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Learn Numpy, Pandas, Matplotlib, Seaborn, Scipy, Supervised & Unsupervised Machine Learning A-Z and feature engineering


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 Clustering


2) Project – Fake News Detection using Machine Learning (Python)


3) Project COVID-19: Coronavirus Infection Probability using Machine Learning


4) Project – Image compression using K-means clustering | Color Quantization using K-Means


This 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 Learning


Python for Data Analysis- Numpy


Working envirnment-


Google Colab


Anaconda Installation


Jupyter Notebook


Data analysis-Pandas




What is Supervised Machine Learning






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 Tree


K-Nearest Neighbor(KNN) Algorithm


Support Vector Machine Algorithm


Random Forest Algorithm I


What 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 Learning


Feature Engineering


Teachable Machine


Python Basics


In 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.


Who this course is for:

Anyone interested in Machine Learning.

Any students in college who want to start a career in Data Science.

Enroll Now