AIN GenX LMS

Advanced Data Analytics & AI

Excel Intermediate - Advanced, Power Query & Power Pivot, Power BI (DAX & M), Tableau, Microsoft Fabric (Data Automation), SQL, MySQL, PostgreSQL, Python for Data Analysis, Machine Learning & AI, Upwork, Prompts-to-Production with Gemini, ChatGPT, DeepSeek, Grok & LangChain

Who this course is for

Working Professionals, Entrepreneurs & Business Owners, Beginners & Students, Anyone Interested in Data

Duration

4 Months
(100 Hours)

Classes

Online via Zoom

Schedule

Day: Sat & Sun Only
Timing: 06:00 - 09:00 pm (PST)

Starting From

Sunday, 09 November, 2025

Overview

Advanced Data Analytics & AI turns scattered data into confident decisions. In this program, you’ll master Excel (Intermediate–Advanced) with Power Query and Power Pivot, then model and visualize in Power BI using DAX and M. You’ll explore Tableau for rapid insights and Microsoft Fabric for streamlined data automation. Build rock-solid foundations with SQL across MySQL and PostgreSQL. Move into Python for Data Analysis, and apply Machine Learning and AI to real business problems. We’ll also cover Upwork essentials to monetize your skills. Finally, go prompts-to-production using Gemini, ChatGPT, DeepSeek, Grok, and LangChain—shipping reliable, cost-aware AI workflows end to end today.

Excel for Data Analysis
• Excel shortcuts make life easy
• Data Validation
• Data Analysis techniques
• Filter, Multi Filter, Sort, Multi Sort and Custom Sort
• Conditional Formatting
• Working with Basic Charts
• Working with Multiple Worksheets and workbooks (Links)
• Applying Security to Files, Workbooks & Worksheets

Essential Formulas for Data Analysis
• SUM, AVERAGE, COUNT, MIN, MAX for basic calculations
• AVERAGEIFS, COUNTIFS, SUMIFS for multi-condition analysis
• SUBTOTAL for filter data analysis

Logical and Lookup Functions
• Logical Functions (IF, AND, OR)
• VLOOKUP, HLOOKUP, and XLOOKUP for searching data
• INDEX and MATCH for advanced lookup scenarios

Advanced Data Analysis Techniques
• Consolidating Techniques
• Working with the Name Manager
• Advanced Charting and Graphs
• Data Analysis with What-If Analysis Tools

Data Analysis with Pivot Tables
• Creating Pivot Tables from raw data
• Summarizing data using Pivot Tables
• Grouping data by categories, dates, and values
• Using slicers to filter data interactively
• Creating dynamic Pivot Charts from Pivot Table data
• Formatting and customizing charts for presentation

Visualizing Data with Charts
• Bar, line, pie, and column charts for visualizing trends
• Adding and formatting data labels, axes, and legends
• Combination charts (e.g., bar and line charts)

Dashboards and Reporting in Excel
• Designing a dashboard layout for clear data representation
• Design Principles for Effective Dashboards
• Optimizing dashboards for sharing and collaboration
• Exporting dashboards and reports as PDFs or Excel workbooks
• Protecting data and restricting access to sensitive information
• Conditional Formatting for Data Highlighting
• Creating Sparklines for In-Line Data Trends

Data Analysis with Power Query
• Importing data from multiple sources
• Cleaning and transforming data using Power Query
• Combining data from multiple tables and sources
• Creating automated data transformations

Power Pivot Fundamentals
• Power Pivot vs normal PivotTable
• Data Model, Tables, Relationships, Star Schema concept
• Power Pivot Window tour (Data View, Diagram View)

DAX Basics
• Calculated Column vs Measure (implicit vs explicit)
• Row context vs Filter context (simple examples)
• Common functions: SUM, DISTINCTCOUNT, RELATED, RELATEDTABLE
• CALCULATE() ka role (filter modify)
• Total Sales, Total Qty, Avg Price, Unique Customers

Time Intelligence
• YTD, MTD, PY (SAMEPERIODLASTYEAR, DATEADD)
• Running totals, Moving average

Introduction to Power BI Desktop
• Overview of Power BI components: Desktop, Service, and Mobile
• Key features and benefits of Power BI Desktop
• Understanding the Power BI interface and workflow

Data Transformation with Power Query
• Importing data from Excel, databases, CSV files, and cloud services
• Handling multiple data sources
• Removing duplicates and filtering data
• Pivoting and unpivoting data
• Splitting and merging columns
• Conditional columns
• Replacing and transforming values
• Grouping and aggregating data
• Merging and appending queries

