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

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