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

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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-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 IWhat is Un Supervised 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. NOTE:- In description reference notes also provided , open reference notes , there is Download link. You can download datasets there.