Safaricom 2025 Internship Program Open For Application

Disciplines :
- Technology
- Cyber Security
- HR
- Legal
- Finance
- Supply Chain
- Marketing
- Audit
- PR & Communications
- Enterprise Risk
Program Overview
(i) 3-Month Industrial Attachment: Ongoing students. Intake -April, July, October & January.
(ii) 6-Month Internship: Recent graduates. intake - April & October.
(iii) Pupillage: Kenya School of Law Students. Intake in April & October.
Eligibility Criteria :
- Ongoing university students or recent graduates.
- Available for full-time internship during specified intake periods
Deadline : 13th February, 2025

Apply:
https://kenyatrends.co.ke/safaricom-internship-program/

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https://microsoft.github.io/AI-For-Beginners

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- Back-End Engineer (.Net)
- Graphic Design Specialist
- Content and Social Media Manager
- Data Analyst
- Community Manager/Content Creator
- Business Development Manager
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14 Free & Essential Generative AI Cources for 2025

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C++ Programming Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to C++
| | |-- Setting Up Development Environment (IDE: Code::Blocks, Visual Studio, etc.)
| | |-- Compiling and Running C++ Programs
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators (Arithmetic, Relational, Logical, Bitwise)
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Switch Case
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Do-While Loop
| |
| |-- Jump Statements
| | |-- Break, Continue
| | |-- Goto Statement
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Syntax
| | |-- Parameters and Arguments (Pass by Value, Pass by Reference)
| | |-- Return Statement
| |
| |-- Function Overloading
| | |-- Overloading Functions with Different Parameters
| |
| |-- Scope and Lifetime
| | |-- Local and Global Scope
| | |-- Static Variables
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Classes and Objects
| | |-- Member Functions and Data Members
| |
| |-- Constructors and Destructors
| | |-- Constructor Types (Default, Parameterized, Copy)
| | |-- Destructor Basics
| |
| |-- Inheritance
| | |-- Single and Multiple Inheritance
| | |-- Protected Access Specifier
| | |-- Virtual Base Class
| |
| |-- Polymorphism
| | |-- Function Overriding
| | |-- Virtual Functions and Pure Virtual Functions
| | |-- Abstract Classes
| |
| |-- Encapsulation and Abstraction
| | |-- Access Specifiers (Public, Private, Protected)
| | |-- Getters and Setters
| |
| |-- Operator Overloading
| | |-- Overloading Operators (Arithmetic, Relational, etc.)
| | |-- Friend Functions
|
|-- Advanced C++
| |-- Pointers and Dynamic Memory
| | |-- Pointer Basics
| | |-- Dynamic Memory Allocation (new, delete)
| | |-- Pointer Arithmetic
| |
| |-- References
| | |-- Reference Variables
| | |-- Passing by Reference
| |
| |-- Templates
| | |-- Function Templates
| | |-- Class Templates
| |
| |-- Exception Handling
| | |-- Try-Catch Blocks
| | |-- Throwing Exceptions
| | |-- Standard Exceptions
|
|-- Data Structures
| |-- Arrays and Strings
| | |-- One-Dimensional and Multi-Dimensional Arrays
| | |-- String Handling
| |
| |-- Linked Lists
| | |-- Singly and Doubly Linked Lists
| |
| |-- Stacks and Queues
| | |-- Stack Operations (Push, Pop, Peek)
| | |-- Queue Operations (Enqueue, Dequeue)
| |
| |-- Trees and Graphs
| | |-- Binary Trees, Binary Search Trees
| | |-- Graph Representation and Traversal (DFS, BFS)
|
|-- Standard Template Library (STL)
| |-- Containers
| | |-- Vectors, Lists, Deques
| | |-- Stacks, Queues, Priority Queues
| | |-- Sets, Maps, Unordered Maps
| |
| |-- Iterators
| | |-- Input and Output Iterators
| | |-- Forward, Bidirectional, and Random Access Iterators
| |
| |-- Algorithms
| | |-- Sorting, Searching, and Manipulation
| | |-- Numeric Algorithms
|
|-- File Handling
| |-- Streams and File I/O
| | |-- ifstream, ofstream, fstream
| | |-- Reading and Writing Files
| | |-- Binary File Handling
|
|-- Testing and Debugging
| |-- Debugging Tools
| | |-- gdb (GNU Debugger)
| | |-- Valgrind for Memory Leak Detection
| |
| |-- Unit Testing
| | |-- Google Test (gtest)
| | |-- Writing and Running Tests
|
|-- Deployment and DevOps
| |-- Version Control with Git
| | |-- Integrating C++ Projects with GitHub
| |-- Continuous Integration/Continuous Deployment (CI/CD)
| | |-- Using Jenkins or GitHub
| |
|   |--Free courses
| | |--https://www.udacity.com/course/c-for-programmers--ud210
|   |   |--Microsoft Documentation (https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019)

|   |   |--Udemy Course (https://www.udemy.com/course/introduction-to-algorithms-and-data-structures-in-c/)

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Top 15 Advanced Power BI Interview Questions

Link: https://dev.to/henryclapton/top-15-advanced-power-bi-interview-questions-2942

4 Most Useful Charts to Show Trends: Data Visualization

Link: https://dev.to/justdetermined/4-most-useful-charts-to-show-trends-data-visualization-3pdg

Urgent: Senior Frontend Engineer (Remote)

We need a Senior Frontend Engineer skilled in React, Vue, and Angular, with a Computer Science background.

