Complete Machine Learning & Data Science with Python| ML A-Z

Complete Machine Learning & Data Science with Python| ML A-Z

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

Complete Machine Learning & Data Science with Python| ML A-Z

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,K-Nearest Neighbor(KNN) Algorithm,Support Vector Machine Algorithm,Random Forest Algorithm
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 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

Join Our Telegram Group21500+ Members

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.