Machine Learning with Python

Overview of course :

Discover the power of Machine Learning with Python! This course introduces you to essential concepts, algorithms, and practical applications. Learn to build predictive models, process data, and implement machine learning techniques using Python libraries like scikit-learn, Pandas, and NumPy. Perfect for beginners and professionals, this course equips you with the skills to tackle real-world problems and advance your career in AI and data science

Course Details: 

Course Curriculum: Machine Learning with Python

Module 1: Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
• Applications and Real-World Use Cases
• Setting up Python Environment for ML

Module 2: Data Preprocessing

• Data Cleaning and Handling Missing Values
• Data Normalization and Standardization
• Feature Selection and Engineering
• Introduction to Pandas and NumPy

Module 3: Supervised Learning

• Linear Regression and Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• Performance Metrics: Accuracy, Precision, Recall, F1-Score

Module 4: Unsupervised Learning

• Clustering Techniques (K-Means, Hierarchical Clustering)
• Dimensionality Reduction (PCA)
• Anomaly Detection

Module 5: Neural Networks and Deep Learning (Basics)

• Introduction to Neural Networks
• Building Simple Neural Networks with TensorFlow/Keras

Module 6: Working with Real-World Data

• Importing and Analyzing Datasets
• Practical Applications in Finance, Healthcare, and E-commerce

Module 7: Model Evaluation and Optimization

• Cross-Validation and Hyperparameter Tuning
• Avoiding Overfitting and Underfitting
• Grid Search and Random Search

Module 8: Deployment and Projects

• Exporting Models for Production
• Case Studies and Capstone Project