{"id":6363,"date":"2024-04-07T22:18:04","date_gmt":"2024-04-07T17:18:04","guid":{"rendered":"https:\/\/corvit.com\/systems\/?page_id=6363"},"modified":"2024-05-14T13:05:26","modified_gmt":"2024-05-14T08:05:26","slug":"ai-machine-learning-deep-learning","status":"publish","type":"page","link":"https:\/\/corvit.com\/systems\/navttc-courses-outlines\/ai-machine-learning-deep-learning\/","title":{"rendered":"AI Machine Learning Deep Learning"},"content":{"rendered":"[vc_row type=”full_width_background” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” bg_color=”#cc3036″ scene_position=”center” top_padding=”2%” bottom_padding=”2%” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][vc_custom_heading text=”AI Machine Learning Deep Learning Outline” font_container=”tag:h1|font_size:40|text_align:left|color:%23ffffff|line_height:1.2″ google_fonts=”font_family:Montserrat%3Aregular%2C700|font_style:700%20bold%20regular%3A700%3Anormal” css=”.vc_custom_1714981581705{margin-bottom: 3% !important;padding-top: 4% !important;}”][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” top_padding=”28″ bottom_padding=”28″ text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][vc_column_text]Course Contents \/ Lesson Plan<\/p>\n

Course Title: <\/strong>Artificial Intelligence (Machine Learning & Deep Learning)<\/p>\n

Duration: <\/strong>3 Months<\/p>\n

 <\/p>\n\n\n\n\n\n
Trainer Name<\/strong><\/td>\n<\/td>\n<\/tr>\n
Course Title<\/strong><\/td>\nArtificial Intelligence (Machine Learning & Deep Learning)<\/td>\n<\/tr>\n
Objective of Course<\/strong><\/td>\nEmployable skills and hands on practice for Artificial Intelligence, including specialization in Natural Language Processing (NLP) and Microsoft Azure AI Associate<\/p>\n

The aim for the team of staff responsible for delivery of the advanced IT curriculum is to provide knowledge and develop skills related to the IT. The course will allow participants to gain a comprehensive understanding of all the aspects. It will also develop the participant\u2019s ability to act in a professional and responsible manner.<\/p>\n

Teaching staff will provide the technical knowledge and abilities required to solve tasks and problems that are goal-oriented. They will use participant-centered, practically oriented methods. They will also develop a program of practical assessment that reflects the learning outcomes stated in the curriculum. Trainees of the IT curriculum will also develop their willingness and ability as individuals to clarify issues, as well as think through and assess development opportunities.<\/p>\n

 <\/p>\n

Teaching staff will also support trainees in developing characteristics such as self-reliance, reliability, responsibility, a sense of duty and a willingness and ability to criticize and accept criticism well and to adapt their future behavior accordingly.<\/p>\n

 <\/p>\n

Teaching staff also use the IT curriculum to address the development of professional competence. Trainees will acquire the ability to work in a professional environment. By the end of this course, the trainees should gain the following competencies:<\/p>\n

 <\/p>\n

Understanding of core concepts of artificial intelligence and machine learning<\/p>\n

State of the art machine learning techniques Hands-on exposure to exploratory data analysis Practical exposure to model design, evaluation<\/p>\n

Familiarity with tools and libraries such as scikit learn, pandas numpy, tensorflow, pytorch and keras<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n
Learning Outcome of the Course<\/strong><\/td>\nAfter taking this course, you will be familiar with the fundamentals of Artificial Intelligence. You will gain practical experience in applying AI for problem solving, and will develop a deep understanding of the core concepts by implementing solutions to real world problems.<\/p>\n

By the end of this course, the trainees should gain the following competencies:<\/p>\n

Understanding of core concepts of artificial intelligence and machine learning<\/p>\n

State of the art machine learning techniques Hands-on exposure to exploratory data analysis Practical exposure to model design, evaluation<\/p>\n

Familiarity with tools and libraries such as scikit learn, pandas numpy, tensorflow, pytorch and keras<\/p>\n

After the specialization in NLP, you will be comfortable using TensorFlow pipelines for NLP at the end of the course. Moreover, You will learn to build your own models which will extract information from textual data. You will learn text processing fundamentals, including text normalization, stemming and lemmatization. You will learn about different evaluation metrics for models trained for NLP tasks. You will learn to make a part of speech (POS) tagging model. You will learn about named entity recognition. You will learn advanced techniques including word embeddings, deep learning (DL) techniques. You will learn how to deploy a NLP model<\/p>\n

Moreover, you will learn not only all these skills but also learn to use Microsoft Azure API for Machine and Deep Learning for numerical, image and text data.<\/td>\n<\/tr>\n