Data Modeling in Power BI
• Creating a Data Model
• Defining relationships between tables
• Star schema and snowflake schema in Power BI
• Difference between calculated columns and measures
• Creating calculated columns for custom data
• Hiding and sorting columns
• Managing relationships and model performance

DAX (Data Analysis Expressions) Functions
• Introduction to DAX
• Syntax and structure of DAX
• Understanding row context and filter context
• Aggregation functions: SUM, MIN, MAX, COUNT, AVERAGE
• Logical functions: IF, SWITCH
• Text functions: CONCATENATE, LEFT, RIGHT
• Time intelligence functions: DATEADD, SAMEPERIODLASTYEAR, YTD, QTD, MTD
• Advanced measures for financial analysis (e.g., YTD growth, % changes)
• Handling complex scenarios with CALCULATE and FILTER

Data Visualization and Reporting
• Best Practices in Data Visualization
• Choosing the right visual for your data
• Overview of visualization types: bar charts, line charts, pie charts, maps, etc.
• Building dynamic and interactive visuals
• Customizing Visuals
• Formatting and styling visuals for clarity and impact
• Conditional formatting for highlighting key insights
• Using slicers and filters to add interactivity
• Creating drill-through and drill-down capabilities for deeper analysis
• Adding tooltips and custom visuals for enhanced reporting
• Simplifying and decluttering reports for better readability

Report Sharing and Collaboration
• Publishing reports to the cloud (Power BI Service)
• Sharing reports via email, Teams, and embedding in websites
• Setting up automatic data refreshes
• Scheduling reports and dashboards to update regularly

Introduction to SQL Server
• Overview of SQL Server
• What is SQL Server?
• Installation and Setup
• SQL Server Management Studio (SSMS) Interface

Basics of SQL
• Introduction to SQL
• SQL Syntax and Structure
• Data Types
• Basic SQL Commands
• SELECT, FROM, WHERE
• INSERT, UPDATE, DELETE
• Filtering and Sorting Data
• WHERE Clause
• ORDER BY Clause

Advanced SQL Queries
• Aggregate Functions
• COUNT, SUM, AVG, MIN, MAX
• Grouping Data
• GROUP BY Clause
• HAVING Clause
• Joining Tables
• INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN

Data Manipulation and Transformation
• Subqueries and Nested Queries
• Common Table Expressions (CTEs)
• Window Functions
• ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE()
• Data Transformation Techniques

Working with Complex Data Types
• Working with Dates and Times
• Date Functions
• String Functions
• CONCAT, SUBSTRING, CHARINDEX, REPLACE

SQL Server Advanced Topics
• Indexing and Performance Tuning
• Creating and Managing Indexes
• Query Optimization
• Stored Procedures and Functions
• Creating and Executing Stored Procedures
• User-Defined Functions

Data Analysis and Reporting
• Basic Data Analysis Techniques
• Descriptive Statistics
• Using SQL for Data Analysis
• Exploratory Data Analysis (EDA)
• Generating Reports
• Creating Simple Reports in SSMS
• Exporting Data to Excel

Module 1: Foundations of AI, Python & Data Handling
• Understanding Python: Building Strong Foundations for AI & Data Science
• Introduction to Artificial Intelligence: Concepts, Origins, and Evolution
• The Rise of AI: Modern Applications and Its Impact on Society
• Ethics in AI: Responsible Use, Transparency, and Governance
• Addressing Ethical Dilemmas: Bias, Fairness, and Accountability
• Regulatory, Legal, and Social Considerations of AI Deployment
• Preparing Data for AI: Cleaning, Transformation, and Standardization
• Exploratory Data Analysis (EDA): Uncovering Patterns and Insights

Module 2: Core Principles of Machine Learning
• Understanding Machine Learning: Key Concepts and Definitions
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• The Machine Learning Lifecycle: From Data Ingestion to Model Deployment
• Programming with Python: Essential Libraries for ML (NumPy, Pandas, Scikit-learn)
• Predictive Modeling: Introduction to Linear and Logistic Regression
• Evaluating Model Performance: Accuracy, Precision, Recall, and Beyond
• Practical Exercise: Build and Evaluate a Simple Regression Model

