Python + Statistics+ Machine Learning

1. Module : Python Essentials Introduction

• What is Python...?
• A Brief history of Python
• Why Should I learn Python...?
• Installing Python
• How to execute Python program
• Write your first program
• Variables
• Numbers
• if...statement
• if...else statement
• Elif...statement

2. Variables & Data Types

• String
• Lists, Tuples & Dictionary

3. Conditional Statements & Loops

• The while...Loop
• The for....Loop
• CONTROL STATEMENTS
• Continue statement
• Break statement
• Pass statement

4. Functions

• Define function
• Calling a function
• Function arguments
• Built-in functions
• OOPs introductions

5. Modules & Packages

• Modules
• How to import a module...?
• Packages
• How to create packages

6. Files & Exception Handling

• Wring data to a file
• Reading data from a file
• Read and Write data from csv file
• Try...except
• Try...except...else
• Finally

7. Database

• Introduction
• Connections with MySql
• Executing queries (DDL, DML, DQL)
• Transactions
• Handling error

Statistics

8. Introduction to Statistics

• Why stats is required???
• Types of statistics
  1. Descriptive
  2. Inferential
• Types of data
• Ways to measure the data
• Sampling technique

9. Data Representation

• Different ways to represent data
• Normal distribution curve
• Skewness,kurtosis

10. Regression and correlation

• Different methods of regression
• With case studies
• Correlation analysis
• Types of correlation
• Measurement steps

11. Hypothesis

• Steps of hypothesis testing
• Stages of hypothesis
• Components
• Null and alternative hypothesis
• Type1 and type2 error

Data Science

12. Getting Started with Python Libraries

• What is data analysis?
• Why python for data analysis?
• Essential Python Libraries Installation and setup Ipython Jupyter Notebook
• MODULE 2:NUMPY ARRAYS
• Creating multidimensional array
• NumPy-Data types
• Array attributes
• Indexing and Slicing
• Creating array views and copies
• Manipulating array shapes
• I/O with NumPy

13. Working with Pandas

• Installing pandas
• Pandas dataframes
• Pandas Series
• Data aggregaon with Pandas DataFrames
• Concatenating and appending DataFrames
• Wring CSV files with numpy and pandas
• HTML5 format
• Reading and Wring to Excel with pandas
• JSON data
• Handling missing data

14. Data Visualization

• Installation matplotlib
• Basic matplotlib plots
• Bar graph,histogram,Scatter plots
• Saving plots to file
• Plotting functions in pandas

15. Intorduction to Machine Learning

• The origins of machine learning
• Uses and abuses of machine learning
• Machine learning successes
• The limits of machine learning
• Machine learning ethics
• How machines learn
• Data storage
• Abstraction
• Generalization
• Evaluation
• Machine learning in practice
• Types of input data
• Types of machine learning algorithms
• Matching input data to algorithms

16. Lazy Learning –Classification Using Nearest Neighbors

• Understanding nearest neighbor classification
• The k-NN algorithm
• Measuring similarity with distance
• Choosing an appropriate k
• Preparing data for use with k-NN
• Why is the k-NN algorithm lazy?
• Casestudy using iris data set

17. Probabilistic Learning –Classification Using Naive Bayes

• Understanding Naive Bayes
• Basic concepts of Bayesian methods
• Understanding probability
• Understanding joint probability
• Computing conditional probability with Bayes' theorem
• The Naive Bayes algorithm
• Classification with Naive Bayes
• The Laplace estimator
• Using numeric features with Naive Bayes
• Example –filtering mobile phone spam with the
• Naive Bayes algorithm

18. Divide and Conquer –Classification Using Decision Trees and Rules

• Understanding decision trees
• Divide and conquer
• The decision tree algorithm
• Choosing the best split
• Pruning the decision tree
• Example –identifying risky bank loans using C5.0 decision trees

19. Forecasting Numeric Data –Regression Methods

• Understanding regression
• Simple linear regression
• Ordinary least squares estimation
• Correlations
• Multiple linear regression
• Example –predicting medical expenses using linear regression

20. Black Box Methods –Neural Networks and Support Vector Machines

• Understanding neural networks
• From biological to artificial neurons
• Activation functions
• Network topology
• The number of layers
• The direction of information travel
• The number of nodes in each layer
• Training neural networks with backpropagation
• Example –Modeling the strength of concrete with ANNs

21. Finding Groups of Data –Clustering with k-means

• Understanding clustering
• Clustering as a machine learning task
• The k-means clustering algorithm
• Using distance to assign and update clusters
• Choosing the appropriate number of clusters
• Example –finding teen market segments using k-means clustering

22. Finding Patterns –Market Basket Analysis Using Association Rules

• Understanding association rules
• The Apriori algorithm for association rule learning
• Measuring rule interest –support and confidence
• Building a set of rules with the Apriori principle
• Example –identifying frequently purchased groceries with
• Association rules