Course Contents
Introduction
1. Introduction to Machine Learning
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Basic Terminology
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Tools
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Client Server Architecture and Various Protocol
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Relational And Non-Relational Database
Basics
2. Python Basics:
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Introduction to programming in Python
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Learn how Jupyter Notebooks work
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Learn data structures, data operations
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Learn if else statements, for and while loops, and logical operations.
Databases
3. Database
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Learn MySql
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Learn MongoDB
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Learn OrientDB
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4. Database Connection
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Learn how to connect Python Application with database with Basic CRUD Operation
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5. Database Operations
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User Authentication
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Tabular and graphical representation of data
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Various type of Data (Organised and Unorganised Text, Data Stream, Image, Audio and Video) and Information,
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Sorting and Searching in Python
Advanced
6. Python Advanced
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Learn advanced functionality in Python including functions
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Learn debugging, error handling
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Learn string manipulations and writing efficient code
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File and Folder Operation
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7. Data Manipulation and Visualization Library in Python
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Learn Data manipulation and Visualization using NumPy
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Learn Data manipulation and Visualization using SciPy
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Learn Data manipulation and Visualization using Pandas
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Learn Data manipulation and Visualization using Matplotlib
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8. Scientific Calculation Library in Python
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Learn Scientific calculation and Machine Learning using TensorFlow
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Learn Scientific calculation and Machine Learning using SciKit-Learn
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9. Natural Language Processing and Statistics Library in Python
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Learn Natural Language Processing and Statistics using NLTK,
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Learn Natural Language Processing and Statistics using Scrapy
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Learn Natural Language Processing and Statistics using Statsmodels
Data Science
10. Learn Data Science
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Introduction to Data Science
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Introduction to Basic Statistics
Machine Learning
11. Machine Learning Overview
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What is machine learning?
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Why to go for machine learning?
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Application of machine learning in several areas,
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Sample Project Assignment to be developed as part of Course and build testing models based on Machine Lear
12. Linear Algebra and Basic Statistics
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Learn Matrix and vector operations
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Learn Systems of linear equations and solutions
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Learn Matrix factorization and transformations
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Learn Eigen analysis of the data
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Learn Principal Component Analysis (PCA)
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Learn Basic probability, expected value and variance
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Learn Implementation using Python and Tensor Flow
13. Optimisation
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Learn Quadratic Energy function
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Learn Gradient Descent, Stochastic Gradient Descent
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Learn Regularization, Sparse representation
14. Regression
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Learn Linear Regression
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Learn Prediction based on Regression models
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Develop Regression model for Testing activities using learning made so far
15. Classification
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Overview (binary and multi class), Support vector machine, K-mean clustering.
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Logistic regression, Decision Trees and random forest,
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Examples and comparison between several classification methods.
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Develop Classification model for Testing activities using learning made so far
Deep Learning
16. Deep Learning Overview
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Neural network basics
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NN structure
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Activation functions
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Forward and backward propagation
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17. Deep Learning Advance
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Convolution neural network theory & examples
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Image pixel data vs image feature data
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Recurrent neural network theory, Long short-term memory with examples
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Develop Deep Learning model for Test activities using learning made so far
Other materials provided along with the Machine Learning in testing training
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PDF files, PPT files as walk through during classroom
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Life time access to training recorded videos covering Complete Module and Framework development
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Sample code as developed during classroom
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Support for overseas placements in association with Global Next Generation Automation