This course provides a comprehensive introduction to machine learning, covering essential algorithms, tools, and techniques used to build and deploy machine learning models. It is suitable for beginners and those looking to deepen their knowledge of machine learning.

Course Content:

1. Introduction to Machine Learning:

   • Overview of machine learning
   • Types of machine learning (supervised, unsupervised, reinforcement learning)
   • Key concepts and terminology

2. Python for Machine Learning:

   • Introduction to Python programming
   • Essential libraries (NumPy, pandas, Matplotlib)
   • Data manipulation and visualization

3. Data Preprocessing:

   • Data cleaning and handling missing values
   • Feature engineering and selection
   • Data normalization and scaling
   • Splitting data into training, validation, and test sets

4. Supervised Learning Algorithms:

   • Linear Regression
   • Logistic Regression
   • Decision Trees and Random Forests
   • K-Nearest Neighbors (KNN)
   • Support Vector Machines (SVM)
   • Model evaluation metrics (accuracy, precision, recall, F1 score)

5. Unsupervised Learning Algorithms:

   • Clustering (K-Means, Hierarchical Clustering, DBSCAN)
   • Dimensionality Reduction (PCA, LDA, t-SNE)Anomaly detection

6.Advanced Supervised Learning:

• • Ensemble methods (Bagging, Boosting, Stacking)
  • Gradient Boosting Machines (XGBoost, LightGBM)
  • Neural Networks and Deep Learning basics

7.Deep Learning:

• • Introduction to neural networks
  • Building neural networks with TensorFlow and Keras
  • Convolutional Neural Networks (CNNs) for image recognition
  • Recurrent Neural Networks (RNNs) for time series and text data