• views: 80
• date: 23 January 2026
• posted by: voska89

What you'll learn
Data Science libraries like Numpy , Pandas , Matplotlib, Scipy, Scikit Learn, Seaborn , Plotly and many more
Machine learning Concept and Different types of Machine Learning
Machine Learning Algorithms like Regression, Classification, Naive Bayes Classifier, Decision Tree, Support Vector Machine Algorithm etc..
Feature engineering
Python Basics
Requirements
No previous programming experience needed.
Description
Artificial Intelligence is the next digital frontier, with profound implications for business and society. The global AI market size is projected to reach $202.57 billion by 2026, according to Fortune Business Insights.This Data Science & Machine Learning (ML) course is not only 'Hands-On' practical based but also includes several use cases so that students can understand actual Industrial requirements, and work culture. These are the requirements to develop any high level application in AI. In this course several Machine Learning (ML) projects are included.1) Project - Customer Segmentation Using K Means Clustering2) Project - Fake News Detection using Machine Learning (Python)3) Project COVID-19: Coronavirus Infection Probability using Machine Learning4) Project - Image compression using K-means clustering | Color Quantization using K-MeansThis course include topics ---What is Data Science Describe Artificial Intelligence and Machine Learning and Deep Learning Concept of Machine Learning - Supervised Machine Learning , Unsupervised Machine Learning and Reinforcement LearningPython for Data Analysis- Numpy Working envirnment-Google ColabAnaconda Installation Jupyter Notebook Data analysis-PandasMatplotlib What is Supervised Machine LearningRegressionClassification Multilinear Regression Use Case- Boston Housing Price Prediction Save Model Logistic Regression on Iris Flower Dataset Naive Bayes Classifier on Wine Dataset Naive Bayes Classifier for Text Classification Decision TreeK-Nearest Neighbor(KNN) Algorithm Support Vector Machine AlgorithmRandom Forest Algorithm IWhat is UnSupervised Machine Learning Types of Unsupervised Learning Advantages and Disadvantages of Unsupervised Learning What is clustering? K-means Clustering Image compression using K-means clustering | Color Quantization using K-Means Underfitting, Over-fitting and best fitting in Machine Learning How to avoid Overfitting in Machine LearningFeature EngineeringTeachable MachinePython BasicsIn the recent years, self-driving vehicles, digital assistants, robotic factory staff, and smart cities have proven that intelligent machines are possible. AI has transformed most industry sectors like retail, manufacturing, finance, healthcare, and media and continues to invade new territories. Everyday a new app, product or service unveils that it is using machine learning to get smarter and better.NOTE :- In description reference notes also provided , open reference notes , there is link. You can download datasets there.
Anyone interested in Machine Learning.,Any students in college who want to start a career in Data Science.
Homepage

https://www.udemy.com/course/complete-machine-learning-data-science-libraries-with-python/

   
   
   

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