What Will You Learn?

• Python Programming for Data Science: Solidify your Python skills for data science tasks.
• Machine Learning Algorithms: Understand and implement regression, classification, and clustering algorithms using Scikit-learn.
• Prepare data for machine learning models by joining tables, aggregating rows, and applying feature engineering techniques
• Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
• Data Preprocessing Techniques: Master essential data cleaning, transformation, and feature engineering methods.
• Data Analysis with Python: Utilize Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization.
• Model Building and Evaluation: Learn to build predictive models and assess their accuracy with real-world datasets.
• Data Visualization: Create informative and visually appealing data visualizations to communicate insights.
• Explore the fundamentals of MLOps and how it overcomes the challenges inherent in the traditional ML lifecycle.