Machine Learning

Introduction to Machine Learning: Understand the basics of machine learning, its history, types of learning (supervised, unsupervised, and reinforcement learning), and the key algorithms used in ML.

Data Preprocessing: Learn how to clean, transform, and preprocess raw data into a format suitable for training machine learning models, including techniques for handling missing data, outliers, and feature scaling.

Supervised Learning Algorithms: Dive into popular supervised learning algorithms like Linear Regression, Logistic Regression, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN) for classification and regression tasks.

Unsupervised Learning Algorithms: Learn about unsupervised learning techniques, including clustering (e.g., k-Means) and dimensionality reduction (e.g., PCA), to discover hidden patterns in data without labeled output.

Model Evaluation and Validation: Understand how to evaluate and validate machine learning models using techniques like cross-validation, ROC curves, precision, recall, and F1 score, ensuring your models are robust and reliable.

Ensemble Methods: Explore ensemble learning techniques like Random Forests, Gradient Boosting, and AdaBoost, which combine multiple models to improve prediction accuracy and reduce overfitting.

Neural Networks and Deep Learning: Learn the basics of neural networks and how they form the foundation for deep learning. Understand concepts like feedforward networks, backpropagation, and activation functions.

Natural Language Processing (NLP): Gain skills in processing and analyzing text data using machine learning, including techniques like text classification, sentiment analysis, and word embeddings (Word2Vec, GloVe).

Model Tuning and Optimization: Master the techniques for improving model performance, including hyperparameter tuning (Grid Search, Random Search), feature engineering, and regularization methods (L1, L2).

Reinforcement Learning: Explore the concept of reinforcement learning, where agents learn to make decisions by interacting with an environment, and its applications in robotics, gaming, and autonomous systems.

Machine Learning Tools and Frameworks: Get hands-on experience with popular ML libraries and frameworks such as Scikit-learn, TensorFlow, Keras, and PyTorch for building and training machine learning models.

Deploying Machine Learning Models: Learn how to deploy machine learning models to production environments, including model integration with web applications, APIs, and cloud platforms (AWS, Azure, Google Cloud).