Course Execution Plan<\/strong><\/td>\nTotal Duration of Course: 3 Months<\/td>\n<\/tr>\n
Class Hours: 4 Hours per day<\/td>\n<\/tr>\n
Theory: 20% Practical: 80%<\/td>\n<\/tr>\n
Companies Offering Jobs in the respective trade<\/strong><\/td>\n1.\u00a0\u00a0\u00a0 Careem<\/p>\n

2.\u00a0\u00a0\u00a0 Afiniti<\/p>\n

3.\u00a0\u00a0\u00a0 Addo.ai<\/p>\n

4.\u00a0\u00a0\u00a0 Arbisoft<\/p>\n

5.\u00a0\u00a0\u00a0 I2c<\/p>\n

6.\u00a0\u00a0\u00a0 Xavor<\/p>\n

7.\u00a0\u00a0\u00a0 Fiverivers Technologies<\/p>\n

8.\u00a0\u00a0\u00a0 Confiz<\/p>\n

9.\u00a0\u00a0\u00a0 Crossover<\/p>\n

10.\u00a0 NetSol<\/p>\n

11.\u00a0 Research institutes<\/p>\n

12.\u00a0 All Private Institutes who have an ML department<\/td>\n<\/tr>\n

Job Opportunities<\/strong><\/td>\nAI is the buzzword of the century, attracting attention across industries, motivating changes in products as well as services. It<\/p>\n

is the very nature of the subject that makes its applications infinite,<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
<\/td>\nin multiple domains. Whether you belong to a technical background or not, chances are that AI can make your job easier, and push it in the right direction. Dive in to develop an understanding of the core concepts, while gaining hands on experience and training from the industry\u2019s finest. Trained resources can find work as one of the following roles:<\/p>\n

 <\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 AI Associate Engineer<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Machine Learning associate analyst<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Assistant Data Analyst<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Research Assistant<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n
No of Students<\/strong><\/td>\n25<\/td>\n<\/tr>\n
Learning Place<\/strong><\/td>\nClassroom \/ Lab<\/td>\n<\/tr>\n
Instructional Resources<\/strong><\/p>\n

\/ Reference Material<\/strong><\/td>\n

Linux:<\/strong><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Learn Linux Shell Scripting \u2013 Fundamentals of Bash 4.4<\/p>\n[Sebastiaan Tammer – Packt Publishing Ltd.<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Sams Teach Yourself Shell Programming in 24 Hours<\/p>\n[Second Edition , Sams Publishing<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Applied Data Science \u2013 (Chapter 01) [Ian Langmore & Daniel Krasner<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Linux Tutorial \u2013 Basic Command Line https:\/\/www.youtube.com\/watch?v=cBokz0LTizk<\/u><\/p>\n

Python:<\/strong><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Learning Python \u2013 2nd Edition (Ch:12: OOP in Python)<\/p>\n[B. Nagesh Rao, CyberPlus Infotech Pvt. Ltd.<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Python for Everybody<\/p>\n[Dr. Charles R. Severance<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Python: A Simple Tutorial<\/p>\n[Matt Huenerfauth, University of Pennsulvania, USA<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Smarter Way to Learn Python [Mark Mayers<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 A Python Book: Beginning Python, Advanced Python, and Python Exercises<\/p>\n[Dave Kuhlman<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Mastering Object-Oriented Python<\/p>\n[Second Edition, Steven F. Lott, Pack Publishing Ltd.<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Python Official Documentation https:\/\/docs.python.org\/3\/<\/u><\/p>\n

Descriptive Statistics and Probability:<\/strong><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Probability for Machine Learning [Jason Brownlee<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining (Ch: 02) [Second Edition, Glenn J. Myatt & Wayne P. Johnson, WILEY<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Practical Statistics for Data Scientists<\/p>\n[Second Edition, Peter Bruce, Andrew Bruce, and Peter Gedeck, O\u2019REILLY<\/em>]<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
<\/td>\nExploratory Data Analysis:<\/strong><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Numpy<\/p>\n

o\u00a0\u00a0 Python for Data Analysis<\/p>\n

(Ch:04, Appendix A: Advanced Numpy) [Second Edition, Wes McKinney, O\u2019REILLY]\n

o\u00a0\u00a0 Numpy Official Documentation https:\/\/numpy.org\/doc\/1.24\/<\/u><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Pandas<\/p>\n

o\u00a0\u00a0 Pandas 1.x Cookbook<\/p>\n[Second Edition, Matt Harrison & Theodore Petrou, Pack Publishing Ltd.<\/em>]\n

o\u00a0\u00a0 Python for Data Analysis (Ch:05, 07, 10, 12)<\/p>\n[Second Edition, Wes McKinney, O\u2019REILLY]\n

