Summer Industrial Training and Internship Program in Machine Learning using Python

Summer Industrial Training and Internship Program in Machine Learning using Python

Eduvance conducts a 30 day training and internship program called the “Summer Industrial Training and Internship Program in Machine Learning using Python” (SIT 2019). This training is in collaboration with IBM Edtech Partner and Powered by IBM Cloud. The focus of this training is to give the student a hands-on experience to one of the hottest technologies that is revolutionizing the computer industry. After the training, students will be given free access to CISCO Networking Academy in collaboration with CISCO Networking Academy Academic Support Center. The student will be taught on how to implement machine learning algorithms using the IBM Watson platform.

Course content

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


Course Details

Course Duration : 30 Days (20 days - Training, 10 days - Project Development)
Enquiry Contact : +919920844802 / +919137517358

Benefits



* Make your resume stronger for M.S., M.Tech. and Placements and Higher Education  


* 3 Major Level Projects and 3 Mini Projects


* Learn Machine Learning and Python using IBM Watson  


* Certificate in collaboration with CISCO Networking Academy


* Training certificate from IBM Edtech Partner  


* Internship Letter from Eduvance.


* LinkedIn IBM Badge  

FAQs



Who can apply for this course?

  • Students belonging to 2nd year, 3rd year and 4th year Electronics, Instrumentation, Electronics and Telecommunications and Biomedical engineering can apply for this course.
  • Students from Computer Engineering or Information Technology Engineering can also apply for this course.
  • Hobbyist, those who want to prototype applications using Python can join this course.

How do I register for this course?

  • You can visit our office at Ghatkopar and pay registration fee of Rs. 1000 and get yourself enrolled for this course.

Training Fees


INR 8950 (inclusive of GST)
Registration starts from 1st February 2019

Registration

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