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Data Science with Analogies, Algorithms and Solved Problems

  • Machine learning, Data Mining, Data Science, Deep Learning, Data analysis, Data analytics, Python, Visualization
  • Free tutorial
  • Rating: 4.1 out of 54.1 (287 ratings)
  • 18,090 students
  • 1hr 19min of on-demand video
  • Created by Ajay Dhruv, Ph.D.Neha MayekarShreya PattewarShubham Patil

English

What you’ll learn

  • Truly understand what Algorithms, Big Data, Machine Learning, and Data Science is.
  • To understand how these different domains are distinct and how they collaborate as well.
  • To really understand where these concepts are used using real life analogies.
  • To understand the different algorithms and their working.
  • To learn how these algorithms are applied to solve various problems.

Requirements

  • Basic mathematics and computer skills.

Description

Interested to know about the field of Machine Learning?

Then this course is for you! This course has been designed such that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this field. While preparing this course special care is taken that the concepts are presented in fun and exciting way but at the same time, we dive deep into machine learning.

Here is a list of few of the topics we will be learning:

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• Difference between Data Mining and Deep Learning

• Data and 5 Vs of Big Data

• Types of Attributes

• Outliers

• Supervised learning, Unsupervised learning, Reinforcement learning

• Python Libraries

• CNN, RNN, LSTM

• K – means Clustering Algorithm

• Bayesian Algorithm, ID3 Algorithm

• Simple Linear Regression

• Anaconda

• Visualization

Who this course is for:

  • People/Researchers interested in machine learning
  • Technologists who are curious about how deep learning really works
  • Any student willing to begin a career in machine learning
  • People who want to brush up their basics.

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Course content

3 sections • 18 lectures • 1h 19m total lengthCollapse all sections

Introduction6 lectures • 28min

  • Introduction01:15
  • Data Mining and Deep Learning04:16
  • Big Data05:35
  • Attributes05:56
  • Outlier04:27
  • Libraries in Python06:41
  • Quiz 16 questions

Types Of Machine Learning and Algorithms8 lectures • 40min

  • Supervised Learning03:39
  • Bayesian Classifier06:01
  • ID3 Algorithm08:16
  • Decision Tree07:02
  • Regression04:46
  • Unsupervised Learning02:27
  • K-means clustering05:01
  • Reinforcement Learning02:28
  • Quiz 26 questions

Fundamentals of Deep Learning4 lectures • 11min

  • CNN Algorithm02:26
  • RNN and LSTM Algorithm04:47
  • Data Visualization02:11
  • Anaconda Installation01:51
  • Quiz 33 questions

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