o\u00a0\u00a0 Hands-on Exploratory Data Analysis with Python<\/p>\n

(Ch: 04, 06)<\/p>\n[Suresh Kumar Mukhiya & Usman Ahmed, Pack Publishing Ltd.<\/em>]\n

o\u00a0\u00a0 Pandas Official Documentation https:\/\/pandas.pydata.org\/docs\/<\/u><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Matplotlib<\/p>\n

o\u00a0\u00a0 Pandas 1.x Cookbook (Ch:13)<\/p>\n[Second Edition, Matt Harrison & Theodore Petrou, Pack Publishing Ltd.<\/em>]\n

o\u00a0\u00a0 Hands-on Exploratory Data Analysis with Python<\/p>\n

(Ch: 04, 06)<\/p>\n[Suresh Kumar Mukhiya & Usman Ahmed, Pack Publishing Ltd.<\/em>]\n

o\u00a0\u00a0 Matplotlib Official Documentation https:\/\/matplotlib.org\/stable\/index.html<\/u><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Seaborn<\/p>\n

o\u00a0\u00a0 Pandas 1.x Cookbook (Ch:13)<\/p>\n[Second Edition, Matt Harrison & Theodore Petrou, Pack Publishing Ltd.<\/em>]\n

o\u00a0\u00a0 Python for Data Analysis (Ch:09)<\/p>\n[Second Edition, Wes McKinney, O\u2019REILLY<\/em>]\n

o\u00a0\u00a0 Seaborn Official Documentation https:\/\/seaborn.pydata.org\/<\/u><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
<\/td>\nMachine Learning:<\/strong><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Machine Learning by Andrew NG (Also available freely on Youtube) https:\/\/www.coursera.org\/collections\/machine-<\/u> learning<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Machine Learning: An Algorithmic Perspective [Second Edition, Stephen Marsland, CRC Press]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Hands-On Machine Learning with Scikit-Learn,<\/p>\n

Keras, and TensorFlow<\/p>\n[Third Edition, Aur\u00e9lien G\u00e9ron, O\u2019REILLY<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 XGBoost with Python [Jason Brownlee<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Learn TensorFlow 2.0<\/p>\n[Pramod Singh & Avinash Manure, Apress<\/em>]\n

 <\/p>\n

Natural Language Processing:<\/strong><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Speech and Language Processing<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 [Third Edition, Dan Jurafsky, James H. Martin<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Deep Learning for Natural Language Processing [Jason Brownlee<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Natural Language Processing Cookbook<\/p>\n[Krishna Bhavsar, Naresh Kumar, & Pratap Dangeti, Pack Publishing Ltd.<\/em>]\n

 <\/p>\n

Deep Learning:<\/strong><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Deep Learning by Andrew NG (Also available freely on Youtube)<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 https:\/\/www.coursera.org\/learn\/neural-networks-<\/u> deep-learning<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Deep Learning with Python [Jason Brownlee<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Deep Learning for Time Series Forecasting [Jason Brownlee<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Long Short-Term Memory Networks with Python [Jason Brownlee<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 [Jason Brownlee<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Dive into Deep Learning<\/p>\n[Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola]\n

Microsoft Azure Machine Learning:<\/strong><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Mastering Azure Machine Learning: Execute Large- Scale End-to-end Machine Learning with Azure [Second Edition, Christopher Korner and Marcel Alsdorf, Packt Publishing Ltd.<\/em>]\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Microsoft Azure AI Fundamentals Training<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
<\/td>\nhttps:\/\/learn.microsoft.com\/en-<\/u> us\/training\/paths\/prepare-teach-ai-900- fundamentals-academic-programs\/<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Microsoft Azure AI Associate Training https:\/\/learn.microsoft.com\/en- us\/training\/paths\/prepare-teach-ai-102-microsoft-<\/u> design-implement-azure\/<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Microsoft Learn for Educators Program https:\/\/learn.microsoft.com\/en-us\/training\/educator- center\/programs\/msle\/<\/u><\/p>\n

Software Download:<\/strong><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Anaconda https:\/\/www.anaconda.com\/<\/u><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 VSCode https:\/\/code.visualstudio.com\/<\/u><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 PyCharm (Community Edition) https:\/\/www.jetbrains.com\/pycharm\/<\/u><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 PyTorch<\/p>\n

https:\/\/pytorch.org\/get-started\/locally\/<\/u><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 TensorFlow 2.0 https:\/\/www.tensorflow.org\/install<\/u><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Schedule d Week<\/strong><\/td>\nModule Title<\/strong><\/td>\n<\/td>\n<\/td>\nLearning Units<\/strong><\/td>\nRemarks<\/strong><\/td>\n<\/tr>\n
Week 1<\/strong><\/td>\nIntroduction<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

Linux Shell Scripting Fundamentals<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

