Course Detail

Course Description

Machine Learning with Python

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

• Hierarchical 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)

• principal component analysis (PCA)

• Linear discriminant analysis(LDA)

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

• Adaptive Boosting

• Working of AdaBoost

• XGBoost

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