Fun over IP

07/05/2025

Shout out to my newest followers! Excited to have you onboard! Imam Hasan Sujon, Muhammad Saiful Islam, Arup Das, Apple Mahmud, Ohidul Haq, Ayrin Abedin, Tareq Al Abedin Piyal, Sahib Sadman, Arman Sheikh Arman, Shahik AB, MD Manik Mir, Muzibul Hossain, Mohd Tarique Noor, Enayet Sharif, Sujoy Saha, Hamjajul Asrafi, Abdul Khaleque, Shakir Hossain, Ishtiaque Azad, Suman Nag

02/05/2025

Polynomial Regression is an extension of linear regression that allows us to model more complex, non-linear relationships between variables. Instead of fitting a straight line through the data, it fits a curved line (a polynomial equation like a parabola or higher-degree curve) that better captures patterns when the data bends or changes direction. For example, if we're predicting the growth of a plant over time, the growth may speed up and then slow down — something a straight line can't represent accurately, but a polynomial curve can. It's a powerful tool when the data doesn’t follow a straight path, helping us build more accurate predictive models.

02/05/2025

Unlike linear regression, which predicts continuous values, Logistic Regression is used when the outcome is categorical — like yes/no, spam/not spam, or disease/no disease. It works by using a sigmoid function to map input values to a probability between 0 and 1. For example, it might predict the chance that a student will pass an exam based on study hours. If the probability is above a certain threshold (like 0.5), it classifies the result as "pass." Logistic Regression is simple, yet powerful — and widely used in classification problems across industries.

02/05/2025

:FindingtheSweetSpot
This 3D graph shows the Gradient Descent algorithm at work over a cost surface defined by the slope (m) and intercept (b) in a linear regression model. The surface represents the Mean Squared Error (MSE) — a measure of how far off the predictions are from actual values. The red path on the surface shows how gradient descent moves step-by-step, adjusting both m and b to reduce the cost and reach the bottom of the curve — the point of minimum error. It’s like hiking down a valley in a 3D landscape, where the goal is to find the lowest point. This is how machine learning models learn to make better predictions!

02/05/2025

This graph beautifully illustrates how the Gradient Descent algorithm works. Imagine you're standing on a hill (the curve), trying to reach the lowest point (minimum cost). Starting from an initial weight, gradient descent takes small steps in the direction where the cost decreases most steeply — this is guided by the slope or gradient of the curve. With each incremental step, the algorithm updates the weight to reduce the cost, gradually moving closer to the optimal point. This method is widely used in machine learning to fine-tune model parameters and minimize errors. Simple yet powerful!

02/05/2025

In this graph of Height vs Age, the red line represents the linear regression best fit line, which captures the overall trend in the data. As we can see, height generally increases with age, and the best fit line helps us visualize this relationship by minimizing the distance between itself and all the data points. Even though there are some fluctuations in the actual measurements, the line gives us a clear, simplified view of how height tends to grow as age increases. This is a powerful example of how linear regression helps us find patterns in data and make predictions!

10/06/2024

Ahmed Ronjue

Send a message to learn more

10/06/2024

Master AWS EC2 & VPC in Just 4 Hours with Hands-On Learning!

In today's fast-paced and demanding professional landscape, finding time to learn new technologies that can advance our careers is increasingly challenging. The AWS Certified Solutions Architect Associate course, in particular, presents a significant hurdle for professionals who lack experience in the enterprise network domain. The course's extensive curriculum can make it daunting to begin and even harder to complete.
To address this challenge, I have divided the course into manageable sections, focusing first on compute services. This section will cover essential components such as EC2 and VPC.
The topics include –
a) Fundamental EC2 instance basics
b) Launching EC2 instances
c) Gaining SSH access
d) AMIs, snapshots
e) Bastion hosts
f) Security groups
g) Elastic IPs
h) Key pairs and
i) Network interfaces.
Additionally, we will explore VPC components like –
a) Understanding VPC
b) Subnets
c) Route tables
d) Internet gateways
e) NAT gateways and instances
f) VPC endpoints and
g) VPC peering
This structured approach aims to simplify the learning process, making it easier for busy professionals to gain the necessary skills and knowledge.

Send a message to learn more

03/09/2022

Hello Friends.
Fun over IP is going to conduct a 35 hours training on Advance Routing covering below items-
1. OSPF
a. Area design and terminology
b. Network types ( Broadcast & P2P network)
c. Forming neighborship and troubleshooting.
d. Understanding Topology table.
e. Route filtering and area types
f. Route Summarization
g. OSPF virtual link
h. OSPF sham link
2. Route Redistribution and filtering.
a. Route redistribution between different routing protocols.
b. Route tagging and filtering
c. Routing loop prevention.
3. BGP
a. BGP Overview
b. Configuring & troubleshooting BGP neighborship.
c. Route advertisement into BGP.
d. Loop prevention mechanism for iBGP & eBGP.
e. BGP split horizon rule
f. BGP Route Reflector
g. BGP Path Selection
h. Filtering BGP Routes
i. Understanding routing black haul
j. BGP Authentication
k. Verifying BGP
4. MPLS
a. Why do we need MPLS?
b. How MPLS works?
c. MPLS network architecture.
d. Prerequisites of MPLS deployment.
e. MPLS control plane
f. Label allocation by LDP & RSVP.
g. Understanding MPLS LSP.
h. Understanding L3 VPN & L2VPN
i. MPLS traffic engineering.
5. IPv6
a. IPv6 addressing details.
b. IPv6 routing
c. IPv6 tunneling ( 6to4)- IPv6 communication over IPv4 network
d. 6vPE solution for IPv6 communication over MPLS network.
6. Segment routing basic.
7. Python scripting for network automation.

For details please inbox here.

28/10/2019

Fun_Over_IP is excited to offer a 3 days log training course named "Network Programmability & Automation".

***The goal of the course is to equip trainees with foundational knowledge and a set of baseline skills in the areas of network programmability and automation by using Python.

*** 5(five) trainees in one batch.
*** Start: 1st December,2019.

*** For details Please feel free to call us.
Contact Number:+8801712558377

10/07/2019

5 Days (30 Hours) training on

OSPF,
BGP,
MPLS &
Python scripting for Network Automation

is going to start soon.

Interested candidates can contact with me for details.

Thanks
01712558377