Python Fundamentals<\/td>\n

Day 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Introduction to AI<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n

 <\/p>\n

 <\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 1<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 2<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 3-25<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

Details may be<\/u><\/em> seen at <\/u>\u00a0Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour# 2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Course Introduction<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Job market<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Course Applications<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Work ethics<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Survey of career opportunities<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Survey of industry requirements for each career path<\/p>\n

\u00b7\u200b<\/td>\n<\/tr>\n

Hour#<\/p>\n

3, 4<\/td>\n

\u00b7\u00a0\u00a0 Software Installation (Anaconda, VSCode, PyCharm, etc.)<\/td>\n<\/tr>\n
Day 2<\/td>\nHour# 1<\/td>\nIntroduction to Debian<\/p>\n

\u00b7\u00a0\u00a0 Basic Commands: pwd, cd, ls, cat, sudo, man, redirection, mkdir, rm, rmdir, cp, mv<\/td>\n<\/tr>\n

Hour#<\/p>\n

2<\/td>\n

\u00b7\u00a0\u00a0 file, reading, cat, more, less, head, alias,<\/td>\n<\/tr>\n
Hour#<\/p>\n

3<\/td>\n

\u00b7\u00a0\u00a0 shutdown, restart, touch, nano, bash, sh, chmod, ps, kill, dpkg<\/td>\n<\/tr>\n
Hour#<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0 Package update and upgrade<\/p>\n

\u00b7\u00a0\u00a0 Environment Variables<\/td>\n<\/tr>\n

Day 3<\/td>\nHour #<\/p>\n

1<\/td>\n

Values, expressions, and statements<\/p>\n

\u00b7\u00a0\u00a0 Numbers, Booleans, Strings<\/p>\n

\u00b7\u00a0\u00a0 Operators, variables and keywords<\/td>\n<\/tr>\n

Hour<\/p>\n

# 2,3<\/td>\n

\u00b7\u00a0\u00a0 String operations<\/td>\n<\/tr>\n
Hour #<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0 Input and Type casting<\/p>\n

\u00b7\u00a0\u00a0 Comments<\/td>\n<\/tr>\n

Day 4<\/td>\nHour #<\/p>\n

1 & 2<\/td>\n

Data Structures<\/p>\n

\u00b7\u00a0\u00a0 Lists<\/p>\n

\u00b7\u00a0\u00a0 Tuples<\/td>\n<\/tr>\n

Hour #<\/p>\n

3 & 4<\/td>\n

\u00b7\u00a0\u00a0 Dictionaries<\/p>\n

\u00b7\u00a0\u00a0 Sets<\/td>\n<\/tr>\n

Day 5<\/td>\nHour #<\/p>\n

1 & 2<\/td>\n

Conditional Execution<\/p>\n

\u00b7\u00a0\u00a0 If, elif, and else statements<\/p>\n

\u00b7\u00a0\u00a0 Break, continue, and pass statements<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n\u00b7\u00a0\u00a0 Nested conditionals<\/p>\n

\u00b7\u00a0\u00a0 Conditional (Ternary) Expression<\/td>\n

<\/td>\n<\/tr>\n
Hour #<\/p>\n

3 & 4<\/td>\n

\u00b7\u00a0\u00a0 While, for loops and use of enumerate<\/p>\n

\u00b7\u00a0\u00a0 Nested loops<\/p>\n

\u00b7\u00a0\u00a0 List comprehension<\/p>\n

\u00b7\u00a0\u00a0 Iterators and Iterables<\/p>\n

\u00b7\u200b<\/td>\n<\/tr>\n

Week 2<\/strong><\/td>\nPython Fundamentals<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

Implementation of OOP Principals in Python<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

Descriptive Statistics and Probability<\/td>\n

Day 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 26-<\/p>\n

27<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 49-<\/p>\n

51<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

Details may be<\/u><\/em> seen at <\/u>\u00a0Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour# 2, 3<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Functions<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Functions and variable scope<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Lambda expression<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Map and Filter<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Inner\/Nested functions<\/td>\n<\/tr>\n

Hour #<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 File Handling<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Exception Handling<\/td>\n<\/tr>\n

Day 2<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Classes and Objects<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Instance Variables and Methods<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Class Variables and Functions<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Constructors and Destructors<\/td>\n<\/tr>\n

Hour# 2,3<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Inheritance<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Multilevel Inheritance<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Hierarchical Inheritance<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Multiple Inheritance, Method Resolution Order<\/td>\n<\/tr>\n

Hour# 4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Access Specifiers: Private, Public, Protected<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Name Mangling<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Inner\/Nested Class<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Association, Aggregation, Composition<\/td>\n<\/tr>\n

