Please see the attached flowchart to get some of the components of analytics/data science. If you are planning on just running descriptive reports, build data warehouses, maintain data quality and occasionally run a simple regression - you do not need machine learning. For every other Data Analytics/Science job, you will need to understand and run Machine learning.
Please see the attached flowchart to get some of the components of analytics/data science. If you are planning on just running descriptive reports, build data warehouses, maintain data quality and occasionally run a simple regression - you do not need machine learning. For every other Data Analytics/Science job, you will need to understand and run Machine learning.
Data Analytics is a Bigger picture of the same thing which is referred as Machine learning. Like Data Analytics has various categories based on the Data used, similarly, Machine Learning, expresses the way one machine learns a code or work in supervised,unsupervised,semi supervised and reinforcement manner.Machine learning is a method of data analysis that automates analytical model building.
It is a branch of artificial intelligence based on the idea that machines should be able to learn and adapt through experience. It is the science of creating algorithms and program which learn on their own. Once designed, they do not need a human to become better.
Some of the common applications of machine learning include following: Web Search, spam filters, recommended systems, ad placement, credit scoring, fraud detection, stock trading, computer vision and drug design.
An easy way to understand is this - it is humanly impossible to create models for every possible search or spam, so you make the machine intelligent enough to learn by itself. When you automate the later part of data mining - it is known as machine learning.
Right Now Machine Learning is Trending .
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Machine Learning A-Z™: Hands-On Python & R In Data Science
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.
This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:
- Part 1 - Data Preprocessing
- Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 - Clustering: K-Means, Hierarchical Clustering
- Part 5 - Association Rule Learning: Apriori, Eclat
- Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
- Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Moreover, the course is packed with practical exercises which are based on live examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
All The best .
Of course YES, in such case we are talking about the philosophy of an idea or lecture. Thanks a lot for invitation.
Regards,
Emad
Excellent comments by dear colleagues Dr. Beemnet and Prof. Emad. In fact, data analytics in general help us with skills in managing our data in our research whether experimental or in real life setting while deep learning offers us the perspective to philosophize theories in our research and our findings as well. Thanks for the invitation. Best regards
Dickson
Yes, I think so. Its one of the key area in research presently.
There are vast amount of data here and there that requires data analytic tools to make meaning out of the so that they can be useful.
Dr Beemnet already mentioned the tools you need to learn to be able to carry out data science
https://www.quora.com/Is-it-worth-moving-away-from-machine-learning-and-data-science
https://www.kdnuggets.com/2018/05/data-science-machine-learning-business-analytics.html
https://medium.com/iotforall/data-analytics-vs-machine-learning-90d93dc06291
https://dzone.com/articles/top-5-data-science-and-machine-learning-course-for
https://towardsdatascience.com/data-science-machine-learning-and-artificial-intelligence-for-art-1ac48c4fad41
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