Online Summer Internship Program in Machine Learning

Eduvance in association with IETE conducts a 30 day training and internship program called the "Online Summer Internship Program in Machine Learning" (SIT 2020).

This program is in collaboration with IBM EdTech Partner and powered by IBM cloud. The program shall be a Live Instructor-Led Training Program hosted by Eduvance.

The focus of the program is to give the participants a hands-on experience on one of the hottest technologies revolutionizing the industry.

Participants shall be taught how to implement Machine Learning algorithms using IBM Watson platform and Python.

Participants shall receive certificate by IBM EdTech Partner and internship letter from IETE and Eduvance.

Module 1: Basics of Python Scripting

    1.1 Introduction to Python
        1.1.1 Interpreter vs compiler
    1.2 Python Scripting methods
        1.2.1 Basic Python syntax
        1.2.2 Built-in data types - int, float, str
    1.3 Sequential data types
        1.3.1 Lists and Tuples
    1.4 Non-sequential data types
        1.4.1 Dictionaries

Module 2: Conditions and loops

    2.1 Conditional statements
        2.1.1 If, if else, if elif else conditions
    2.2 Loops
        2.2.1 For loop, while loop

Module 3: File IO and Exception Handling

    3.1 File operations
        3.1.1 Writing text to a file
        3.1.2 Reading text from a file

Module 4: Advanced scripting techniques

    4.1 One line for loop
    4.2 Lambda, reduce, map functions
    4.3 List comprehension

Module 5: Modular scripting techniques

    5.1 Functions
        5.1.1 Return, non-return type
        5.1.2 Creating user-defined functions
        5.1.3 Calling functions
    5.2 Exception handling
        5.2.1 Try-Except methods
        5.2.2 Custom exception methods

Module 6. Understanding Multidimensional arrays using Numpy

    5.1 Introduction and installation of numpy
    5.2 Array creation
    5.3 Array indexing
    5.4 Array slicing
    5.5 Boolean indexing (optional)
    5.6 Mathematical operations on matrices
    5.7 Examples and exercises on NumPy

Module 7: Data visualization using Python

    6.1 Installing matplotLIB
    6.2 Subplot nrow and ncols
    6.3 Legend labels and titles
    6.4 Setting colors,Linewidths and Linetypes
    6.5 Axis range and Axis grid
    6.6 Visualisation MATPLOTLIB - 2d scatter plot, Bar, Histogram
    6.7 Example and exercises in MatplotLib

Module 8: Data analytics using Pandas

    7.1 Creation of Dataframe with Pandas
    7.2 Indexing Dataframe with pandas
    7.3 Indexing using labels in pandas
    7.4 Pandas series objects
    7.5 Pandas Dataframe operations
    7.6 Boolean indexing with pandas
    7.7 Missing values-data refining

Module 9: Introduction to Machine Learning

    9.1 Components of Artificial Intelligence
        9.1.1 Machine Learning
        9.1.2 Deep Learning
    9.2 Classification of Machine Learning
        9.2.1 Supervised Learning
        9.2.2 Unsupervised Learning
        9.2.3 Reinforcement Learning

Module 10: Association Analysis

10.1 Market basket analysis
10.2 Apriori algorithm

Module 11: Supervised Learning - Regression

    11.1 Linear Regression
        11.1.1 OLS method
        11.1.2 SGD method
    11.2 Polynomial Regression
    11.3 Multivariate Regression

Module 12: Supervised Learning - Classification

    12.1 Perceptron
    12.2 Decision Tree
    12.3 Support Vector Machine

Module 13: Unsupervised Learning - Clustering

13.1 K-means Clustering

Module 14: IBM Cloud Fundamentals

    14.1 Setting up the IBM Cloud
    14.2 IBM Cloud Dashboard
    14.3 IBM cloud services

Module 15: IBM Watson Fundamentals

    15.1 Understanding IBM Watson machine Learning architecture
    15.2 Provisioning different services useful for ML application
    15.3 Machine Learning Implementation on IBM Watson and Cloud Platform
        15.3.1 Lab : Working with IBM Datasets
        15.3.2 Lab: Building regression based models with IBM watson services
    15.4 Implementing case study on IBM Cloud Platform
        15.4.1 Lab : Building Web interface for Machine Learning Model
        15.4.2 Lab : Interfacing with IBM machine learning API
        15.4.3 Lab : Calculating performance and accuracy