Day 3<\/td>\nHour#<\/p>\n

1<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Polymorphism and Operator Overloading<\/td>\n<\/tr>\n
<\/td>\nHour# 2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Magic Functions\/Dunder Functions<\/td>\n<\/tr>\n
<\/td>\nHour# 3<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Dynamic Polymorphism (subclass as base class)<\/td>\n<\/tr>\n
<\/td>\nHour# 4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Abstract Method and Class, Empty Class, Data Class<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Keyword Arguments<\/td>\n<\/tr>\n

Day 4<\/td>\nHour# 1, 2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Data and its types (structured, Unstructured)<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Quantitative data, Numerical, Continuous, and Discrete variables<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\nOverview<\/td>\n<\/td>\n<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Qualitative data, Categorical, Nominal, Ordinal, and Binary variables<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nHour #<\/p>\n

3-4<\/td>\n

Measures of Central Tendency<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Mean, Mode, Median<\/td>\n<\/tr>\n

Day 5<\/td>\nHour# 1,2<\/td>\nMeasures of Dispersion<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Variance, Standard deviation<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Co-efficient of variation, skewness and kurtosis<\/td>\n<\/tr>\n

<\/td>\nHour# 3, 4<\/td>\nMeasures of Position<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Z-Score, Percentile, Quartile<\/td>\n<\/tr>\n

Week 3<\/strong><\/td>\nDescriptive Statistics and Probability Overview<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

Python Support Libraries for Exploratory Data Analysis<\/p>\n

–\u00a0\u00a0\u00a0\u00a0 NUMPY<\/td>\n

Day 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 28-<\/p>\n

48<\/p>\n

 <\/p>\n

 <\/p>\n

Details may be<\/u><\/em> seen at <\/u>\u00a0Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour#<\/p>\n

2<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Correlation Coefficient<\/td>\n<\/tr>\n
Hour# 3<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Univariate, bivariate and multivariate plots<\/td>\n<\/tr>\n
Hour# 4<\/td>\nProbability<\/td>\n<\/tr>\n
Day 2<\/td>\nHour# 1<\/td>\nJoint, Marginal and Conditional probability<\/td>\n<\/tr>\n
Hour# 2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Probability Distributions<\/p>\n

\u00b7\u200b<\/td>\n<\/tr>\n

Hour # 3-4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Discrete and Continuous probability distributions<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Bayesian Probability<\/td>\n<\/tr>\n

Day 3<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Introduction to Numpy<\/td>\n<\/tr>\n
Hour# 2,3,4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Creating Numpy Arrays (from Python list, from built-in methods, from random)<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Array Attributes and Methods (reshape, max,<\/p>\n

min, argmax, argmin, shape, dtype, size, ndim)<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Operations on Arrays (copying, append and<\/p>\n

Insert, Sorting, Removing\/Deleting, Combining\/Concatenating, Splitting)<\/td>\n<\/tr>\n

Day 4<\/td>\nHour # 1-2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Data Loading & Saving<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NumPy Indexing and Selection (Indexing a 2D array, Logical Selection)<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Broadcasting<\/td>\n<\/tr>\n

Hour # 3-4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Type Casting<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Arthmetic Operations (Add, Subtract, Multiply, Divide, Exponentiation)<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Universal Array Functions (sqrt, exp, max,<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n <\/p>\n

–\u00a0\u00a0\u00a0\u00a0 Pandas<\/td>\n

<\/td>\n<\/td>\nsin, etc)<\/td>\n<\/td>\n<\/tr>\n
Day 5<\/td>\nHour#<\/p>\n

1<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Introduction to Pandas<\/td>\n<\/tr>\n
Hour# 2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Series and DataFrame and Data Input<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Selection and Indexing (rows, columns, conditional selection, selection of subset of rows and columns, index setting, etc)<\/td>\n<\/tr>\n

Hour# 3<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Operations on DataFrames (head, unique, value counts, applying custom functions, getting column and index names, sorting and<\/p>\n

ordering, null value check, value replacement, dropping rows and columns, etc)<\/td>\n<\/tr>\n

Hour#<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Missing data & its handling<\/td>\n<\/tr>\n
Week 4<\/strong><\/td>\nPython Support Libraries for Exploratory Data Analysis<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Pandas<\/p>\n

–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Seaborn<\/td>\n

Day 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 28-48<\/p>\n

Details may be<\/em><\/td>\n<\/tr>\n

Hour# 2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Merging, Joining, and Concatenation (inner,<\/p>\n

outer, right and left joins)<\/td>\n<\/tr>\n

Hour # 3-4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 GroupBy<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Discretization and Binning<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Operations on DataFrames<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Data output\/saving<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Pandas for Plotting (area, bar, density, hist,<\/p>\n

line, scatter, barh, box, hexbin, kde, and pie plots<\/td>\n<\/tr>\n

Day 2<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Introduction to Seaborn<\/td>\nseen at <\/u><\/em><\/p>\n