Module 3: Unsupervised Learning & Clustering Techniques
• Fundamentals of Unsupervised Learning: When Labels Aren’t Available
• Clustering Algorithms: K-Means, Hierarchical Clustering, and DBSCAN
• Reducing Complexity: Dimensionality Reduction with PCA
• Detecting Anomalies: Outlier Detection Methods in ML
• Hands-on Lab: Apply K-Means Clustering to Real-World Datasets

Module 4: Neural Networks and Deep Learning Essentials
• Neural Networks Demystified: Architecture, Neurons, and Activation Functions
• Learning Through Layers: Backpropagation and Optimization
• Deep Learning in Practice: Convolutional Neural Networks (CNNs)
• Vision Systems: Image Recognition and Classification
• Leveraging Pretrained Models: Introduction to Transfer Learning
• Hands-on Lab: Design and Train a CNN for Image Classification

Module 5: Natural Language Processing (NLP)
• Introduction to NLP: Bridging Human Language and Machines
• Text Preparation: Tokenization, Normalization, and Noise Removal
• Feature Representation: Bag-of-Words and TF-IDF Techniques
• Understanding Sentiment and Classifying Text Data
• Word Embedding Models: Word2Vec, GloVe, and Vector Representations
• Advanced NLP: Sequence-to-Sequence Models and Named Entity Recognition (NER)
• Hands-on Lab: Build a Sentiment Analysis Application

Introduction to Tableau
• Overview of Tableau and its uses in data analysis.
• Understanding the Tableau interface.
• Connecting to data sources.

Data Preparation
• Data sourcing and importing.

Visualization Basics
• Creating basic visualizations (bar charts, line charts, pie charts, etc.).
• Applying filters and sorting data.
• Formatting visualizations for clarity.

Advanced Visualization Techniques
• Creating interactive dashboards.
• Using parameters and calculated fields.
• Implementing advanced chart types (treemaps, heatmaps, etc.).

Data Analysis
• Exploring trends and patterns in data.
• Conducting ad-hoc analysis with Tableau.
• Utilizing forecasting and trend analysis tools.

Mapping Data
• Geospatial analysis with maps.
. • Analyzing location-based data.

Sharing and Collaboration
• Publishing workbooks to Tableau Public.
• Sharing insights and visualizations with stakeholders.
• Collaborating on projects within Tableau.

Getting Started with n8n
● What n8n is and why automation matters in business workflows
● Exploring the n8n dashboard & UI walkthrough
● Practice Task: Build your first simple workflow (Manual Trigger → Send Slack/Email message)

Understanding Triggers
● Types of triggers: Manual, Scheduled, App-based
● Use cases of triggers in daily work
● Practice Task: Create a daily reminder workflow with Scheduled Trigger + Slack/Email

Actions & Connecting Your Apps
● Actions in workflows and data flow basics
● Connecting popular apps (Google Sheets, Slack, Gmail)
● Practice Task: Automated notification workflow (Form submission → Google Sheets → Slack alert)

Working Smarter with Templates
● Explore the n8n Templates Library
● Import and customize pre-built workflows
● Practice Task: Modify and run “Gmail → Google Sheets” template

Workflow Management + Logic
● Organizing workflows (folders, logs, naming conventions)
● Adding conditional logic with the If Node
● Practice Task: Prioritize important emails (Filter: High Priority → Slack/Email alert)

Real-World HR & Finance Use Cases
● HR Use Case: Automated interview reminders (Google Calendar → Gmail/Slack)
● Finance Use Case: Daily financial summary (Google Sheets → Slack/Email)
● Practice Task: Build an HR interview reminder workflow

Hands-On Deep Dive Practice
● Participants work on extending workflows built earlier
● Trainer provides guided exercises for each department (HR, Finance, Marketing, Support)
● Practice Task: Pick one use case (Marketing Email Capture, Support Ticket Routing, etc.)

