🧠 Artificial Intelligence (AI)

• Definition: Simulation of human intelligence by machines.

• Scope: Broad — includes ML, DL, robotics, NLP.

• Goal: To create systems that can think, learn, and make decisions like humans.

• Examples: Chatbots, autonomous vehicles, recommendation systems.

🤖 Machine Learning (ML)

• Definition: A subset of AI where machines learn patterns from data.

• Goal: Improve performance on tasks without being explicitly programmed.

• Types: Supervised, Unsupervised, Reinforcement Learning.

• Examples: Email spam detection, Netflix recommendations.

🧬 Deep Learning (DL)

• Definition: A subset of ML using multi-layered neural networks.

• Goal: Handle complex tasks like image and speech recognition.

• Needs: Large data sets and computing power.

• Examples: Face recognition, voice assistants (like Alexa, Siri).

📊 Data Analytics (DA)

• Definition: Analyzing data to discover useful insights and support decision-making.

• Types: Descriptive, Diagnostic, Predictive, Prescriptive.

• Tools: Excel, SQL, Python, Power BI, Tableau.

• Examples: Sales trend analysis, customer behavior reports.

💻 Data Structures & Algorithms (DSA)

• Definition: Core computer science concepts for organizing and processing data efficiently.

• Importance: Essential for coding interviews and software development.

• Key Topics: Arrays, Linked Lists, Trees, Graphs, Sorting, Searching, Recursion, DP.

• Examples: Binary search, shortest path in maps, autocomplete systems.

📈 Advanced Excel

• Definition: High-level proficiency in Excel for data analysis and automation.

• Skills:

  • Formulas (VLOOKUP, INDEX-MATCH)

  • Pivot Tables

  • Charts & Graphs

  • Macros/VBA

  • Power Query & Power Pivot

• Use Cases: Financial modeling, data dashboards, business reports.