Annexure-<\/em>I<\/em><\/td>\n

<\/td>\n<\/tr>\n
Hour# 2<\/td>\nDistribution Plots<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 distplot<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 jointplot (pairplot, rugplot, kdeplot)<\/td>\n

<\/td>\n<\/tr>\n
Hour# 3<\/td>\nCategorical Data Plots<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 factorplot, boxplot, violinplot, stripplot, swarmplot, barplot, countplot<\/td>\n<\/tr>\n

Hour# 4<\/td>\nMatrix Plots<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Heatmap<\/td>\n<\/tr>\n

Day 3<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Machine learning introduction and types<\/td>\n<\/tr>\n
Hour# 2,3,4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Classical machine learning pipeline (data collection, preprocessing, feature crafting,<\/p>\n

modeling, testing and evaluation, and deployment)<\/td>\n<\/tr>\n

Day 4<\/td>\nHour # 1,2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Supervised machine learning<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Regression and classification problems<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Components of supervised machine learning (labeled data, hypothesis, cost function,<\/p>\n

optimizer)<\/td>\n<\/tr>\n

Hour<\/p>\n

# 3,4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Univariate Linear Regression with Gradient Descent<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\nDay 5<\/td>\nHour # 1-2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Univariate Linear Regression with Gradient Descent<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Without Vectorization<\/td>\n

<\/td>\n<\/tr>\n
Hour # 3-4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 With Vectorization<\/td>\n<\/tr>\n
Week 5<\/strong><\/td>\nMachine Learning-I<\/td>\nDay 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task \u2013 51,52<\/p>\n

Details may be<\/u><\/em> seen at <\/u>\u00a0Annexure-I<\/em><\/td>\n<\/tr>\n

Hour#<\/p>\n

2,3,4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Multivariate Linear Regression<\/td>\n<\/tr>\n
Day 2<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Polynomial Regression<\/td>\n<\/tr>\n
Day 3<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Logistic Regression (Binary Classification)<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
Day 4<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Logistic Regression (Multiclass Classification)<\/td>\n<\/tr>\n
Day 5<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Code practice<\/td>\n<\/tr>\n
Week 6<\/strong><\/td>\nNatural Language Processing<\/td>\nDay 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n <\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 53-<\/p>\n

55<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

Details may be<\/em><\/td>\n<\/tr>\n

Hour#<\/p>\n

2<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Introduction to Natural Language Processing<\/td>\n<\/tr>\n
Hour# 3<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Syntax, Semantics, Pragmatics, and Discourse<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NLP curves and future directions<\/td>\n<\/tr>\n

Hour# 4<\/td>\nData pre-processing for NLP<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Introduction to NLTK\/SpaCy<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Noise removal (stopwords, punctuation, etc)<\/td>\n<\/tr>\n

Day 2<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Word and sentence tokenization<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Word segmentation<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Stemming<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Text normalization<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Regular expression for string parsing<\/td>\n

seen at <\/u><\/em><\/p>\n

Annexure-<\/u><\/em>I<\/u><\/em><\/td>\n<\/tr>\n

Hour # 2-3<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 POS tagging<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NER tagging<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Chunking and Chinking<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Lemmatization<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 WordNet<\/td>\n<\/tr>\n

Hour# 4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Words as features (BoW model)<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Feature Selection and Extraction<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Document Similarity<\/td>\n<\/tr>\n

Machine Learning II<\/td>\nDay 3<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Testing<\/td>\n<\/tr>\n
Hour# 2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Evaluation Metrics<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Classification and Regression<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour # 3-4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Dataset imbalance and its remedies (Augmentation)<\/td>\n<\/td>\n<\/tr>\n
Day 4<\/td>\nHour# 1,2,3<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Support Vector Machine (SVM)<\/td>\n<\/tr>\n
Hour#<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Decision Tree<\/td>\n<\/tr>\n
Day 5<\/td>\nHour# 1,2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Decision Tree<\/td>\n<\/tr>\n
Hour<\/p>\n

# 3-4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Bagging \u2013 Random Forest<\/td>\n<\/tr>\n
 <\/p>\n

Build Your CV \u2013 Mid-term Exam<\/strong><\/td>\n<\/tr>\n

Week 7<\/strong><\/td>\n <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

Deep Learning I<\/td>\n

Day 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n <\/p>\n

 <\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 56-64<\/p>\n

 <\/p>\n

 <\/p>\n

Details may be<\/u><\/em> seen at <\/u>\u00a0Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour# 2,3,4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Boosting<\/td>\n<\/tr>\n
Day 2<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

