1. Introduction to Artificial Intelligence (AI)
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Definition: AI is a branch of computer science focused on creating systems capable of performing tasks that typically require human intelligence, such as recognizing patterns, making decisions, and understanding natural language.
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History: The concept dates back to the 1950s with Alan Turing’s “Turing Test” to determine if a machine can exhibit intelligent behavior indistinguishable from a human . The term “Artificial Intelligence” was coined by John McCarthy in 1956.
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Types of AI:
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Weak AI (Narrow AI): Designed for specific tasks (e.g., Siri, Alexa, self-driving cars). It does not possess genuine intelligence or self-awareness.
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Strong AI (Artificial General Intelligence): Hypothetical AI that equals human intelligence, possessing consciousness and the ability to solve problems and plan for the future.
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Artificial Super Intelligence: A future stage where AI surpasses human capabilities .
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2. Machine Learning (ML)
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Definition: A subset of AI that provides systems the ability to learn and improve from experience without being explicitly programmed.
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Types of Learning:
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Supervised Learning: Training the model using labeled data (e.g., teaching a computer to recognize cats by showing it images labeled “cat”) . Algorithms include Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines (SVM).
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Unsupervised Learning: Training on unlabeled data where the model finds patterns and structures on its own (e.g., customer segmentation) . Algorithms include K-Means Clustering.
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Reinforcement Learning: An agent learns to make decisions by interacting with an environment and receiving rewards or penalties (e.g., training a game bot). Algorithms include Q-Learning.
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ML Process: Typically involves 7 steps: Defining the objective, Data Gathering, Data Preparation (cleaning), Exploratory Data Analysis (EDA), Building the Model, Model Evaluation, and Predictions.
3. Deep Learning (DL)
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Definition: A specialized subset of ML inspired by the structure and function of the human brain (artificial neural networks). It is particularly effective for high-dimensional data like images and speech.
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Neural Networks: Composed of layers—an input layer, hidden layers (where processing happens), and an output layer. Deep learning models have multiple hidden layers.
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Key Concepts:
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Perceptron: A single artificial neuron.
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Backpropagation: The method used to train neural networks by calculating errors and updating weights to minimize loss .
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Convolutional Neural Networks (CNNs): Primarily used for image processing and recognition tasks .
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Recurrent Neural Networks (RNNs) & LSTMs: Used for sequential data like time series or natural language, capable of “remembering” previous inputs.
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4. Natural Language Processing (NLP)
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Definition: A field of AI focused on the interaction between computers and human language. It involves analyzing, understanding, and generating natural language.
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Applications: Chatbots, sentiment analysis, machine translation, and text summarization.
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Techniques: Tokenization (breaking text into words), Stemming/Lemmatization (reducing words to their root form), and Stop Word removal.
5. Generative AI & GANs
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Generative Adversarial Networks (GANs): A framework where two neural networks (a generator and a discriminator) compete. The generator creates fake data, and the discriminator tries to distinguish it from real data. This is used for generating realistic images, videos, and art.
6. Practical Demos (Python)
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Weather Prediction: A walkthrough of building a classification model (Logistic Regression, Random Forest, etc.) to predict if it will rain tomorrow using a dataset from Kaggle.
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Titanic Survival Prediction: Analyzing the Titanic dataset to predict passenger survival based on factors like class, sex, and age .
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Credit Card Fraud Detection: Using deep learning to construct a model that identifies fraudulent transactions .
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Name Entity Prediction: Implementing an LSTM model to predict the gender of a name .
7. Tools & Libraries
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Python: The primary language used due to its simplicity and vast library support .
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Libraries: The course covers essential libraries like TensorFlow (for deep learning), Scikit-Learn (for machine learning algorithms), NumPy (for numerical computation), Pandas (for data manipulation), Keras (high-level neural networks API), and Matplotlib/Seaborn (for visualization).