Section A: Introduction of Machine Learning

• Introduction to machine learning
• Understanding the need
• Understanding Big data and machine learning
• Running machine learning under linux platform
• Introduction to Redhat Enterprise linux
• Why linux is important for machine learning with respect to future
• Role of Python and R programming in this domain
• Basic Introduction of Python syntax and programming logics
• Deep dive with Supervised , Unsupervised and Reinforcement learning
• Algo discussion with use case
• Popular machine learning framework like tensorflow , scikit-learn

Section B: Python Programming

• Basic of python and why python for machine learning
• Installation of software on different OS.
• Understanding basic syntax with data types
• Number, String, List, Tuple, Dictionary
• Extracting data from a file
• Committing your code to GIT

Section C: More about Python Programming

• Conditional statement and loops
• Function and modules
• File handling
• Creating own modules / library
• Web scraping with urllib2
• Grabbing system information from Popen and os library
• Scanning Network IP & MAC address with loops
• Introduction to Ipython with jupyter notebook
• Using jupyter notebook with Ipython & Python
• UDP Socket programming
• Exception & Signal handling
• Making chat program with UDP socket
• Extending chat programming
• Introduction to pandas
• Making data frames with pandas
• Handling xls & csv files with pandas
• Loading and extracting existing xls files

Section D: Libraries Used

• Introduction to Numpy & Matplotlib
• Managing arrary with numpy
• Multidimensional array with numpy
• Unit matrix handling & creating
• Deleting indexes from matrix
• Deep dive with Matplotlib
• Drawing general purpose graphs
• Graphs with mathematics

Section E: Machine learning Techniques

• Advice of applying machine learning
• Machine learning System Design
• Decision Tree algo deep dive
• Training your machine with real time datasets
• Deep dive with UCI
• Lab session for loading data from different apis
• Detecting data from numpy and converting for training and testing data
• Exercise with ML and others framework
• Introduction to iris datasets
• Understanding iris datasets
• Modifying and loading with scikit-learn
• Separating data with numpy
• Training classifier
• Algo data process view

Section F: Supervised learning

• Regression
• Classification
• Case study learning in regression
• Case study learning in classification
• Comparing the result of Decision Tree and Navie Bayes algo
• Graphploting with Matplotlib for comparison

Section G: Deep Learning for image search and Recognition

• Searching for image
• Loading image with cloud library
• Registering image for training model
• Browsing image from url and local
• Training image datasets
• Recognition of different images to detect face
• Deregistering images from cloud library
• Pushing code to github for automatic updates

Section H: Live Image Processing and ML

• How image search is going to work
• Taking pictures with python for image processing
• Loading and registering images
• Face detection with android sdk
• Machine learning with Amazon cloud
• Image processing with amazon cloud
• Introduction to Reinforcement learning
• An example implementation of reinforcement learning

Section I: Neural Networks Analysis

• Understanding neural networks
• Data learning and machine predictions
• Neural networks real understanding
• Neural network implementation with real datasets

Projects:

• Face and expression recognition based Smart Music Player and Communication System using ML Algo’s
• Smart Machine Learning System

Coming Soon...