MLP Feed Forward Neural Network<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Forward and backward passes<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Nonlinearity: Activation functions<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Cross-Entropy<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Computational graph and Backpropagation<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Vanishing and exploding gradients<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Overfitting, underfitting, dropout regularization<\/td>\n<\/tr>\n

Day 3<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Introduction and implementation of neural networks using appropriate deep learning API<\/p>\n

of choice (TensorFlow, PyTorch, Keras)<\/td>\n<\/tr>\n

Day 4<\/td>\nHour # 1-2<\/td>\nConvolutional Neural Network (CNN)<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2D CNN for image classification<\/td>\n<\/tr>\n

Hour # 3-4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1D CNN for text document classification<\/td>\n<\/tr>\n
Day 5<\/td>\nHour<\/p>\n

# 1-2<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Code Practice Neural Networks<\/td>\n<\/tr>\n
Hour # 3-4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Code Practice Neural Networks<\/td>\n<\/tr>\n
Week 8<\/strong><\/td>\nDeep Learning II<\/td>\nDay 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n<\/td>\n<\/tr>\n
Hour# 2,3,4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Recurrent Neural Networks (RNNs)<\/td>\n<\/tr>\n
Day 2<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Long-Short-Term-Memory Networks (LSTM)<\/td>\n<\/tr>\n
Day 3<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 LSTM Code Practice<\/td>\n<\/tr>\n
Day 4<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Gated Recurrent Unit Networks<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\nDay 5<\/td>\nHour #1,2,3<\/p>\n

,4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 GRU Code Practice<\/td>\n<\/td>\n<\/tr>\n
Week 9<\/strong><\/td>\nDeep Learning II<\/td>\nDay 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n<\/td>\n<\/tr>\n
Hour # 2,3,4<\/td>\nWord Embeddings<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Word2vec<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Continuous BOW<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Continuous Skip-gram<\/td>\n<\/tr>\n

Day 2<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Gensim and Custom Embedding Training<\/td>\n<\/tr>\n
Day 3<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Sequence Models<\/td>\n<\/tr>\n
Day 4<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

Sequence Models<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1 to 1<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1 to Many<\/td>\n<\/tr>\n

Day 5<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

Sequence Models<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Many to 1<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Many to Many<\/td>\n<\/tr>\n

Week 10<\/strong><\/td>\nDeep Learning II<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

Employable Project<\/p>\n

\/ Assignment<\/p>\n

(2 weeks, 11-12) in addition of regular classes.<\/p>\n

OR<\/p>\n

On job training (2 weeks)<\/td>\n

Day 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 65<\/p>\n

Details may be<\/u><\/em> seen at <\/u>\u00a0Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour# 2,3,4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Bi-Directional LSTM\/RNN in Sequence Models<\/td>\n<\/tr>\n
Day 2,3<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Attention Mechanism in Models<\/td>\n<\/tr>\n
Day 4,5<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

Selection of Project, architecture discussion, preparation.<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Guidelines to the Trainees for selection of employable project like final year project (FYP).<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Assignment of Independent project to each Trainee.<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 A project based on trainee’s aptitude and acquired skills.<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Designed by keeping in view the emerging trends in the local market as well as across the globe.<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 The project idea may be based on entrepreneurship.<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Leading to the successful employment.<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 The duration of the project will be 2 weeks<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Ideas may be generated via different sites such as:<\/p>\n

https:\/\/1000projects.org\/<\/p>\n

https:\/\/nevonprojects.com\/ https:\/\/www.freestudentprojects.com\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\nhttps:\/\/technofizi.net\/best-computer- science- and-engineering-cse-project- topics-ideas-for- students\/<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Final viva\/assessment will be conducted on project assignments.<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 At the end of session, the project will be presented in skills competition.<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 The skill competition will be conducted on zonal, regional and National level.<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 The project will be presented in front of Industrialists for commercialization<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 The best business idea will be placed in NAVTTC business incubation center for commercialization.<\/p>\n

OR<\/strong><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 On job training for 2 weeks:<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Aims to provide 2 weeks industrial training to the Trainees as part of overall training program<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Ideal for the manufacturing trades<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 As an alternate to the projects that involve expensive equipment<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Focuses on increasing Trainee’s motivation, productivity, efficiency and quick learning<\/p>\n

approach.<\/td>\n

<\/td>\n<\/tr>\n
Week 11<\/strong><\/td>\nMS Azure AI Service<\/td>\nDay 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n <\/p>\n

 <\/p>\n

 <\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 65<\/p>\n

Details may be<\/em><\/td>\n<\/tr>\n

Hour # 2,3<\/td>\nSelection of Microsoft Azure AI Service<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Selection the appropriate service for a vision solution<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Selection the appropriate service for a language analysis solution<\/td>\n<\/tr>\n

