https://towardsdatascience.com/who-is-a-data-engineer-how-to-become-a-data-engineer-1167ddc12811
https://medium.com/datadriveninvestor/python-vs-r-choosing-the-best-tool-for-ai-ml-data-science-7e0c2295e243
https://towardsdatascience.com/who-is-a-data-engineer-how-to-become-a-data-engineer-1167ddc12811
https://medium.com/datadriveninvestor/python-vs-r-choosing-the-best-tool-for-ai-ml-data-science-7e0c2295e243
Posted in Career/Earning, Data Science
Pagerank Algorithm Eigenvalue
Square Matrices
Geometric Interpretation
Eigenvalue:
https://www.khanacademy.org/math/linear-algebra/alternate-bases/eigen-everything/v/linear-algebra-introduction-to-eigenvalues-and-eigenvectors
Deep Learning, Deep Neural Network:
Posted in Data Science, Machine Learning
Here , I will try to share some of the info about data scince mastery step by step and pythonic attitude :p
These links may help:
https://www.quora.com/Which-career-is-more-promising-data-scientist-or-software-developer-1
https://www.inferentialthinking.com/
https://www.facebook.com/groups/pythonbd/permalink/814281808673238/
Data Podcats:
https://dataskeptic.com/
Python:
https://www.coursera.org/specializations/python
https://pythonspot.com/en/
Job:
Posted in Data Science
Chatbot:
https://chatbotslife.com/machine-learning-for-dummies-part-1-dbaca076ec07
Sentiment Analysis, Natural Language Processing:
https://medium.com/udacity/natural-language-processing-and-sentiment-analysis-43111c33c27e
Posted in Data Science
http://www.kdnuggets.com/2015/09/top-20-data-science-moocs.html
https://lagunita.stanford.edu/courses/Home/Databases/Engineering/about
Posted in Data Science, Database
A confusion matrix shows the number of correct and incorrect predictions made by the classification models compared by actual outcomes (target value) in the data.
Found a good lecture regarding confusion matrix with easy explanation for HIV AIDS. Video is found below and my own drawing regarding this is also given below:
1. Watch all the videos in youtube regarding data science including algorithms.
2. For data mining complete a series example Rushdi Shams with WEKA
3. For basic theory:
Watch and complete UDACITY, Andrew NG machine learning course step by step.
Udacity course I have found much interesting than Andrew NG but I will finish both In Sha Allah
4. There is a UDEMY paid course hands on data science with python
5. For python learn from codeacademy
UBUNTU is good rather windows for python
6. Subeen vaia’s book is also good for python
To be continued
Top 5 popular data science algorithms:
Decision Tree
Random Fores
Association Rule Mining
Linear Regression
K-means Clustering
Data science is nothing but extracting and actionable knowledge from data:
Data Scienctist must know data architecture , machine learning, data analytics.
Machine Learning Algorithms(sample)
Continuos:
Unsupervised Supervised
Clustering Regression
Kmeans Linear
SVD Polynomial
PCA Decision Trees
Radom Forests
Categorical:
Association Analysis Classification
Apriori KNN
FP Growth Trees
Hidden Markov Model Logistic Regression
Naive Bayes
Bayes
Supervised Learning: The categories of the data is already known
Unsupervised Learning: The learning process attempts to find appropriate category for the data.