When it comes to Machine Learning, you will need help from other professionals. And the best way to network with these professionals is through meetups. Here is the list of top machine learning meetups in Hyderabad:
- Machine Learning. Do it right.
- IoT + Machine Learning + Python
- Hyderabad Machine Learning Foundation meetup
- Hyderabad Machine Learning
- Deep Learning Hyderabad
The best recommendation to get started on Machine Learning as a beginner includes a 5 step process, which goes as follows:
Step 1: Adjust Mindset. Firstly you need to have patience and believe you can practice and apply machine learning. You need to figure out answers for some questions like the ones listed, to clear the doubts in your mind:
- What is holding you back from your Machine Learning goals?
- Why Machine Learning does not have to be so hard
Machine Learning is a subset of Artificial Intelligence, and it provides systems with the ability to automatically learn, perform and improve upon set tasks, without requiring reprogramming or human intervention in any form. Therefore, you need a firm understanding in statistics. The type of machine learning methods that you need will derive the relationship between the inputs and outputs in your historical data.This constitution allows you to understand what that real underlying yet unknown mapping function might look like and how factors like noise, corruption, and sampling of your historical data may impact approximations of this mapping made by different modeling methods. Therefore, you need to change the way you think and thus your approach to any problem.
Step 2: Pick a Process: Use a systemic process to work through problems like the one given:
- Define the Problem: Try to visualize the problem from different perspectives. You need to analyze and understand the problem thoroughly. You must clarify the answers to the following:
- What is the problem?
- Why does the problem need to be solved?
- How would I solve the problem?
- Prepare Data: You can start with data preparation with a data analysis phase that involves summarizing the attributes and visualizing them using scatter plots and histograms, or any other method that you are comfortable with. You should describe in detail each attribute and relationships between attributes. The actual process can be broken down into the following:
- Data selection
- Data preprocessing
- Data transformation
- Spot Check Algorithms: You can spot check algorithms, which means loading up a bunch of standard machine learning algorithms into your test harness and performing a formal experiment. You can typically run 10-20 standard algorithms from all the major algorithm families across all the transformed and scaled versions of the dataset that is available to you.
- Improve Results: You can do this by an automated sensitivity analysis on the parameters of the top performing algorithms. The process of improving results involves the following:
- Algorithm tuning
- Ensemble methods
- Extreme feature engineering
- Present Results: The results of a complex machine learning problem should be put to work. In industry, it means a presentation to stakeholders. Even if it is a competition or a problem you are working on for yourself, it is good to go through presentation. It gives you checkpoints and helps you to summarize it.
Step 3: Pick a Tool. Choose a tool based on your level of understanding and knowledge and put it in action. Here is a sample of levels:
- Beginners: Weka Workbench.
- Intermediate: Python Ecosystem.
- Advanced: R Platform.
Step 4: Practice on Datasets: Data Science projects are a great way to boost up your knowledge. You get a practical edge to the problems and their solutions, you can decipher how data science contributes in real time. It not only lets you apply your knowledge but also boost up your CV, hence increases your chances of getting hired. There are lots of datasets available online. Some of the websites where you can find free data sets are as follows:
- Socrata OpenData
- AWS Public Data sets
- Google Public Data sets
- UCI Machine Learning Repository
Step 5: Build a Portfolio: You should demonstrate your results and solutions in a simplified way.