Internship Highlights
Learn from home
Beginner friendly
Certificate of Internship
Doubt clearing
Build 4 projects
8 weeks access
Why learn Machine learning?
The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. Artificial intelligence (AI) makes it possible for machines to learn from experience, adapt to new inputs
and perform human-like tasks.
Technologies you learn
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Python
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Numpy, Scipy
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Scikit-Learn, Pytorch
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Tensor Flow, Keras
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NLTK, Spacy AI, ML & Deep Learning
Structure Of Internship:
Introduction to Machine Learning - Day 1
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Introduction to Python
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Features of Python
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Modes of Python – Batch script mode, Interpreter mode
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Indentation in Python
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Coding in Python
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Python Data Types
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Objectives, Variables, Types of Variables – String, Numeric Type, Boolean Variables
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Types of Variable lists – Adding Elements to lists, Accessing Elements of the list
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Types of Variables – Dictionary
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Operators – Logical Operators, Arithmetic and numeric operators
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Order of Operands
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Operators on String
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Variables Comparison
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Control Statements
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Loops In Python
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Python in Python
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Objects and Classes in Python
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Imports and Modules
Various Techniques Under Machine Learning - Day 2
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Supervised Learning
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Unsupervised Learning
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Semi-Supervised Learning
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Reinforcement Learning
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Data Preprocessing Techniques in ML
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Refreshing Mathematical Concepts: Linear Algebra, Calculus, Probability and Statistics
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Mathematical Computing by Numpy
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Scientific Computing by Scipy
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Introduction to Various Python Machine Learning Libraries
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Various Application of Data Science and Data Visualization
Day 3
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Regression and its Types
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Linear Regressions – Algorithms and Equations
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Logistic Regression
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K – Nearest Neighbours
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Support Vector machines
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Kernel SVM
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Naïve Bayes Decision Tree Classifier
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Random Forest Classifier
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K – Means Clustering
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Clustering Algorithms
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Various Machine Learning Algorithms
Day 4 / Day 5
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Introduction to Ubuntu
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Features of Ubuntu
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Types of the flavor of Ubuntu
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Basic Linux command
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Installing OpenCV
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Installing YOLO and Darknet Framework
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Creating a dataset for Supervised learning
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Training the first model
Day 6 / Day 7 / Day 8
This day is dedicated to Teams working extensively on tools and technologies throughout the Program, applying their learning, and getting clarification of their doubts from experts.
Day 9 / Day 10
Solution/Project presentation – Peer to Peer learning Day – Learn from your other fellow participants about the projects they are working on and vice versa. • Best Teams will be selected and awarded “Winner of MLCV Summer’20” with prizes. Best Students who perform well throughout the Program will get the “Best Intern Award” and certificate of Excellence.
After the program the students should be able to:
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Understand ML/AI landscape and all emerging areas to develop products.
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Understand and build ML/AI applications.
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Familiar with various computing techniques and tools.
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Understanding of Data Mining, Data Handling tools.
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Develop or Train Algorithms for a specific purpose.
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Develop a Simple AI-based application for NLP.
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Emulate real-time ML/AI application sequence.
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Conceptualize and develop products using ML/AI.
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Develop confidence in presenting their project/Product.
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Inclination towards entrepreneurship and business opportunities.
