This repository serves as a learning and practice ground for Python-based machine learning (ML) projects. It includes a collection of Jupyter notebooks, scripts, and resources covering fundamental to advanced concepts in ML, with hands-on examples and exercises. Whether you're a beginner exploring the basics or an experienced practitioner looking f

AtlasAI offers a comprehensive TinyML course using Arduino and Raspberry Pi. This repository includes detailed Jupyter notebooks covering TinyML fundamentals, data collection, model training with Edge Impulse, deployment, and hands-on projects. Ideal for students and professionals exploring machine learning on embedded devices.

Objective: The repository is a learning exercise to: Apply the fundamental concepts of machine learning from an available dataset Evaluate and interpret and justify our results and interpretation based on observed dataset in Jupyter notebook The analysis is divided into four sections, saved in juypter notebooks in this repository Identifying the problem and Data Sources Exploratory Data Analysis Pre-Processing the Data Build model to predict whether breast cell tissue is malignant or Benign Part_1: Identifying the problem and Getting data. Aim:Identify the types of information contained in our data set We'll used Python modules to import external data sets for the purpose of getting to know/familiarize ourself with the data to get a good grasp of the data and think about how to handle the data in different ways. Part_2: Exploratory Data Analysis Aim: Explore the variables to assess how they relate to the response variable In this notebook, we'll get familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) Part_3: Pre-Processing the data Aim: Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model. We'll use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. Part_4: Predictive model using Support Vector Machine (svm) Aim: Construct predictive models to predict the diagnosis of a breast tumor. We'll construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). we'll also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. Part_5: Optimizing the Support Vector Classifier Aim: Construct predictive models to predict the diagnosis of a breast tumor. We'll aim to tune parameters of the SVM Classification model using scikit-learn.