Debugging & Error Handling Basics
● How to test workflows step by step
● Logs, error messages, retry strategies
● Practice Task: Debug a broken Gmail → Google Sheets workflow

Best Practices for Workflow Design
● Credential management & security basics
● Versioning & workflow collaboration tips
● Practice Task: Create a shared folder with 2 workflows inside for team use

Final Workshop & Presentation
● Capstone project: Design an end-to-end workflow (choose HR, Finance, Marketing, or Support case)
● Practice Task: Build and demo your workflow
● Participants present workflows to group, explain the business impact

Getting Started with n8n
● What n8n is and why automation matters in business workflows
● Exploring the n8n dashboard & UI walkthrough
● Practice Task: Build your first simple workflow (Manual Trigger → Send Slack/Email message)

Understanding Triggers
● Types of triggers: Manual, Scheduled, App-based
● Use cases of triggers in daily work
● Practice Task: Create a daily reminder workflow with Scheduled Trigger + Slack/Email

Actions & Connecting Your Apps
● Actions in workflows and data flow basics
● Connecting popular apps (Google Sheets, Slack, Gmail)
● Practice Task: Automated notification workflow (Form submission → Google Sheets → Slack alert)

Working Smarter with Templates
● Explore the n8n Templates Library
● Import and customize pre-built workflows
● Practice Task: Modify and run “Gmail → Google Sheets” template

Workflow Management + Logic
● Organizing workflows (folders, logs, naming conventions)
● Adding conditional logic with the If Node
● Practice Task: Prioritize important emails (Filter: High Priority → Slack/Email alert)

Real-World HR & Finance Use Cases
● HR Use Case: Automated interview reminders (Google Calendar → Gmail/Slack)
● Finance Use Case: Daily financial summary (Google Sheets → Slack/Email)
● Practice Task: Build an HR interview reminder workflow

Hands-On Deep Dive Practice
● Participants work on extending workflows built earlier
● Trainer provides guided exercises for each department (HR, Finance, Marketing, Support)
● Practice Task: Pick one use case (Marketing Email Capture, Support Ticket Routing, etc.)

Debugging & Error Handling Basics
● How to test workflows step by step
● Logs, error messages, retry strategies
● Practice Task: Debug a broken Gmail → Google Sheets workflow

Best Practices for Workflow Design
● Credential management & security basics
● Versioning & workflow collaboration tips
● Practice Task: Create a shared folder with 2 workflows inside for team use

Final Workshop & Presentation
● Capstone project: Design an end-to-end workflow (choose HR, Finance, Marketing, or Support case)
● Practice Task: Build and demo your workflow
● Participants present workflows to group, explain the business impact

Introduction to Upwork
• What is Upwork?
• Benefits of being an Upwork freelancer
• Overview of the Upwork platform

Setting Up Your Upwork Profile
• Creating an effective profile
• Crafting a compelling title and overview
• Highlighting your skills and expertise
• Building a portfolio and showcasing your work

Finding and Applying for Jobs
• Search strategies and filters
• Understanding job descriptions and requirements
• Crafting winning proposals
• Following up on proposals and interviews

Upwork Fees and Billing
• Understanding Upwork’s fee structure
• Setting your rates and pricing strategies
• Invoicing and getting paid

Communication and Client Management
• Effective communication with clients
• Setting expectations and deliverables
• Managing revisions and feedback
• Building long-term client relationships

Upwork’s Policies and Guidelines
• Upwork’s terms of service
• Maintaining a good job success score
• Handling disputes and resolving issues

Growing Your Upwork Business
• Building a strong reputation and profile
• Earning and maintaining high ratings
• Leveraging Upwork’s features and tools
• Expanding your service offerings

Bonus Tips and Best Practices
• Time management and productivity tips
• Networking and collaboration opportunities
• Continuing education and skill development
• Q&A and open discussion

Introduction to Prompt Engineering
• What is prompt engineering and why it matters
• Real-world examples in business, marketing, HR, operations
• Capabilities and limitations of LLMs (like Gemini, ChatGPT, DeepSeek, Grok & LangChain)

Understanding the Basics
• Types of prompts: System vs. User
• Structure of a good prompt
• Few-shot, zero-shot, and chain-of-thought prompting
• RACE Framework of Prompt
• Role prompting: setting context for better results

Prompting Techniques for Common Tasks
• Summarizing emails, reports, and meetings
• Drafting professional content: emails, SOPs, LinkedIn posts
• Generating ideas, outlines, and presentations
• Rewriting and simplifying complex content

Improving Prompt Effectiveness
• Tone, format, and length control
• Using tables, bullet points, and sections
• Getting better output through step-by-step instructions
• Iterating and refining prompts

Use Cases by Department
• Marketing: ad copy, social posts, blog ideas
• HR: job descriptions, interview questions, policy summaries
• Sales: pitch drafting, email follow-ups, objection handling
• Finance: summarizing reports, variance commentary

Using Prompt Libraries and Templates
• How to reuse and adapt effective prompts
• Storing prompt templates in Google Docs / Notion
• Community libraries and best prompt repositories

