Introduction to Machine Learning
o What is Machine Learning?
o Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
o Applications of Machine Learning
o Basics of AI and Data Science
Python for Machine Learning
o Setting up Python Environment (Jupyter Notebook, Google Colab)
o Introduction to NumPy & Pandas (Data Handling)
o Matplotlib & Seaborn (Data Visualization)
o Scikit-learn Basics
Data Preprocessing & Feature Engineering
o Handling Missing Data
o Encoding Categorical Variables
o Feature Scaling (Normalization & Standardization)
o Feature Selection Techniques
Supervised Learning Algorithms
o Linear Regression
o Logistic Regression
o Decision Trees
o Random Forest
o Support Vector Machines (SVM)
o k-Nearest Neighbors (KNN)
Model Evaluation & Optimization
o Train-Test Split & Cross-Validation
o Metrics (Accuracy, Precision, Recall, F1 Score, ROC-AUC)
o Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV)
Unsupervised Learning Algorithms
o Clustering (K-Means, Hierarchical Clustering, DBSCAN)
o Dimensionality Reduction (PCA, t-SNE)
Neural Networks & Deep Learning (Intro)
o Basics of Artificial Neural Networks (ANN)
o Introduction to TensorFlow & Keras
o Building a Simple Neural Network
o Training, Validation, and Overfitting
Natural Language Processing (NLP)
o Tokenization & Text Preprocessing
o Sentiment Analysis
o Word Embeddings (Word2Vec, GloVe)
Practical Applications & Projects
o House Price Prediction (Regression)
o Spam Email Classification (Classification)
o Customer Segmentation (Clustering)
o Handwritten Digit Recognition (Deep Learning)
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