Machine Learning

12,900.00


Course Overview

Duration

100 Hours (50 Hours LIVE Teaching + 50 Hours of assignments and project work)

Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.
2. At the end of the program, a final assessment will be conducted.

Certification

1. All successful participants will be provided with a Certificate of Merit / Achievement from RCPL INDIA
2. Students who complete the course BUT do not take the final test will be provided with a certificate of participation from RCPL INDIA
3. Students who do not complete the course / leave it midway will NOT be awarded any certificate.

Location

Online Live

Tentative Date & Schedule

1st and 15th of every month

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

Prerequisite

No experience is required. But fundamental knowledge of C/C++ and statistics would be helpful.

General Information
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  • You can use only one discount for a course in a particular program it can be either of the discounts as per the discount validity.
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