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AI/ML & Data Analytics

Artificial Intelligence (AI), Machine Learning (ML) and Data Analytics, are
closely related fields that are transforming various industries.

Artificial Intelligence (AI)

Definition

AI is the simulation of human intelligence in machines that are programmed to think and learn like humans.

Key Technologies

Includes neural networks, deep learning, reinforcement learning, and expert systems.

Applications

AI is used in various applications, including natural language processing (e.g., chatbots like me!), computer vision (e.g., facial recognition), robotics, and autonomous systems.

Machine Learning (ML)

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Definition

ML is a subset of AI that involves training algorithms to learn from and make predictions or decisions based on data.

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Applications

Includes fraud detection, Recommendation systems, predictive analytics, and speech recognition.

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Types of ML

Supervised Learning : The algorithm is trained on labelled data (e.g., image classification).

Unsupervised Learning : The algorithm identifies patterns in unlabelled data (e.g., clustering).

Reinforcement Learning : The algorithm learns by interacting with an environment and receiving feedback (e.g., game playing).

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Applications1

Includes fraud detection, Recommendation systems, predictive analytics, and speech recognition.

Definition

Data Analytics involves analysing raw data to uncover patterns, correlations, and insights.

Types of Data Analytics

Descriptive Analytics : Summarizes past data to understand what happened (e.g., sales reports).

Diagnostic Analytics : Examines data to understand why something happened (e.g., root cause analysis).

Predictive Analytics : Uses historical data to predict future outcomes (e.g., forecasting).

Prescriptive Analytics : Suggests actions based on data analysis (e.g., optimization).

Tools and Technologies

Common tools include SQL, Excel, Tableau, Power BI, Python, and R.

AI and ML Algorithms: Data analytics provides the data that AI and ML models use to learn and make predictions.

Data-Driven Decision Making: AI and ML enhance data analytics by providing advanced methods for analysing large and complex data sets, leading to more accurate and actionable insights.

Oracle VM Virtual Box: A free and open-source hypervisor for running VMs on various operating systems.

Automation: AI and ML can automate data analysis tasks, making the process faster and more efficient.

Healthcare : AI and ML are used to analyse patient data, predict disease outbreaks, and personalize treatments.

Finance: Predictive analytics powered by ML models helps in credit scoring, fraud detection, and investment strategies.

Retail: Data analytics and AI are used for customer segmentation, inventory management, and personalized marketing.

Manufacturing: AI-driven analytics improve quality control, predictive maintenance, and supply chain optimization.

These Technologies are revolutionizing how businesses operate, making processes
more efficient and enabling data-driven decision-making.