The reality of AI is transformation. AI is set to complement human capabilities, emphasizing the need for individuals to acquire relevant skills to thrive in this AI-integrated future. The influence of AI is only expected to escalate, bringing forth a variety of roles, such as machine learning engineers, business intelligence engineers, and research engineers, alongside opportunities in data analysis and data science powered by AI.
Top AI skills for dev teams
As AI becomes a predominant force in workplaces and daily routines, we have curated a list of AI skills that L&D must ensure are a part of their plan of upskilling tech teams in 2024.
Data science
Deep learning
Cloud computing
Machine learning
Programming languages
Natural language processing (NLP)
Data science
In this digital age, every industry is data-based. All day-to-day operations and long-term strategic planning of businesses involve the processing of large sets of data to extract deep insights into customer preferences. These insights serve as a foundation for AI systems to learn, adapt, and predict outcomes. Organizations focus on capitalizing on their data resources effectively so they are better able to enhance performance indicators and foster data-driven decision-making in their managerial approach. AI enhances data science techniques like exploratory data analysis and predictive modeling and paves the way for autonomous learning, pattern recognition, and decision-making in intelligent systems. For this reason, tech teams must be equipped with data science as one of the many AI skills.
Introduction to Data Science with Python
Python is one of the most popular programming languages for data science and analytics. It’s used across a wide range of industries. It’s easy to learn, highly flexible, and its various libraries can expand functionality to natively perform statistical functions and plotting. This course is a comprehensive introduction to statistical analysis using Python. You’ll start with a step-by-step guide to the fundamentals of programming in Python. You’ll learn to apply these functions to numerical data. You’ll first look at strings, lists, dictionaries, loops, functions, and data maps. After mastering these, you’ll take a deep dive through various Python libraries, including pandas, NumPy, Matplotlib, Seaborn, and Plotly. You’ll wrap up with guided projects to clean, analyze, and visualize unique datasets using these libraries. By the end of this course, you will be proficient in data science, including data management, analysis, and visualization.
Deep learning
Another in-demand AI skill for 2024 is deep learning. Inspired by the human brain’s neural networks, deep learning is a part of machine learning, offering a sophisticated method for machines to interpret complex data types such as images, audio, and text. With its multilayered neural networks, deep learning excels in processing unstructured data. This enables machines to perform tasks in a manner that closely resembles human cognitive abilities, such as learning independently and making decisions. Data scientists who are equipped with this skill develop deep-learning models that use sophisticated algorithms to serve as trained computer brains. This is why L&D teams must incorporate deep learning as one of the core AI skills under focus when planning upskilling training for their tech teams.
Machine learning
Machine learning (ML) is about teaching computers to learn from data and experiences. From voice assistants to algorithms in diagnostic machines, ML autonomously recognizes patterns, forecasts future events, and improves operations. Machine learning processes a wide range of data, from textual content to numerical data, without explicit instructions by developers. ML’s transformative power enables systems to seek patterns and insights within the data, which increases their precision and efficiency in switching to dynamic, data-informed decision-making. Consequently, there’s a growing demand for skilled tech professionals who can develop advanced ML algorithms capable of generating accurate predictions and extracting valuable insights from complex data sets. This is why L&D teams must incorporate training specific to ML in their upskilling plan for tech teams.
Machine Learning System Design
Machine Learning System Design is an important component of any ML interview. The ability to address problems, identify requirements, and discuss tradeoffs helps you stand out among hundreds of other candidates. Readers of this course able to get offers from Snapchat, Facebook, Coupang, Stitchfix and LinkedIn. This course will help you understand the state of the practice on model techniques along with best practices in applying ML models in production at scale. Once you finished the course you can learn more use-cases at: http://mlengineer.io/ Once you're done with the course, you will be able to apply and leverage knowledge from top researchers at tech companies. You will have up to date knowledge in model techniques from hundreds of the latest research and industry papers. There is even a chance that the interviewers will be surprised at the depth of your knowledge.
Programming languages
One of the most popular programming languages in the domain of AI is Python. With its simplicity, user-friendliness, and open-source nature, Python is considered a dynamic tool for tech teams. Python’s clear syntax and powerful libraries make it especially suited for the complex requirements of AI and DS applications. The language’s modularity and support for object-oriented programming (OOP) further amplify its effectiveness in developing intricate, data-driven solutions. With the help of Python, tech teams can handle a wide range of tasks, including web scraping, scientific computing, and data visualization. Mastery of Python will help tech teams handle complex data structures.
Programming in Python
Python is a multi-purpose language with an extensive range of application domains, including basic programming, web development, data science, and machine learning. In this course, you’ll learn Python's programming fundamentals and constructs. Each concept offers ample hands-on experience with executable programs. You’ll learn the basic concepts of data types and structures as well as conditional and iterative constructs. You’ll learn functions, functional programming, and recursion. The course introduces object-oriented programming, providing experience with its various modules. Finally, you’ll learn advanced topics such as containership, iterators, generators, exception and file handling, concurrency, parallelism, and synchronization. After taking this course, you’ll be able to undertake advanced programming courses with specialized application domains using Python. The course provides a strong foundation for data science and machine learning careers, enabling you to become an effective data scientist.
Natural language processing
As the volume of digital text grows, the necessity of process and interpreting extensive datasets also increases. This is where natural language processing (NLP) comes into play. NLP is an area in artificial intelligence that focuses on bridging the communication gap between humans and machines. It equips machines with the ability to break down and comprehend human language. Tackling tasks such as translating languages, summarizing texts, and unraveling the sentiments behind words are some of the key functions of NLP. For this reason, tech teams must be provided with relevant training to upskill them in the field of NLP. Tech teams need to specialize in developing advanced algorithms to process textual content and classify information for computers to better understand the nuances of language. At the heart of NLP is the goal of rendering machine interactions more seamless and more similar to human communication. The recent advancements in the field with the emergence of ChatGPT are a testament to how pivotal it is for tech teams to empower themselves with this AI skill so that conversations with computers become more intuitive and human-centric.
Performing NLP with R
This course will cover concepts in natural language processing (NLP). Developers will find this valuable because of the high demand for NLP skills. This includes understanding natural language when building applications like chatbots, sentiment analysis, search engines, and content recommendations. NLP also provides tools for data analysis, personalization, and content filtering. In this course, you will learn concepts of NLP, how to use the tm package, the use of corpora, how to convert text to structured data, advanced search techniques, metadata, and how to use quanteda, and tidytext. With this information, you can expect to command in-demand skills in many text-processing-related fields.
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