encoding

  • Label encoding and ordinal encoding

  • Feature scaling

  • Hyper parameter tuning

  • dimension reduction (feature reduction)


 

Chapter-15

Model selection

  • What is Model Selection?

  • The need for Model Selection

  • Cross-Validation

  • What is Boosting?

  • How Boosting Algorithms work?

  • Types of Boosting Algorithms

Chapter -16

Time series prediction

  • What is Time Series Analysis?

  • Importance of TSA

  • Components of TSA

  • White Noise

  • AR model

  • MA model

  • ARMA model

  • ARIMA model


Chapter-17

Project work

Trainer

Ranjan Das

Mrp Price

₹ 35000

Discount In Percentage

29% off

Discounted Fee

₹ 25000

Duration

60 Hr

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