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Microsoft Azure: Connecting solutions to reality

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This Article will be focussed on a Cloud Computing player that is quite helpful in setting data landscape and its solutions in production: Microsoft Azure Before we Continue our journey into creating some real solutions, let's look at cloud computing where you can develop and set up a data landscape easily. In real solutions, we deal with millions and sometimes billions of records of data which requires large computing resources that are not easily available on a PC or computer. For that, you might need a RAM of 56GB which will cost a lot but instead, you can avail the resource in the cloud at a cheaper cost. Even if you want to automate the process of inference, Then you can set up the pipeline in the cloud for doing so which makes it an important part of the Data Landscape. Today we will dive into one of the players in cloud space that can be used in doing so. Microsoft Azure In the ever-changing environment of cloud computing, Microsoft Azure emerges as a strong force, enabling
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GenAI: A new form of intelligence

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Let's bring our discussion to one of the hot topics in today's world: GenAI or Generative AI, an AI that looks like General Artificial Intelligence. Can you tell which one of the above images is AI-generated or human-generated? If you think one of these is human paintings, then correct yourself as these are AI-generated using GenAI. In the vast world of artificial intelligence, generative AI emerges as an intriguing and inventive branch that pushes the limits of what machines can produce. This AI paradigm extends beyond standard problem-solving and into the world of creative generation, producing output ranging from art to music. Let's go on a trip to uncover the mysteries of generative AI and investigate its disruptive impact across multiple fields. Generative AI follows two components:   1. Creative Content Creation: Generative AI focuses on the generation of unique material, such as images and writing, music, and even complete virtual worlds. It taps into the world of cr
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Location: Adelaide SA, Australia
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Re-Inforcement learning: Robotic Gym for Dashing ML Models

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Today let's look at Robotic Gym where you can train AI like trial and error methods. This technique is Reinforcement learning which trains an AI using an action and penalty mechanism. Mastering Decisions Reinforcement learning is a shining example of autonomous decision-making in machine learning, emulating human and animal learning processes. This paradigm, founded on the ideas of rewards and consequences, propels robots into the realm of adaptive and strategic behavior. Let's begin on a trip to unravel reinforcement learning and investigate its real-world applications. Reinforcement learning contains 2 Major parts, 1. The Agent in Action: Reinforcement learning revolves around an agent, which is tasked with making a series of decisions to achieve a set of objectives. These decisions are influenced by feedback from the environment. 2. Reward-Based Learning: Reinforcement learning, unlike supervised learning, which uses explicit labels to guide the model, is based on a reward s
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Location: Adelaide SA, Australia
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Semi-Supervised Learning: linking two parallel universes of Machine learning

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Let's find out a middle way out of 2 parallel universes of machine learning. Semi-supervised learning, A technique combining two important roads of AI. In the enormous universe of machine learning, semi-supervised learning emerges as a refined strategy for bridging the labeled and unlabeled data gaps. Striking a careful balance, this methodology provides an appealing alternative when labeled datasets are rare. Let's look at understanding the complexities of semi-supervised learning and its real-world applications using a realistic examples. For Semi-Supervised learning there are two major considerations: 1. The Middle Ground: Semi-supervised learning operates in the gray area between fully labeled and completely unlabeled datasets. It leverages the power of a limited number of labeled examples while making the most of the vast pool of unlabeled data. 2. The Scarcity Challenge: In many real-world scenarios, obtaining labeled data for training machine learning models can be a cos
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Location: Adelaide SA, Australia
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Non-Supervised learning: An Unmapped treasure in data kingdom

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Today, let's focus on Unsupervised learning, a machine learning type involving unlabeled datasets. Unsupervised learning emerges as an exciting paradigm in the large domain of machine learning, providing a one-of-a-kind technique for detecting patterns and structures within data without explicit direction. Unlike its supervised counterpart, unsupervised learning accepts the challenge of exploring the unknown, making it a versatile and effective tool in the field of artificial intelligence. Think of this technique as searching for the right color of a T-shirt in a big mall where you end up selecting the best of the good ones even if your favorite color is not available. Just like that, this ends up selecting the best one in the availability. Let's look at the two major problems of unsupervised learning: 1. The Absence of Labeled Guidance: Unsupervised learning operates without the luxury of labeled training data. Instead, it ventures into the data landscape, seeking to identify
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Location: Adelaide SA, Australia
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Supervised Machine learning: Baby feeding to ML models

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We discussed what machine learning is and different types of Machine learning problems. Today, let's focus on Supervised learning, a type of machine learning which involves labeled datasets. In the wide world of machine learning, one light stands out, illuminating the route to predictive precision: supervised learning. This technique, similar to that of a skilled mentor helping a student, serves as the foundation for many artificial intelligence applications. This Technique is like a baby feeding all the possible data for a particular category and then asking to answer new data. Because of the efficiency of this method, 🌐💡 this powerful concept has become the backbone of countless real-world applications, helping computers to evaluate large datasets while also drawing significant insights and predictions. Let's go on a voyage to decipher the complexities of supervised machine learning and discover its real-world applications. Understanding Supervised Machine Learning: The Men
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Location: Adelaide SA, Australia
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Exploratory Data Analysis: Basics for data

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The first and foremost step in the Creation of the Machine learning model after data source extraction by Data engineers is Data exploration. Exploring data in the input data sources. Tell me one thing what do you do when you buy a product? Won't you check the Quality of it? What about the quantity of it? What about its components? You will check everything about it, Right? This process of checking Quality in Data Science is Exploratory Data Analysis.  Exploratory Data Analysis (EDA) is the compass guiding us through the vast seas of raw data. It's not just about crunching numbers; it's an art of unraveling stories, patterns, and insights hidden within datasets. Let's embark on a journey into the world of EDA. This is the preliminary phase of data analysis that focuses on summarizing the main characteristics of a dataset. Its primary goal is to understand the structure, relationships, and key features of the data before diving into complex modeling or hypothesis testing
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Location: Adelaide SA, Australia
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