Artificial Intelligence

"Unleash the Power of AI: Transforming Data into Intelligence."

"Dive into the forefront of technology with our AI course. Explore the foundations of artificial intelligence, including machine learning and deep learning. Learn to develop AI models, algorithms, and applications using Python and popular frameworks like TensorFlow and PyTorch. Discover real-world applications in natural language processing, computer vision, and reinforcement learning. Gain hands-on experience through projects and labs, preparing you to leverage AI for innovation and problem-solving in diverse industries."

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Course Overview

📗Introduction to AI:
Understand the fundamentals, history, and applications of artificial intelligence.
📗Machine Learning Basics:
Learn about algorithms, supervised and unsupervised learning, and model evaluation techniques.
📗Deep Learning:
Dive into neural networks, convolutional and recurrent neural networks, and applications like image recognition and natural language processing.
📗AI Ethics and Bias:
Explore ethical considerations, fairness, transparency, and accountability in AI systems.
📗Practical Projects:
Apply AI techniques to solve real-world problems through hands-on projects and case studies.

>Introduction to AI
>Software setup

>Basics
>Definitions
>Print
>Variables
>Loop
>Basic Function

>List
>Tuple
>Set
>Dictionary

>Arithmetic operators
>Assignment operators
>Comparison operators
>Logical operators
>Identity operators
>Membership operators

>Mean, Standard Deviation
>Variance -Implementation.
>Descriptive and Inferential Statistics
>Probability Theory and Distribution.
>Sampling Distribution,
>Linear Regression.
>Sample Error and True Error.
>Bias Vs Variance and Its Trade Off
>Hypothesis Testing
>Confidence Intervals.
>Correlation and Covariance
>Correlation Coefficient.

>Covariance Matrix
>Pearson Correlation
>Spearman’s Rank Correlation Measure
>Kendall Rank Correlation Measure
>Robust Correlations

>curtosis
>Empirical rule

>major projects

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