Machine Learning with Python Certification

Objectives

• Understand the fundamental concepts of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
• Develop proficiency in Python programming for machine learning, including data manipulation, visualization, and model implementation using popular libraries such as NumPy, Pandas, and Scikit-learn.
• Learn various machine learning algorithms, such as linear regression, logistic regression, decision trees, random forests, support vector machines, and k-nearest neighbors.
• Gain knowledge of model evaluation techniques, including cross-validation, performance metrics (e.g., accuracy, precision, recall), and learning curves, to assess the performance of machine learning models.
• Understand the concept of feature engineering and learn techniques to preprocess, transform, and select features for machine learning models.
• Explore dimensionality reduction techniques, such as principal component analysis (PCA) and t-SNE, to handle high-dimensional data and improve model performance.
• Gain hands-on experience with model training, tuning, and optimization to achieve the best possible performance from machine learning models.
• Learn how to handle common challenges in machine learning, such as overfitting, underfitting, and dealing with imbalanced datasets.