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

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 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

 

Matplotlib

 

What is Supervised Machine Learning

 

Regression

 

Classification

 

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.

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