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MHN edtech offers a comprehensive program in Computer Science and Artificial Intelligence (AI). Here's an overview of what you might expect from such a program:

Program Overview
The Computer Science and AI program at MHN edtech is designed to equip students with the foundational knowledge and practical skills needed to excel in the rapidly evolving field of artificial intelligence. The curriculum is structured to cover key areas of computer science while emphasizing AI techniques, tools, and applications.

Key Components
1. Core Computer Science Subjects:
Programming Languages: Python, Java, C++
Data Structures and Algorithms: Essential for problem-solving and coding interviews.
Databases: SQL, NoSQL, database design, and management.
Software Engineering: Principles of software development, version control (Git), and project management.
2. Mathematics for AI:
Linear Algebra: Vectors, matrices, and their applications in machine learning.
Calculus: Differentiation and integration for understanding optimization algorithms.
Probability and Statistics: Essential for data analysis, Bayesian networks, and statistical models.
3. Artificial Intelligence and Machine Learning:
Machine Learning Fundamentals: Supervised and unsupervised learning, model evaluation.
Deep Learning: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs).
Natural Language Processing (NLP): Text processing, sentiment analysis, language models.
Computer Vision: Image processing, object detection, facial recognition.
Reinforcement Learning: Markov decision processes, Q-learning, policy gradients.
4. Tools and Frameworks:
Programming Libraries: NumPy, Pandas, Scikit-Learn for data manipulation and analysis.
Deep Learning Frameworks: TensorFlow, Keras, PyTorch.
Big Data Technologies: Hadoop, Spark for large-scale data processing.
Version Control Systems: Git and GitHub for collaboration and version tracking.
5. Practical Applications and Projects:
Capstone Projects: Real-world AI projects to build a portfolio.
Hackathons and Competitions: Opportunities to apply skills in competitive environments.
Internships and Industry Partnerships: Practical experience through internships and collaborations with tech companies.
6. Soft Skills and Career Development:
Communication Skills: Effective technical communication, presentations.
Career Coaching: Resume building, interview preparation, job search strategies.
Ethics in AI: Understanding the ethical implications and responsibilities of AI technologies.
Learning Approach
The program employs a blend of theoretical learning, hands-on practice, and collaborative projects. It may include:

Lectures and Workshops: In-depth sessions on various topics.
Lab Sessions: Practical exercises and coding practice.
Online Modules: Flexible learning through video tutorials and interactive content.
Mentorship: Guidance from experienced professionals in the field.
Admission Requirements
Typical prerequisites for the program might include:

A background in mathematics and programming.
An undergraduate degree in computer science or a related field (for advanced programs).
Passion for AI and a willingness to engage in rigorous study.
Outcome
Graduates of the program will be well-equipped to pursue careers as:

AI Engineers
Machine Learning Engineers
Data Scientists
Software Developers
Research Scientists
They will possess a strong foundation in both theoretical concepts and practical applications, making them valuable assets in various tech industries.

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Categories
Computer Programming
Web Programming
Computer Science
Computer Network
Computer Database
Computer Architecture
Computer Security
Operating Systems
Office Applications
Computer Graphics
Mathematics
*Other IT Topics

Python, JavaScript, C++, Java, C#, Ruby,
and PHP Popular courses
Python Tutorial
Adobe Photoshop Tutorial
Basic Computer course book
Beginning Excel 2019
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An Introduction to Blender 3D
Excel Fundamentals
Excel for advanced users
Mobile Phone Repair and Maintenance
Latest Added courses
TCP/IP Tutorial and Technical Overview
Network Infrastructure Security Guide
Digital Marketing Step-By-Step
Open Source Intelligence Tools and Resources Handbook
The Ultimate Guide to Drupal 8
Cyber Security for Beginners
Wireless Networks
Capture One 22 User Guide
Procreate: Actions & Animation
Procreate: Editing Tools
DATA ANALYTICS
COURSE
CURRICULUM
Business statistics
Excel: Basics to
Advanced
My SQL
Tableau
Power BI
SAS
R Basics
Python Basics

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MODULE 1
BUSINESS STATISTICS
Descriptive Statistics
Data Types
Measure Of central tendency
Measures of Dispersion
Graphical Techniques
Skewness & Kurtosis
Box Plot
Probability and Normal Distribution
Random Variable
Probability
Probability Distribution
Normal Distribution
SND
Inferential Statistics
Sampling Funnel Central Limit Theorem
Confidence interval
Introduction to Hypothesis Testing
Anova and Chisquare
Data cleaning and Insights
Data Cleaning
Imputation Techniques
Scatter Diagram and Correlation
Analysis

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MODULE 2
EXCEL: BASICS TO ADVANCED
Intorduction to Excel :
Quantum of Excel and Basics
Workbook
Types of workbooks and their uses
Common uses of Excel
Cell
Row
Column
Range/Array
Name box
Formatting of cells
Ribbon
Formula bar Status bar
Basic operators
Intorduction to Functions :
Commonly used Excel Functions
What is syntax
Arguments
Navigations using keyboard
Shortcuts
Sum
Average
Maximum- Minimum
Product
CountBlank
CountA
CountIF
If,Now,Today
Cut,Copy,Paste,Paste Special
Anchoring data :
Referencing , Named ranges and its uses
Absolute
Relative
Mixed referencing
Name Manager
Named ranges
Creating Tables
Create functions using named ranges AND/OR
referencing
Referring data from different tables:
Various types of Lookup, Nested IF
Lookup
Vlookup
Nested Vlookup
Hlookup
Index
Index with Match function
If with combination of AND/OR
IFERROR
Referring data from different tables:
Advanced functions
RANK
RAND
RANDBETWEEN
INDIRECT with ADDRESS & MATCH
OFFSET
Data Handling : Data cleaning,
Data type identification, Data restrictions
LEN
LEFT
RIGHT
MID
CONCATENATE
CONCAT
FIND
SUBSTITUTE
TEXT
TRIM
SECOND
MINUTE
HOUR
DAY
WEEK
MONTH
QUARTER
YEAR
WORKDAYINTL
ISNUMBER
ISNA
ISNONTEXT
ISEVEN
ISODD
ISFORMULA
ISERROR
Data validation
Depended drop down
Protecting cell
Array
range
sheet
Workbook
Data Handling : Formatting and Filtering
Conditional formatting
Sort
Advanced Sort
Filtering
Data Summerization :
Advanced functions, Charts
Sum
Average
Max-Min with IF and IF'S
CountIF'S
Various types of Charts
Data Summerization :
Pivots, Preparing the Dashboard
Pivot table
Slicers
Pivot charts
Calculated field
Calculated item
ADD/REMOVE/CHANGE data into the pivot table
Refreshing pivot data
Dashboard creation
Power query, Power pivot
Cleaning data
Extracting data from multiple sources
Transforming data
Imputation techniques
Getting data from CSV files
Databases
Workbooks
Webpages
Power query, Power pivot, Use case
discussion:
Data Preparation, Project
Summarization
Consolidating data from multiple
sources
Merging data from different
workbooks/worksheets
Relationships
Use Data handling steps taught in the
previous session
Use Data summarization techniques
Populate output in Excel
Combining multiple functions
Intro to Automation:Macros(Recorded
/VBA)
How VBA works
Record a sample macro
VBA
If constructs, Select construct,
User defined functions
Input box, message box
Procedures
Automatic macros
Methods to cleanup the codes

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If you have any specific questions or need more detailed information about the MHN Coaching Computer Science and AI program, feel free to ask!

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