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About the Tutorial             

Today’s Artificial Intelligence (AI) has far surpassed the hype of blockchain and quantum computing. The developers now take advantage of this in creating new Machine Learning models and to re-train the existing models for better performance and results. This tutorial will give an introduction to machine learning and its implementation in Artificial Intelligence.

Audience 

This tutorial has been prepared for professionals aspiring to learn the complete picture of Machine Learning and AI. This tutorial caters to the learning needs of both the novice learners and experts, to help them understand the concepts and implementation of AI

Prerequisites 

The learners of this tutorial are expected to know the basics of Python programming. Besides, they need to have a solid understanding of computer programming and fundamentals. If you are new to this arena, we suggest you pick up tutorials based on these concepts first, before you embark on with Machine Learning.

Copyright & Disclaimer 

@Copyright 2019 by BeingDatum. 

All the content and graphics published in this section are the property of Being Datum. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any content or a part of the contents of this e-book in any manner without written consent of the publisher. We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. Being Datum provides no guarantee regarding the accuracy, timeliness or completeness of our website or its contents including this tutorial. If you discover any errors on our website or in this tutorial, please notify us at contact@beingdatum.com

Course Curriculum

INTRODUCTION
Introduction to Machine Learning, AI & Data ScienceFREE 00:00:00
Use CasesFREE 00:00:00
Python BasicsFREE 00:00:00
Alternatives to Python for Data Science 00:00:00
Essential Modules/LibrariesFREE 00:00:00
Types of LearningFREE 00:00:00
CLASSIFICATION ALGORITHMS
Classification Algorithms 00:00:00
K-nearest neighbors (K-NN) 00:00:00
Decision Trees 00:00:00
Random ForestFREE 00:00:00
Naive BayesFREE 00:00:00
Support Vector Machines 00:00:00
Logistic RegressionFREE 00:00:00
REGRESSION ALGORITHMS
Regression Algorithms 00:00:00
Simple Linear RegressionFREE 00:00:00
Multiple Linear RegressionFREE 00:00:00
Polynomial Regression 00:00:00
CLUSTERING ALGORITHMS
Clustering AlgorithmsFREE 00:00:00
K-Means ClusteringFREE 00:00:00
Hierarchical ClusteringFREE 00:00:00
Mean-Shift Algorithm 00:00:00
ASSOCIATION RULE LEARNING
Association Rule Learning 00:00:00
ENSEMBLE LEARNING
Ensemble TechniquesFREE 00:00:00
TIME SERIES ANALYSIS
Time Series AnalysisFREE 00:00:00
INTRODUCTION TO DEEP LEARNING
Introduction to Deep LearningFREE 00:00:00
DIMENSIONALITY REDUCTION
Dimensionality ReductionFREE 00:00:00

Course Reviews

5

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4 ratings
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  1. Awesome course!

    5

    Awesome course!

  2. Good course for beginners

    5

    Good course for beginners, looking forward for the deep learning content asap

  3. Narration is Simple and Informative

    5

    Narration is Simple and Informative

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