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MODULE-I: ADVANCED MACHINE LEARNING
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* Introduction to Advanced ML: A study of sophisticated algorithms and architectures (Ensembles, Boosting, Kernels) that solve complex high-dimensional patterns.
* Random Forest (Concept & Working): An ensemble (Bagging) method that constructs multiple Decision Trees and uses "Majority Voting" for classification.
* Random Forest (Implementation): Building a model using Scikit-Learn's 'RandomForestClassifier' to handle large datasets with high variance.
* SVM (Theory & Types): A supervised model finding the optimal hyperplane; Types include Linear SVM (Hard/Soft Margin) and Non-Linear/Kernel SVM (RBF, Polynomial).
* SVM (Implementation): Utilizing 'SVC' from sklearn with specific kernels to classify linearly non-separable data points effectively.
* XGBoost (Concept & Working): An advanced Gradient Boosting implementation using "Parallel Processing" and "Tree Pruning" for extreme speed and accuracy.
* XGBoost (Implementation): Implementing the 'XGBClassifier' to handle missing values automatically and improve performance on tabular data.
* PCA (Concept & Working): An unsupervised technique for dimensionality reduction that identifies "Principal Components" to preserve maximum variance.
* PCA (Implementation): Using 'PCA' from sklearn to compress features while retaining the core information of the original dataset.
* Evaluation Metrics: Measures to judge performance, primarily: Accuracy (Overall correct), Precision (False Positive reduction), and Recall (False Negative reduction).
* AUC-ROC: A graph showing performance at all classification thresholds; ROC is the curve (TPR vs FPR) and AUC is the Area Under that curve.
* Hyperparameter Tuning: Optimizing settings like 'n_estimators', 'max_depth', and 'learning_rate' using techniques like GridSearch or RandomSearch.

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MODULE-II: NATURAL LANGUAGE PROCESSING (NLP)
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* Intro to NLP: Enabling machines to process human language using libraries like NLTK and SpaCy for tasks like translation and summarization.
* Text Preprocessing: Essential cleaning steps including Tokenization (splitting into words), Stop-word removal, and Noise cleaning (regex).
* Lemmatization & Stemming: Stemming (cuts words crudely - PorterStemmer) vs Lemmatization (returns dictionary root - WordNetLemmatizer).
* Vectorization: Converting text to numbers; Types include BoW (Bag of Words), TF-IDF (Term Frequency-Inverse Document Frequency), and N-grams.
* Word2Vec: A deep learning-based embedding technique including 'Skip-gram' and 'CBOW' (Continuous Bag of Words) to capture semantic meanings.
* GloVe (Global Vectors): An unsupervised algorithm that leverages global co-occurrence statistics of words to represent them in vector space.
* Sentiment Basics: Identifying emotional polarity (Positive, Negative, Neutral) in text data using rule-based or machine learning approaches.
* ANN for NLP: Using simple Artificial Neural Networks with input embeddings to handle text classification and sequence modeling tasks.
* Sentiment Implementation: Practical project for classifying reviews or tweets as Positive or Negative using trained ML models.
* NER (Named Entity Recognition): Detecting and classifying 'entities' like PERSON, ORGANIZATION, GPE (location), and DATE in raw text.
* POS Tagging: Identifying the grammatical category of words (NN-Noun, VB-Verb, JJ-Adjective) to understand sentence structure.

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MODULE-III: DATA PREPROCESSING & TIME SERIES
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* Intro to Preprocessing: The phase of cleaning data (Handling Outliers, Scaling) to make it suitable for training machine learning models.
* Handling Large Data: Techniques like Batch Processing, Dask, or Chunking to work with files that exceed the system's RAM capacity.
* Feature Engineering: The process of creating new features (e.g., extracting "Year" from "Date") to help the model learn better patterns.
* Advanced Features: Deep feature synthesis and interaction terms that capture complex relationships between multiple variables.
* SMOTE (Synthetic Minority Over-sampling Technique): Generating "synthetic" examples for the minority class to fix highly imbalanced datasets.
* Sampling Techniques: Methods used to balance datasets; Types include Oversampling (increasing minority) and Undersampling (decreasing majority).
* Time Series Basics: Analyzing data indexed by time to forecast future values based on past historical patterns.
* Trend & Seasonality: Trend (long-term upward/downward movement) and Seasonality (regular cyclic patterns like sales peaks in December).
* Missing Data: Identifying null values; handled by Deletion (dropping rows) or Imputation (filling with Mean/Median/Mode).
* Predictive Imputation: Using algorithms like KNN or MICE to predict and fill in missing values based on other features in the data.

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MODULE-IV: AI FUNDAMENTALS & APPLICATIONS
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* Intro to AI: The simulation of human intelligence by software; broadly categorized into "Applied AI" and "General AI" research.
* Types of AI: (1) Narrow/Weak AI (ANI), (2) General/Strong AI (AGI), and (3) Super AI (ASI) based on intelligence levels.
* History of AI: Evolved from early expert systems and logic (1950s) to Machine Learning (1980s) and Deep Learning (2010s).
* AI Applications: Extensive use in Speech Recognition (Siri/Alexa), Recommendation systems (Netflix), and Medical diagnosis.
* ML vs DL: Machine Learning (needs manual feature extraction) vs Deep Learning (uses Neural Networks to learn features automatically).
* NLP & CV: Representing the two pillars of perception; NLP (understanding text/speech) and Computer Vision (understanding images/video).
* Case Studies: Deep dive into real-world deployments like Google Search ranking, IBM Watson, or Autonomous driving systems.

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MODULE-V: APIS & DATA INTEGRATION
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* Intro to APIs: Interfaces allowing apps to talk; REST (Representational State Transfer) is the most common architectural style.
* API Requests: The basic HTTP methods used to interact with APIs: GET (receive), POST (send), PUT (update), and DELETE.
* Fetching Data: Using the 'requests' library in Python to call an API and retrieve data in JSON (JavaScript Object Notation) format.
* Integrating APIs: The steps to connect a front-end UI to a back-end AI model or database via secure API endpoints.
* Real-time AI: Processing live data streams (e.g., stock prices or social media feeds) to provide instant AI-driven analytics.
