Machine learning, Data Science, and Python are the popular buzzwords and people often use them interchangeably. Though there is a significant difference between them. Let us have a look at these ones by one:
Data Science-
Data Science involves extracting meaningful information from both structured and unstructured data. A huge amount of volume, variety of data is being created at a high velocity. This data needs to be managed and analyzed by data scientists, as there a lot of data getting produced at a high speed, more and more data analysts are required to fill up the positions offering high salaries. Data Science is a broad discipline and useful to gaining insights. The skills required for Data Science include:
“ Statistics
“ Data visualization
“ Data mining and cleaning
“ Understanding programming languages, like Python
“ Data Science tools
Machine learning-
It is a subset of Data Science and deals with the idea that systems can learn from data. It is useful when the computer is trained to learn from data, without the need of the computer getting programmed. It is further used to make predictions and classify results with the help of algorithm statistics. The skills required for Machine Learning:
“ Statistical modelling
“ Data evaluation
“ Fundamentals of Data Science
“ Data architecture design
“ Techniques of text representation
Python-
Python is the best way to start your journey of programming languages. It is the best data science tool and also helpful in machine learning. It is easier to learn, comprehensible, and has various benefits of flexibility, scalability, libraries and frameworks, and automation.
Let us see:
The five beginner friendly steps to learn machine learning and data science with Python-
1. Learn Data Science tools, like Python, and Machine learning concepts-
Start by learning from the most basic as well as the most useful data science tool, Python programming language.
Practice utilizing data science tools like Jupyter and Anaconda while learning Python programming. Spend some time getting to know them, what they do, and why you should utilize them.
2. Learning data analysis, manipulation, and visualization with the help of data science tools-
Pandas, NumPy, and Matplotlib are the tools to be learned.
Pandas will assist you in working with data frames, which are tables of data similar to those found in an Excel file. This works with structured data.
NumPy is a Python package that allows you to execute numerical operations on your data. Machine learning converts whatever you can think of into numbers, then looks for patterns in them.
Matplotlib will assist you in creating graphs and data visualizations. Visualizing your findings is an important component of sharing your findings as they can be more appealing than the data in tabular form.
3. Learn machine learning with python library toolkit-
Scikit-learn is a
Python library that includes a number of useful
machine learning algorithms that are ready to use.
Try to learn the machine learning problems including, like classification and regression, algorithms used for the same. Learn trying to apply the algorithms.
4. Learn deep learning neural networks-
If the data is unstructured then deep learning and neural networks work the best.
For structured data, you’ll want to use random forests or an algorithm like XG Boost, and for unstructured data, you’ll want to utilize deep learning or transfer learning (taking a pre-trained neural network and applying it to your problem).
5. Learn with small projects-
In order to apply for a job, learn from the small projects about the tasks that the job requires you to command. This can be possible when you have the knowledge and apply the same at projects of professional significance.
Where to learn these skills:
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