GenAI Tools with Built-in Prompt Interfaces
• Overview: Gemini, ChatGPT, DeepSeek, Grok & LangChain
• When to use what: strengths and special features
• Voice and image prompts (multimodal prompts)

Ethics, Accuracy & AI Validation
• Avoiding hallucinations and bias
• Checking facts and ensuring data privacy
• When not to trust AI: guardrails for professionals

Mini Projects and Capstone
• Use a prompt to automate a weekly task
• Create a personal prompt library
• Present a real-world use case with your optimized prompts

AI is at the forefront of technological innovation, and AIN GenX equips you to lead in this transformative field. Our "Become an AI Engineer" training blends cutting-edge tools, industry-relevant techniques, and hands-on projects to provide a comprehensive learning experience. Here’s why learners choose us:

Industry-Relevant Curriculum We cover key AI concepts, including machine learning, deep learning, natural language processing, and generative AI, using popular tools like Python, TensorFlow.

Hands-On Projects Gain real-world experience by building intelligent solutions like recommendation systems, predictive models, and conversational agents.

Expert-Led Sessions Learn from AI practitioners with years of industry experience, ensuring you grasp practical applications alongside theoretical knowledge.

Career-Oriented Focus Our training prepares you for high-demand roles with a focus on employable skills, project portfolios, and interview readiness.

Supportive Learning Environment With live sessions, Q&A support, and mentorship, AIN GenX ensures a personalized journey for every learner.

Transform your future with AIN GenX and become a leader in AI innovation!

Artificial Intelligence is shaping the future, and AIN GenX welcomes learners from diverse backgrounds to join our "Become an AI Engineer" training program. Here’s what makes you eligible to embark on this transformative journey:

Educational Background Preferred: A bachelor’s degree in IT, computer science, mathematics, engineering, or a related field is advantageous as it provides a foundation for AI concepts.
Non-Specific: Individuals from non-technical fields with a strong passion for AI and problem-solving are also encouraged.

Basic Programming Knowledge Familiarity with any programming language (such as Python, Java, or C++) is helpful. Beginners with a willingness to learn programming will also find our training approachable, as foundational coding is covered.

Analytical and Mathematical Aptitude Basic understanding of algebra, statistics, and logical reasoning is essential for grasping machine learning and AI concepts. Problem-solving skills and a natural curiosity for understanding systems and data patterns are key assets.

Interest in Technology and AI A passion for exploring AI-driven solutions and enthusiasm for working with data, automation, and innovation. A growth mindset and readiness to learn tools like TensorFlow, Keras, and OpenAI frameworks.

Computer Literacy and Technical Comfort Familiarity with basic computer operations and tools. Prior exposure to data analysis or tools like Excel, SQL, or Python is a plus but not mandatory, as we start from the basics.

AIN GenX’s program is designed to cater to both beginners and professionals, ensuring everyone has the support needed to succeed in the world of AI engineering!

High Demand and Career Opportunities AI is driving innovation across industries, creating an ever-growing demand for skilled AI engineers. Professionals trained in AI are sought after in tech, healthcare, finance, automotive, and beyond, offering diverse career opportunities.

Competitive Salary and Perks AI engineers are among the highest-paid professionals in the tech industry. With specialized skills in machine learning, deep learning, and AI frameworks, you can command impressive salaries and rapid career advancement.

Impactful and Futuristic Work As an AI engineer, you contribute to groundbreaking advancements such as autonomous vehicles, intelligent healthcare solutions, and personalized technology. Your work directly shapes the future, solving global challenges and creating innovative solutions.

Expertise in Cutting-Edge Tools and Techniques Learning AI equips you with mastery of tools like Python, TensorFlow, PyTorch, and OpenAI platforms. These technical skills enable you to build intelligent systems, optimize processes, and create predictive models.

Versatile and Transferable Skills AI skills are highly transferable across domains, empowering you to transition between industries or roles seamlessly. Whether in research, development, or business applications, your expertise remains relevant and adaptable.

Enhanced Problem-Solving and Analytical Skills AI training sharpens your ability to tackle complex problems through logical and data-driven approaches. You'll learn to break down challenges, analyze data, and design innovative AI-powered solutions.

Hands-On Learning with Real-World Projects Training programs, like those at AIN GenX, include real-world projects to apply AI concepts practically. This hands-on experience ensures you are job-ready, confident, and capable of delivering impactful results.

