A Solid Plan for Learning Data Science, Machine Learning, and Deep Learning

Check out this solid plan for learning Data Science, Machine Learning, and Deep Learning. The entire plan is currently available at no cost to KDnuggets readers.



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A Solid Plan for Learning Data Science, Machine Learning, and Deep Learning
 
Interested in learning Data Science, Machine Learning, and Deep Learning? 

Here is a solid plan to do so.

Enroll in The Data Science & Machine Learning Bootcamp in Python to start learning now. 
(use the offer code KDN0 to get 100% off)

 

Python for Data Science

 
Python is the most popular language in Data Science, Machine Learning, and Deep Learning. It’s fairly easy to understand. So I’d suggest that you start by familiarizing yourself with the language. The most important concepts to understand are data structures and Python functions.

 

NumPy for Numerical Computation

 
Once you have Python under your belt, you can continue learning NumPy. NumPy is a package used for numerical computations such as mean average etc. Most other data science packages are also built on top of it, so this is a must-have skill. The most fundamental item to understand here is NumPy arrays and operations on them.

>>  Enroll to start learning NumPy

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Data Manipulation with Pandas

 
Next, you need a tool for data cleaning and manipulation. Pandas is a tool built on top of NumPy that does just that. Since most data is usually not clean, this is a tool you must have in your arsenal. The most important thing to understand here is Pandas DataFrames and how to manipulate them.

>>  Click to start manipulating data with Pandas

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Visualization with Matplotlib

 
Now that you can clean and manipulate your data, it would be nice to share your analyses visually. Enter Matplotlib. This is a tool you can use to visualize the results of your data analysis. You can use it to visualize categories, distributions, and relationships — to mention a few.

>>  Start visualizing data with Matplotlib 

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Beautiful Visualizations with Seaborn

 
Seaborn is built on top of Matplotlib and will give you better-looking visuals than Matplotlib. The tool is very easy to use. However, if you want to perform very complex visuals, you might have to revert to Matplotlib.

>>  Start visualizing data with Seaborn

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Interactive Visualizations with Plotly

 
Plotly allows you to build visuals that one can interact with. For example, you see more information when you hover over an area and the ability to zoom in and out of your visualizations. You can also do advanced visualizations, such as mapping using Mapbox, which is integrated with Plotly.

>>  Create beautiful visuals with Plotly

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Interactive Web Applications with Dash

 
Dash is a web framework that will enable you to build interactive data science applications using Plotly. Any visualization that’s done in Plotly can also be done in Dash. The tool provides all Html via tags via Dash HTML components. It provides advanced interactive visuals via the Dash Core Components. It works on the principle of Dash Callbacks. You will usually provide some Input and Output to these callbacks. Whenever the Input changes, Dash will fire up the callback functions. Amazing, right?

>>  Build data applications with Dash

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Data Science Applications with Streamlit

 
If you are looking for Dash alternatives, then Streamlit is the way to go. It is an open-source framework for data scientists and machine learning engineers to create beautiful, performant apps in only a few hours.

>>  Build data applications with Streamlit

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Build Dashboards with Power BI Desktop

 
If you don’t want to write code, you could probably look to Power BI Desktop. You will still need to find a way to clean your data before importing it into Power BI Desktop; I can’t think of a better way to do that than to use Python.

>>  Start building dashboards with PowerBi

(use the offer code KDN0 to get 100% off)

 

Machine Learning

 
The next thing you can do is dive into machine learning. Here you’ll learn the coolest skills for supervised and unsupervised machine learning. The open-source Scikit-Learn package will provide all the tools you need for this. Here your focus will be on understanding how data is prepared for machine learning models, data splitting, and evaluating your machine learning models.

>>  Enroll to start learning data science and machine learning 

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Association Rule Mining

 
This is a technique that is used to find the relationship between products. You will find it useful if you work in the retail space, such as in supermarkets. This skill can help you increase sales by identifying items frequently bought together. For example, in a Pharmacy, you might find that a patient who buys drug A also buys drug B. It only makes sense to ask patients who bought drug A if they also have drug B in their prescription. You have seen this technique used in online stores or learning platforms as a frequently bought-together section.

>>  Enroll to start learning data science and machine learning 

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Natural Language Processing

 
Nowadays, we have so much data in text form. This data can be obtained from blogs and social sites such as Twitter. The ability to make sense of that data is an indispensable skill in this century. However, handling text data is not a walk in the park. You have to find a way of representing the text data in a numerical form. The good news is that numerous tools that can enable you to do that do exist.

>>  Learn natural language processing

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Deep Learning

 
Once you have grasped the intricacies of machine learning, it’s now time for you to look into Deep Learning. This is just an implementation of machine learning that involves the application of neural networks. These networks mimic the working of the human brain. Deep Learning is majorly used when you have a large dataset, especially in NLP and computer vision problems. It is also widely used in compute-intensive processes such as image classification, image segmentation, and object detection. The most popular tools for this are TensorFlow, Keras, and PyTorch.

>>  Enroll to start learning data science and machine learning 

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Automated Machine Learning

 
This field is fairly new, but it’s sweeping the world like a storm. Imagine if you could feed your data to a model, and it would do everything for you. This includes data processing and tuning the model to obtain the best results. This is the promise of AutoML. Tools that can enable you to do that include AutoSklearn and AutoKeras.

>>  Enroll to start learning data science and machine learning 

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Final Thoughts

 
This gives you an idea of what your Data Science Learning Path would look like. This is just the tip of the iceberg. Once you learn these, you will be amazed at the plethora of data science tools and techniques you can still find online. The point, though, is to get started.

>> Click to start your data science journey. 

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