Machine Learning with Python Training

Curriculum

Topics Covered

• Pre-Training Assessment
  • MCQ based Pre-Training assessment to evaluate understanding current on Python and Data Science
• Module 1: Introduction to Python, its Sequences and File Operations
  • Overview of Python
  • The Companies using Python
  • Different Applications where Python is Used
  • Discuss Python Scripts on UNIX/Windows
  • Values, Types, Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Writing to the Screen
  • Python Files I/O Functions
  • Numbers
  • Strings and Related Operations
  • Tuples and Related Operations
  • Lists and Related Operations
  • Dictionaries and Related Operations
  • Sets and Related Operations
• Deep Dive – Functions, Modules, Errors and Exceptions
  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values
  • Lambda Functions
  • Standard Libraries
  • Modules Used in Python
  • The Import Statements
  • Module Search Path
  • Package Installation Ways
  • Errors and Exception Handling
  • Handling Multiple Exceptions
• Python Jewels
  • The Zen of Python, Common idioms, Lambda functions List comprehensions, Generator expressions, String formatting
• Modules and Packages
  • Initialization, code, Namespaces, executing modules as scripts Documentation, Packages and name resolution Naming conventions, Using imports
• Classes
  • Defining classes, Instance methods and data Initializers, Class methods, Static methods, Inheritance, Multiple inheritance Pseudo-private variable
• OS
  • The OS module, Environment variables Launching external commands Walking directory trees, Paths, directories file names Working with file systems
• Data & Time Module
  • Dates and times operations with datetime and time module
• Regular Expression
  • Defining regular expressions, Compiling regular expressions Using regular expressions, using match objects to extract a value Extracting multiple items, Replacing multiple items
• Database Access
  • Creating and executing a cursor, CRUD operations with databases like mysql, sqlite3, oracledb , mongodb etc
• Module 2: Introduction to Data Science
  • What is Data Science, significance of Data Science in today’s digitally-driven world
  • Applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle
• Data Manipulation / Data Preprocessing using Pandas
  • Pandas
  • Data Structures & Index Operations
  • Basic Functionalities of a Data Object
  • Merging of Data Objects
  • Concatenation of Data Objects
  • Types of Joins on Data Objects
  • Exploring a Dataset
  • Analyzing a dataset
  • Reading and Writing Data from Excel/CSV Formats with Pandas
  • Missing and Replace Values in data frame
  • Data Operations
  • File Read and Write Support by pandas
  • Pandas Sql Operation
• Mathematical Computing with Python (NumPy)
  • NumPy
  • Introduction to Numpy
  • Activity-Sequence it Right
  • Creating and Printing an ndarray
  • Class and Attributes of ndarray
  • Basic Operations
  • Activity-Slice It
  • Copy and Views
  • Mathematical Functions of Numpy
• Data Visualization with Matplotlib and Seaborn
  • The Matplotlib Library
  • Introduction to Data Visualization, Line Properties
  • (x,y) Plot and Subplots, Types of Plots
  • Grids, Axes, Plots
  • Markers, Colors, Fonts and Styling
  • Types of Plots - Bar Graphs, Pie Charts, Histograms Contour Plots
• INTRODUCTION TO MACHINE LEARNING
  • What is Machine Learning?
  • Overview about Sci-Kit learn and TensorFlow
  • Types of ML
  • Some complementing fields of ML,ML algorithms
• Regression and Classification for Business Applications Using Python
  • Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. For predicting a continuous set of data, we use linear regression. However, for categorical data we use various classification algorithm logistic and tree based methods being the most important of them.
    Learning Outcome:
    After completion of this Module, participants would be able to
  • Identify and remove the outliers in the Data Set
  • Detect Multicollinearity amongst independent variables and tackle that issue
  • Able to build a linear regression model and predict
  • Able to perform diagnostic checks about model's efficiency and accuracy (i.e. calculate R-squared)
  • Able to build a classifier and logistic regression for predicting categorical data
  • Build a confusion matrix for measuring accuracy and precision of a classifier
• Model Selection
  • What is Model Selection?
  • Need of Model Selection
  • Cross – Validation
• CLUSTERING BASED LEARNING
  • Definition
  • Types of clustering
  • The k-means clustering algorithm
  • Dimensionality Reduction; Principal Component Analysis
  • PCA Example with the Iris data set
• Post-Training Assessment
  • Online MCQ based post-training assessment to gauge current understanding on Python and Data Science