Applied Machine Learning Course Analyze Data and Build Models with Pythonn

This comprehensive course takes you from the basics of machine learning to expert-level model building using Python. Whether you’re a beginner or an experienced programmer, this course will guide you through data analysis, model development, and advanced techniques to create powerful ML applications.

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What’s Covered in the Course?

1. Introduction to Machine Learning

• Understanding supervised, unsupervised, and reinforcement learning

• Basics of Python for ML (NumPy, Pandas, Matplotlib, Seaborn)

• Exploratory Data Analysis (EDA) & data preprocessing techniques

2. Feature Engineering & Data Preparation

• Handling missing data, outliers, and feature scaling

• Encoding categorical variables and feature selection methods

• Dimensionality reduction (PCA, LDA, t-SNE)

3. Building Machine Learning Models

• Regression models: Linear Regression, Ridge, Lasso

• Classification models: Logistic Regression, Decision Trees, Random Forest, SVM, KNN

• Clustering algorithms: K-Means, DBSCAN, Hierarchical Clustering

• Hyperparameter tuning using GridSearchCV & RandomizedSearchCV

4. Deep Learning & Neural Networks

• Introduction to TensorFlow & Keras

• Building deep learning models with CNNs & RNNs

• Transfer learning and model optimization

5. Model Deployment & Real-World Applications

• Deploying models using Flask, FastAPI, and Streamlit

• Working with cloud platforms like AWS, Google Cloud, and Azure

• Case studies in finance, healthcare, and recommendation systems

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How Will This Course Help You?

By the end of this course, you will be able to analyze datasets, build powerful ML models, and deploy them for real-world applications. This course is perfect for data scientists, analysts, and AI enthusiasts looking to master applied machine learning.

For further learning, check out the eBook that complements this course.