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

    Posted By: ELK1nG
    Complete Machine Learning & Data Science with Python| ML A-Z

    Complete Machine Learning & Data Science with Python| ML A-Z
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
    Language: English | Size: 3.16 GB | Duration: 11h 13m

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

    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

    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.