Data Science with Analogies, Algorithms and Solved Problems

Name: Data Science with Analogies, Algorithms and Solved Problems
Rating: 4.3 (358 reviews)

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
Explore data mining, machine learning, deep learning, supervised and unsupervised learning, and reinforcement learning through Python with prediction and visualization.
Data Mining and Deep Learning4:16
Learn data mining as discovering patterns and knowledge from data warehouses using mining tools and a gold nugget analogy, then explore deep learning with neural networks for image classification.
Big Data5:35
Explore big data and the five v's—volume, velocity, variety, veracity, and value—and see how distributed systems and analytics transform vast data into insights and strategic business decisions.
Attributes5:56
Explore how attributes define features of data objects and distinguish qualitative and quantitative types, including nominal, ordinal, binary (symmetric and asymmetric), and numeric, discrete, continuous, interval and ratio scaled attributes.
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
Discover essential data science libraries in Python, including pandas for data manipulation, NumPy for multidimensional arrays, and Matplotlib for visualization, with practical notes on reading CSV and handling missing data.
Quiz 1

Supervised Learning3:39
Explore supervised learning, where a machine learns from labeled training data to classify or predict outputs, covering classification and regression with examples like fruits and customer purchase data.
Bayesian Classifier6:01
Learn how the bayesian classifier, also called naive bayes, uses prior, likelihood, and posterior probabilities to classify samples and predict class membership.
ID3 Algorithm8:16
Explore how the ID3 algorithm builds a decision tree for classification by calculating entropy and information gain across attributes such as age and income, using a binary target 'buys laptop'.
Decision Tree7:02
Learn how a decision tree is built using information gain to select the root and split data into leaf nodes, with age as the root and branches.
Regression4:46
Explore regression for predicting numerical values with linear and nonlinear models. Use simple linear regression with y equals alpha plus beta x, predictor x, and dependent y to estimate outcomes.
Unsupervised Learning2:27
Explore unsupervised learning, where machines group unlabeled data into clusters by similarities and differences, contrasting with supervised learning and techniques like clustering for dog and cat images.
K-means clustering5:01
K-means clustering is an unsupervised, partitioning based method that forms a user-specified number of clusters by assigning data points to groups based on a threshold and recalculating cluster averages.
Reinforcement Learning2:28
Reinforcement learning is a type of machine learning where an agent learns from interacting with an environment to maximize rewards through trial and error, receiving positive or negative feedback.
Quiz 2

CNN Algorithm2:26
Discover the convolutional neural network (CNN) algorithm and its ability to recognize images. See how filters and convolutional layers feed into a final fully connected layer to classify images.
RNN and LSTM Algorithm4:47
Explain how recurrent neural networks feed the previous output as input and use a hidden state to remember sequences, and how LSTMs address long term dependencies.
Data Visualization2:11
Explore data visualization as the bridge between data mining and insight, using techniques like pixel oriented visualization and tree map visualization technique to map attributes, hierarchy, and quantitative patterns.
Anaconda Installation1:51
Install Anaconda, the open source platform with Jupyter and Spyder, supporting Python and R via conda. Complete Windows setup and launch Anaconda Navigator to code.
Quiz 3

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.

Data Science with Analogies, Algorithms and Solved Problems

What you'll learn

Explore related topics

Course content

Introduction6 lectures • 28min

Types Of Machine Learning and Algorithms8 lectures • 40min

Fundamentals of Deep Learning4 lectures • 11min

Requirements

Description

Who this course is for: