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Machine Learning using Python

12,500.00

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

SYLLABUS

SESSION 1

Topics to be covered:                                                                  

  • Introductory Remark about Python
  • A Brief History of Python
  • How Python is different from other languages
  • Python Version
  • Installing Python
  • IDLE
  • Getting Help
  • How To execute Python program
  • Writing your first program

SESSION 2

Topics to be covered:      

  • Python coding Introduction
  • Python keywords and Identifiers
  • Python statements
  • Comments in python
  • Getting user input
  • Variables
  • Data types
  • Numbers
  • Strings
  • Lists ,tuples & dictionary

SESSION 3

Topics to be covered:      

  • Control flow and syntax
  • The if statement
  • Python operators
  • The while Loop
  • Break and continue
  • The for Loop
  • Pass statement
  • Packages

SESSION 4

Topics to be covered:      

  • Scientific Computing with NumPy.
  • N-Dimensional Array Object
  • Array Slicing Methods
  • Array reshapeing methods
  • Numerical routines in NumPy

SESSION 5

Topics to be covered:      

  • Introduction To Matplotlib
  • Python 2D plotting
  • Plotting with default settings
  • Customizing matplotlib Graphics with colors and line width
  • Generate plots, histograms, power spectra,
  • Generate bar charts, scatterplots
  • Introduction To Pandas
  • Pandas data structures and data analysis

 

SESSION 6

Topics to be covered:      

  • Introduction To Statistics
  • Implementation of Statistics in Python
  • Python Math package to mathematical functions
  • Concepts of Calculus and Linear Algebra
  • Vector and Matrices in Python
  • Operation on Matrices using Python

SESSION 7

Topics to be covered:      

  • Introduction To Machine Learning
  • History and Evolution
  • Artificial Intelligence Evolution
  • Find out where Machine Learning is applied in Technology and Science.
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Reinforcement Learning

SESSION 8

Topics to be covered:      

  • Regression
  • Understand how continuous supervised learning is different from discrete learning.
  • Code a Linear Regression in Python with scikit-learn.
  • Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions.
  • k-Nearest Neighbor
  • Linear models
  • Naive Bayes Classifiers
  • Decision trees
  • Support Vector Machines

SESSION 9

Topics to be covered:

  • Prepare Your Data For Machine Learning
  • Need For Data Pre-processing
  • Data Transforms
  • Rescale Data
  • Standardize Data
  • Normalize Data
  • Binarize Data

SESSION 10

Topics to be covered:

  • Supervised Machine Learning Algorithms
  • Prepare Your Data For Machine Learning
  • k-Nearest Neighbor
  • Linear models
  • Naive Bayes Classifiers
  • Decision trees
  • Support Vector Machines
  • Unsupervised Learning and Preprocessing
  • Challenges in unsupervised learning
  • Preprocessing and Scaling
  • Applying data transformations
  • Scaling training and test data the same way

Course Overview

DURATION

50 Hours

LOCATION

Online Live

TENTATIVE DATE & SCHEDULE

1st and 15th of every month

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