Requirements:
✔️ Strong frontend experience
✔️ Proficiency in React, Vue, Angular
✔️ UI/UX knowledge & API integration

Remote | Apply: Send your CV to office@mamaket.com

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Tools for AI Enthusiasts
* Google Colab: For learning AI/ML coding.
* Kaggle: To practice data science projects.
* Hugging Face: For working with AI models.
* OpenAI API: To integrate AI into apps.
* TensorFlow: To build and deploy AI models.
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Hackathon Alert!

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Tools Every AI Engineer Should Know

1. Data Science Tools
* Python: Preferred language with libraries like NumPy, Pandas, Scikit-learn.
* R: Ideal for statistical analysis and data visualization.
* Jupyter Notebook: Interactive coding environment for Python and R.
* MATLAB: Used for mathematical modeling and algorithm development.
* RapidMiner: Drag-and-drop platform for machine learning workflows.
* KNIME: Open-source analytics platform for data integration and analysis.

2. Machine Learning Tools
* Scikit-learn: Comprehensive library for traditional ML algorithms.
* XGBoost & LightGBM: Specialized tools for gradient boosting.
* TensorFlow: Open-source framework for ML and DL.
* PyTorch: Popular DL framework with a dynamic computation graph.
* H2O.ai: Scalable platform for ML and AutoML.
* Auto-sklearn: AutoML for automating the ML pipeline.

3. Deep Learning Tools
* Keras: User-friendly high-level API for building neural networks.
* PyTorch: Excellent for research and production in DL.
* TensorFlow: Versatile for both research and deployment.
* ONNX: Open format for model interoperability.
* OpenCV: For image processing and computer vision.
* Hugging Face: Focused on natural language processing.

4. Data Engineering Tools
* Apache Hadoop: Framework for distributed storage and processing.
* Apache Spark: Fast cluster-computing framework.
* Kafka: Distributed streaming platform.
* Airflow: Workflow automation tool.
* Fivetran: ETL tool for data integration.
* dbt: Data transformation tool using SQL.

5. Data Visualization Tools
* Tableau: Drag-and-drop BI tool for interactive dashboards.
* Power BI: Microsoft’s BI platform for data analysis and visualization.
* Matplotlib & Seaborn: Python libraries for static and interactive plots.
* Plotly: Interactive plotting library with Dash for web apps.
* D3.js: JavaScript library for creating dynamic web visualizations.

6. Cloud Platforms
* AWS: Services like SageMaker for ML model building.
* Google Cloud Platform (GCP): Tools like BigQuery and AutoML.
* Microsoft Azure: Azure ML Studio for ML workflows.
* IBM Watson: AI platform for custom model development.

7. Version Control and Collaboration Tools
* Git: Version control system.
* GitHub/GitLab: Platforms for code sharing and collaboration.
* Bitbucket: Version control for teams.

8. Other Essential Tools
* Docker: For containerizing applications.
* Kubernetes: Orchestration of containerized applications.
* MLflow: Experiment tracking and deployment.
* Weights & Biases (W&B): Experiment tracking and collaboration.
* Pandas Profiling: Automated data profiling.
* BigQuery/Athena: Serverless data warehousing tools.

Mastering these tools will ensure you are well-equipped to handle various challenges across the AI lifecycle.

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ChatGPT is a MONEY MAKING machine only if you know HOW TO USE IT RIGHT!

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Here is the list for you 👇

📌 VIRTUAL STORYTELLING NIGHTS

IDEA - Host virtual storytelling sessions, let ChatGPT do the storytelling, and charge a small entry fee. It's like a virtual bonfire night, with AI as the narrator.

How to get started ?

Here is the STEP BY STEP TUTORIAL -

1. Define the Purpose
2. Craft Your Story Prompt
3. Run the Prompt through ChatGPT
4. Edit and Refine the Story
5. Choose the Right Platform
6. Promote Your Event
7. Host the Event
8. Gather Feedback and Iterate

📌 AI-DRIVEN PERSONALIZED GIFT SERVICES

Use ChatGPT to create unique, custom gifts based on customer inputs. These gifts could include:

- Poems
- Letters
- Short stories etc

The personalized content can then be transformed into a physical/ digital product.

📌 INTERACTIVE E-LEARNING

We have seen the BOOM of Amazon KDP revenues. Authors are earning $10,000 per month.

Why can't you?

Here is idea breakdown -

Craft AI-generated quizzes, interactive learning materials, or even entire textbooks using ChatGPT.
Steps:

1. Identify Your Topic
2. Outline Your Content
3. Craft Your Prompts for ChatGPT
4. Generate Your Content with ChatGPT
5. Edit and Refine
6. Format Your Book
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9. Promote Your Book

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Not everyone can use AI the way you can.

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