Popular Deep Learning Projects based on Python Programming

Python Programming

Python programming is one of the best ways to build an Artificial intelligence (AI) product. In the growing list of AI applications, Deep Learning with Python is probably the most enticing specialization you could ever aspire to do in 2022. Python based platforms are enabling users to develop many new types of deep learning applications for various businesses of which Natural Language Processing (NLP), Neural Machine Translation (NMT), and voice AI are garnering huge investments. If you are pursuing Python certification, the deep learning industry is the best field for you to progress forward.

In this article, we have highlighted the top deep learning applications where Python based programming is widely used and with superior results.

1- Multi language speech to text translator

Language translation models are part of the growing family of machine learning applications. Such apps are licensed deep learning products that allow users to translate voice or text inputs into other languages. It could be achieved by developing real time interactive dashboards and speech to text compilers, extractors, and translators. Using Python with deep learning algorithms, translators are able to switch from one language to another with ease. 

Most apps have a limitation of translation to maybe 2 or 3 languages – but deep learning libraries using Python could allow translation to 200+ different languages. These are also used to translate coded languages, symbols, and other non-text families of languages.

2 – Marketing analytics dashboards

Deep learning applications are widely used in business analytics solutions for Marketing, Sales, and Finance. In the last 10 years, the scope of marketing intelligence and marketing analysis has grown immensely and the decent volume of research in Python deep learning projects has enabled marketing teams to prepare for the matured chatbot and virtual assistants technologies. In the modern marketing teams, bots and automation are used to extract a massive amount of customer data collected from varied sources to create what we call a 360 degrees customer profile. This helps to segment different customer profiles into groups for better interaction. 

The marketing analytics dashboards are mostly in self service modes where marketing teams themselves are creating dashboards for their business analysis without the help of data scientists, analysts, or engineers. If everything is taken into account in deep learning science, the marketing space would be dominated by Python certified Marketers who work with advanced data analytics and data visualization for better business outcomes.

Digital Payments Interface

Digital payments are a pet topic in the financial services technology market. It is also referred to as the Fintech space where digital payments and crypto markets are widely discussed. In the recent times, the rise of AI with automation and blockchain for security has taken the fintech space by surprise. 

To extract relevant insights on the trends in fintech, analysts have to rely on advanced techniques in deep learning to create new age platforms and solutions. In the last 2 years, the market for digital payments is using all kinds of neural based machine learning tools, reinforcement learning, and supervised learning in computer-based facial recognition, and biometric analysis. 

The era of digital payments and deep learning with Python make a formidable combination. If you are eager to work in AI and Finance markets, a deep learning course with Python programming is your best option.

AI-based loan and insurance processing and customer support

Insurance is a high risk business model. There are structured and unstructured loans and insurance models that are traditionally considered to be very complex. In our previous point, we just spoke about the fintech market for digital payment apps. 

Insurance companies are able to scale their businesses by up to 40% by bringing in Python DL tools. 

A similar revolution is happening in the loans segment too where agents are relying on deep learning applications for the extraction of text and voice based analysis. Insurance companies now tap the insights from chatbot conversations to understand how certain words and sentiments attract customers to invest in insurance products and other nuances that offend prospects and first time inquiry makers. Personalization with chatbots is only half the job done – the real work begins when chatbot insights are passed to the human agents who function based on predictive analysis of past conversations and historical transactions.

Using Python sentiment analysis, fintech agents and AI engineers are able to develop a range of customized product portfolios that would enable agents to make a solid sales pitch and get the business from individual and corporate customers. 

By Michael Caine

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