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Data Science with Analogies, Algorithms and Solved Problems
Rating: 4.2 out of 5(355 ratings)
20,085 students

Data Science with Analogies, Algorithms and Solved Problems

Machine learning, Data Mining, Data Science, Deep Learning, Data analysis, Data analytics, Python, Visualization
Created byAjay Dhruv, Ph.D., Neha Mayekar, Shreya Pattewar, Shubham Patil
Last updated 3/2020
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.

Course content

3 sections • 18 lectures • 1h 19m total length

  • Introduction1:15
  • Data Mining and Deep Learning4:16
  • Big Data5:35
  • Attributes5:56
  • Outlier4:27

    Identify global outliers or point anomalies, contextual (conditional) outliers, and collective outliers, and explore detection challenges like noise, application-specific needs, and explainability.

  • Libraries in Python6:41
  • Quiz 1

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:

• 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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