Hour#<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Selection the appropriate service for a decision support solution<\/td>\nseen at <\/u><\/em>\u00a0Annexure-I<\/em><\/td>\n<\/td>\n<\/tr>\n
Day 2<\/td>\nHour<\/p>\n

# 1,2<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Selection the appropriate service in Cognitive<\/p>\n

Services for a speech solution<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Selection the appropriate Applied AI services<\/td>\n

<\/td>\n<\/tr>\n
Hour # 3-4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Configuring Security for Microsoft Azure AI Service<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Manage account keys<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Manage authentication for a resource<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Secure services by using Azure Virtual Networks<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Plan for a solution that meets Responsible AI principles<\/td>\n<\/tr>\n

Day 3<\/td>\nHour # 1-2<\/td>\nCreate & Manage Microsoft Azure AI Service<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Create an Azure AI resource<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Configure diagnostic logging<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour # 3-4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Manage costs for Azure AI services<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Monitor an Azure AI resource<\/td>\n

<\/td>\n<\/tr>\n
Day 4<\/td>\nHour # 1-2<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Deploy Microsoft Azure AI Services<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Determine a default endpoint for a service<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Create a resource by using the Azure portal<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Integrate Azure AI services into a continuous integration\/continuous deployment (CI\/CD) pipeline<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Plan a container deployment<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Implement prebuilt containers in a connected environment<\/td>\n<\/tr>\n

Hour # 3-4<\/td>\nMicrosoft Azure Creation of Solutions for Anomaly Detection Content Improvement<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Create a solution that uses Anomaly Detector, part of Cognitive Services<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Create a solution that uses Azure Content Moderator<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Create a solution that uses Personalizer, part of Cognitive Services<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Create a solution that uses Azure Metrics Advisor, part of Azure Applied AI Services<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Create a solution that uses Azure Immersive Reader, part of Azure Applied AI Services<\/td>\n<\/tr>\n

Day 5<\/td>\nHour # 1-2<\/td>\nMicrosoft Azure Implementation of Image and Video Processing Solutions<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Analyze images<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Extract text from images<\/td>\n<\/tr>\n

Hour # 3-4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Implement image classification and object<\/p>\n

detection by using the Custom Vision service, part of Azure Cognitive Services<\/td>\n<\/tr>\n

Week 12<\/strong><\/td>\n<\/td>\nDay 1<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Motivational Lecture (For further detail please see Page No: 3& 4)<\/td>\n <\/p>\n

 <\/p>\n

 <\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Task 65<\/p>\n

Details may be<\/u><\/em> seen at <\/u>\u00a0Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour# 2,3,4<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Process videos<\/td>\n<\/tr>\n
Day 2<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

Microsoft Azure Natural Language Processing (NLP) Solutions Implementation<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Analyze text<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Process speech<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Translate language<\/td>\n<\/tr>\n

Day 3<\/td>\nHour# 1,2,3,<\/p>\n

4<\/td>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Build and manage a language understanding model<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Create a question answering solution<\/td>\n<\/tr>\n

Day 4<\/td>\nHour# 1<\/td>\n\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Build and manage a language understanding model<\/td>\n<\/tr>\n
Hour # 2-4<\/td>\nMicrosoft Azure Knowledge Mining Solutions Implementation<\/td>\n<\/tr>\n
Day 5<\/td>\nHour # 1-4<\/td>\nMicrosoft Azure Conversational AI Solutions Implementation<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=”full_width_content” full_screen_row_position=”middle” column_margin=”default” equal_height=”yes” content_placement=”top” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” top_padding=”4%” bottom_padding=”4%” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” class=”contact-form-with-img” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_color=”#406f5b” shape_divider_position=”bottom” shape_divider_height=”100″ shape_divider_bring_to_front=”true” bg_image_animation=”none” shape_type=”waves”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” zindex=”-999999999″ gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”5\/12″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][image_with_animation image_url=”2806″ animation=”Fade In” hover_animation=”none” alignment=”” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default” el_class=”img-side-contact”][\/vc_column][vc_column column_padding=”padding-6-percent” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”left-right” top_margin_phone=”4%” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”7\/12″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][vc_custom_heading text=”Quick Inquiry” font_container=”tag:h2|font_size:36|text_align:left|color:%23000000|line_height:1.2″ google_fonts=”font_family:Montserrat%3Aregular%2C700|font_style:700%20bold%20regular%3A700%3Anormal” css=”.vc_custom_1677876449507{margin-bottom: 3% !important;padding-top: 4% !important;}” el_id=”contact”][vc_column_text]\n
\n

<\/p>

    <\/ul><\/div>\n
    \n
    \n\n\n\n\n\n\n\n<\/div>\n
    \n\t

    <\/i><\/span><\/i><\/span>\n\t<\/p>\n<\/div>\n

    \n\t

    <\/i><\/span><\/i>