Python for Data Science & Machine Learning

What Will You Learn?

• Understand the fundamental concepts of machine learning and its applications in real-world scenarios.
• Learn to implement supervised learning algorithms such as linear regression, logistic regression, decision trees, and random forests.
• Explore unsupervised learning algorithms like K-Means clustering and PCA (Principal Component Analysis) for dimensionality reduction.
• Gain hands-on experience in data preprocessing, including cleaning, feature scaling, and encoding using Python libraries such as Pandas, NumPy, and Scikit-learn.
• Build and evaluate machine learning models, using metrics like accuracy, precision, recall, and F1-score.
• Learn advanced techniques such as cross-validation, hyperparameter tuning (with GridSearchCV and RandomSearchCV), and model optimization.
• Understand the basics of deep learning and implement simple neural networks using TensorFlow and Keras.
• Complete an end-to-end machine learning project, from data loading and preprocessing to model evaluation.