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  • Gain skills and competencies required in Industry by Experts.
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Machine Learning Using Python


Course Objective:

Python: On completion of the training, participants will be able to successfully learn implementation of python. They Will be able to work up on software projects.

Machine Learning:  After completion of Machine Learning classes the students will be able to create and optimize algorithms.


Prerequisite:

No experience is required, however basic knowledge of C will be an added advantage.


Course Content:

PYTHON

Introduction to Python
  • Introduction to basic Python language.
  • Variables,Operators,Control flows in Python.
  • Introduction to Functions in Python.
OOPS Concepts in Python
  • Classes and Objects
  • Introduction to Module in Python.
  • Inheritence , Polymorphism, Class Methods and Attributes.
  • Special Methods in Python
Introduction to Module
  • Introduction to NumPy and Pandas
  • Creating NumPy array and its different operations
  • Creating 1D NumPy array using array() and arange()
  • Creating 2D array using reshape() function
  • Introduction to Pandas Series and Data frame
  • Read CSV file using read_csv() of pandas
  • Introduction to Matplotlib

Practical hands-on on above topic

Create two 1D array, add the two array and show the result using NumPy array
Create two 2D array, calculate the sum and multiplication of two arrays
Download a csv file from Kaggle.com and read the file as well as analysis the file using Pandas different functions

Introduction to Machine Learning
  • The main types of machine learning
  • Supervised, unsupervised learning
  • Using Python for machine learning
Dealing with Datasets
  • Training Data and Test Data
  • Split data into train and test set using sklearn

Regression and Classification using Scikit-learn

  • Simple Linear Regression using sklearn
  • Multiple Linear Regression using sklearn.
  • Classification using KNN, logistic regression

Practical Hands-on on above topic

  • Predict Boston house price from the dataset load_boston
  • Find the survived or no survived person from titanic dataset
  • Identify iris flower using iris dataset
Classification

Classification using Naïve Bayes

  • Naive bayes and its uses in machine learning

Unsupervised Learning

  • Clustering(Unsupervised learning) using K-Means

Practical Hands-on on above topics

  • Classify texts using fetch_20newsgroups() dataset
  • Example shown from make blobs dataset
Clustering Algorithm
  • KMeans Clustering
  • Hierarchal Clustering
Reinforcement Learning
  • Introduction to Reinforcement Algorithm.
  • Upper Confidence Algorithm.
  • Thompson Sampling.
Deep Learning

Introduction to Deep Learning and Neural Network

  • Why Deep learning
  • What is Neural Network and its advantages
  • Introduction to Artificial Neural Network
  • Basics of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)
  • Uses of Tensor Flow and Keras in Deep learning

Concept of calculator & it’s code development.

Practical on above topics

  • Image caption generation
  • News aggregator based on sentiment

Includes

  • 35 – 40 hrs
  • 1st Yr / 2nd Yr / 3rd Yr / 4th Yr B.Tech. / Diploma students
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