Continuous Growth in a Dynamic Field AI is a fast-evolving field, providing endless opportunities for learning and growth. Training ensures you stay at the forefront of emerging technologies and maintain your competitive edge.

Opportunities for Leadership and Specialization AI engineers often advance into strategic roles, such as AI architects, data scientists, or CTOs. With experience, you can specialize in areas like robotics, natural language processing, or AI ethics, becoming a leader in the industry.

Contribution to a Data-Driven World By learning AI, you become a key player in the shift toward intelligent systems, driving decisions based on data and algorithms. Your expertise helps shape a smarter, more efficient world.

AI engineering is a high-impact field that offers immense growth potential and diverse career opportunities across industries. Here's an overview of its scope:

Booming Career Opportunities AI is one of the fastest-growing sectors, with applications in industries such as healthcare, finance, e-commerce, manufacturing, and technology. Organizations are investing heavily in AI, driving demand for skilled AI engineers worldwide.

Versatile Industry Applications AI engineers work on transformative projects, including autonomous vehicles, virtual assistants, recommendation systems, fraud detection, predictive analytics, and more. The skills acquired in AI training are relevant to almost every industry.

High Earning Potential AI engineers are among the highest-paid professionals due to the specialized and critical nature of their skills. Salaries are competitive, with growth opportunities as you gain experience and expertise in AI technologies.

Advanced Problem-Solving Capabilities AI training equips you to solve complex real-world problems through data-driven methods, machine learning algorithms, and intelligent systems. This makes you a valuable asset in driving innovation and efficiency.

Gateway to Emerging Technologies Learning AI opens pathways to cutting-edge areas like robotics, computer vision, natural language processing, and generative AI. These fields are advancing rapidly, ensuring your skills remain in demand.

Global Relevance and Remote Opportunities AI is a global field, allowing professionals to work with companies or clients across the world. Remote job opportunities are plentiful, enabling flexible and dynamic career options.

Entrepreneurial Potential AI training empowers you to develop your own intelligent solutions and start AI-driven businesses. Many AI engineers transition into entrepreneurship by creating innovative products or consulting services.

Strategic Impact on Organizations AI engineers play a critical role in shaping business strategies by automating processes, optimizing workflows, and enabling data-driven decision-making. This strategic importance enhances career stability and growth.

Research and Development For those interested in academia or innovation, AI training provides the foundation for contributing to cutting-edge research in artificial intelligence, shaping its future development.

Lifelong Learning in a Dynamic Field AI is constantly evolving, offering opportunities to grow and specialize throughout your career. With new advancements emerging, AI engineers can explore areas like ethical AI, sustainable AI, or AI for social good.

AI Engineers design, develop, and deploy artificial intelligence systems and applications. They work on projects like machine learning models, natural language processing (NLP), computer vision, and recommendation systems, solving complex problems through data-driven algorithms.

A technical background is helpful but not mandatory. Familiarity with programming, mathematics, and basic statistics can give you a head start. Many training programs, like those at AIN GenX, provide foundational knowledge to help beginners transition into AI engineering.

Python is the most popular language for AI due to its simplicity and vast ecosystem of libraries (TensorFlow, PyTorch, scikit-learn, etc.). Other languages like R, Java, and C++ are also used in specific AI applications.

AI Engineers can work as:
Machine Learning Engineers
Data Scientists
AI/ML Researchers
Computer Vision Engineers
NLP Specialists
AI skills are in high demand across industries like tech, healthcare, finance, and retail.

Some key tools include:
Frameworks: TensorFlow, PyTorch
Libraries: NumPy, Pandas, scikit-learn
Visualization Tools: Matplotlib, Seaborn
Cloud Platforms: AWS, Azure, Google Cloud
Data Management: SQL, Hadoop

AI Engineers are needed in diverse industries like:
Technology (Google, Microsoft)
Healthcare (diagnostics, drug discovery)
Finance (fraud detection, algorithmic trading)
Retail (personalization, inventory optimization)
Automotive (self-driving cars, predictive maintenance)

Successful participants will get the AIN GenX certificates.

View Project

Irfan Bakaly

Data Analyst
25+ years of experience in Data Analysis

Noor Surani

Tech Entrepreneur 25+ years of experience in Data Analysis

Tamkeen Ahmed

Data Analyst
Supply Chain Profession 5+ years of Teaching Experience

Sohail Ahmed

Data Scientist
5+ years Experience of Data Science

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