This course establishes essential machine learning foundations for professionals entering the field. You'll gain hands-on experience with core algorithms, data preparation techniques, and model evaluation methods used in real-world applications. ### Course Content * **Introduction to Machine Learning & Python for ML**: Master supervised and unsupervised learning fundamentals while building proficiency with Python libraries including NumPy, Pandas, Matplotlib, and Scikit-learn. * **Data Preprocessing & Feature Engineering**: Learn to clean and transform real-world data, handle missing values and outliers, encode categorical variables, scale features, and engineer meaningful attributes that improve model performance. * **Classical Machine Learning Algorithms**: Build and compare foundational models including linear/logistic regression, decision trees, random forests, support vector machines, and k-nearest neighbors, understanding when to apply each algorithm. * **Model Evaluation & Optimization**: Develop skills to assess model performance using cross-validation, select appropriate metrics, tune hyperparameters, and prevent overfitting to ensure models generalize well. ### Audience Analysts, software developers, and professionals with basic Python knowledge who want to transition into data science and machine learning roles.

This course covers advanced machine learning techniques used in industry settings. You'll move beyond foundational concepts to work on complex problems that require specialized skills. ### Course Content - **Deep Learning:** Understand neural network architectures and configure them for specific tasks - **Natural Language Processing:** Build text analysis systems including chatbots, sentiment analysis tools, and document classifiers - **Time-Series Forecasting:** Develop models to predict trends, demand patterns, and behavioral changes - **Explainable AI:** Learn methods to interpret model decisions and communicate results to stakeholders - **Applied Project:** Complete a capstone project that demonstrates end-to-end ML implementation ### Audience Data analysts, junior data scientists, and software engineers seeking to develop advanced ML skills.

Master the fundamentals of supervised and unsupervised learning while building proficiency with essential Python libraries like NumPy, Pandas, and Scikit-learn for data manipulation and modeling.

Master deep learning fundamentals and apply them to real-world data across domains like computer vision and NLP through hands-on projects with industry-standard frameworks.

Build strong foundations in Natural Language Processing, from classical methods to deep learning architectures, including embeddings, RNNs, and the basics of modern large language models.

Learn to clean and transform real-world data, handle missing values and outliers, encode categorical variables, scale features, and engineer meaningful attributes that improve model performance.

Build expertise in time series forecasting fundamentals from decomposition to ARIMA models, then advance to machine learning and recurrent neural networks through hands-on projects with real-world datasets.

Build and compare foundational models including linear/logistic regression, decision trees, random forests, support vector machines, and k-nearest neighbors, understanding when to apply each algorithm

Develop skills to assess model performance using cross-validation, select appropriate metrics, tune hyperparameters, and prevent overfitting to ensure models generalize well.

Learn to interpret machine learning models through hands-on implementation of SHAP, LIME, GradCAM, and other essential techniques.