Pythonsoft

Data Science with Machine Learning , Deep Learning & AI

Chapter 1

Introduction

• What is data science

• What is machine learning?

• What is Deep learning?

• Difference between DS and ML and AI and DL

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

Neural Network (Deep learning)

• What is Neural Network

• What is neuron and how it works

• How neural network works

• Perceptron and multi-layer neural network

• Types of neural networks

• Implementing ANN using keras

• CNN(convolution Neural Network )

 

Chapter -18

NLP (natural Language processing)

• Why Natural Language Processing (NLP)

• Applications of Text Mining

• Reading, Writing to text and word files

• Setting the NLTK Environment

• Accessing the NLTK Corpora

• Tokenization

• Frequency Distribution

• Different Types of Tokenizers

• Bigrams, Trigrams & Ngrams

• Stemming

• Lemmatization

• Stopwords

Chapter-19

Project work

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