Machine Learning Workshop 1 : Introduction to Machine Learning

This workshop is presented to you by ECAST -Electronics and computer Community Amidist Students, Thapathali in coordination with Tensor, IOE Thapathali. This workshop aims to introduce machine learning among students and young professionals, providing them with the foundational knowledge and skills necessary to understand and apply machine learning techniques in real-world scenarios.

Throughout this session, we will cover key machine learning concepts, including data preprocessing, supervised learning algorithms, evaluation metrics, and practical examples of how to implement machine learning models using popular tools and libraries. By the end of the workshop, participants will have a clearer understanding of how machine learning works, its applications in various fields, and how to develop their own machine learning models for different tasks.

Table of contents :

• Objectives
• Syllebus
• Instructors
• Practice Labs
• Requirements
• Contact Us

Objectives:

• Understand the fundamental concepts of machine learning, including supervised and unsupervised learning.
• Gain practical experience in implementing basic machine learning algorithms using Python and popular libraries like NumPy, pandas and Scikit-learn.
• Learn how to preprocess data, split datasets, and evaluate models using appropriate metrics.
• Be introduced to the real-world applications of machine learning and how to tackle problems like classification, regression, and clustering.

Syllebus :

Basics of Machine Learning

• Data Science , AI, ML, DL
• Machine Learning Pipeline (Data Collection, Data Preparation, Feature Engineering, EDA, Model Training, Evaluation, Deployment, Monitoring)
• Types (Supervised, Unsupervised, Reinforcement)
• Basics of data collection (Self preparation, Public Dataset, Web Scrapping, API, Augmentation, Survey, Form)
• Basic Syntax of Pandas and NumPy

Data Preprocessing And Feature Engineering

• Handling Null Values
• Handling Duplicate Values
• Outlier Detection and Handling
• Data Manipulation (Selection, Filtration, Merging, Splitting)
• Statistics (Mean, Median, Mode, Variance, Standard deviation)
• Feature Engineering(Normalization, Standardization, Transformation, Encoding)
• Plotting basic charts and graphs

Linear Regression

• Least square principle
• Closed form solution
• Batch gradient descent
• Error Function/ Cost function (MAE,MSE)
• Hands on real data

Loogistic Regression

• Overfitting & underfitting
• Bias variance tradeoff
• Binary classification
• Sigmoid
• Binary cross entropy
• Decision Boundry

Clustering

• Basics of clustering
• K-Mean clustering
• Selecting the number of clusters (Elbow Method, Silhouette Score)
• Cost function (WCSS/ Inertia)
• Limitations (Spherical Symmetry, Outliers)

Deployment

• API development (FastAPI, Flask)
• Frontend integration using Streamlit

Instructors :

• Rajendra Baskota – ML Engineer, Fusemachines
• Khem Raj Shrestha – ML Intern, Fusemachines
• Niyoj Oli – Gold MLSA | Leapfrog Student Partner
• Om Prakash Sharma – Gold MLSA
• Sandesh Dhital – AI Fellow, Fusemachines
• Ishwor Pokharel – Coordinator, Tensor IOE

Practice Labs :

This repo contains practice labs for every day of workshop. Labs of next day will be provided the day before.

How to access lab ?

1. clone this repository

    git clone https://github.com/tensor-ioe/ML_Workshop_1.git
   

2. Update the new labs the day before as :

   git pull origin main

new updates will be automatically added to your existing files

How to submit the assaignments ?

1. Fork this repository into your github account.

2. clone your forked repository as

    git clone https://github.com/your_github_username/forked_repo_name.git
   replace `your_github_username`and` forked_repo_name` with your username and the name of your repository name.
   

3. create a new branch with your name and clz roll number

   git checkout -b your_name_class_roll_number
   Replace `your_name_class_roll_number` with your actual name and class roll number.
   

4. Make your changes or complete the assignment in this branch.

5. commit your changes

   git add .
   git commit -m "Completed Assignment of Day-n"

6. Push your changes to your forked repository

   git push origin your_name_class_roll_number

7. Create a Pull Request from your forked repository to this repository.

Note : you can directly sync new available labs into your forked repository

This repository will be archived after this workshop

Requirements

• Python 3.x

• Jupyter Notebook or any Python IDE (e.g., PyCharm, VS Code)

• Libraries:

  • NumPy
  • pandas
  • Matplotlib
  • Scikit-learn
  • Seaborn

you can install the libraries using :

pip install -r requirement.txt

Contact

ECAST : ecast@tcioe.edu.np
Tensor : tensor@tcioe.edu.np