Course Details
Machine
Learning
Course Overview
A Machine Learning course introduces students to the fundamentals, techniques, and applications of machine learning. It covers the essentials of data preprocessing, supervised and unsupervised learning algorithms, and key tools such as Python and libraries like TensorFlow and Scikit-Learn. Students learn to work with algorithms for regression, classification, clustering, and dimensionality reduction, and gain skills in evaluating model performance, tuning hyperparameters, and deploying models. The course culminates in a capstone project, giving students hands-on experience with real-world data and end-to-end model development, preparing them for practical ML challenges and applications.
Course lessons
- Overview of ML and AI, including key concepts and definitions.
- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning.
- Real-world applications of ML across industries.
- Collecting, cleaning, and preparing data.
- Feature selection and extraction, handling missing values, scaling, and encoding categorical data.
- Exploratory Data Analysis (EDA) and visualization.
- Linear Regression, Logistic Regression, and other regression techniques.
- Classification algorithms like Decision Trees, Support Vector Machines, K-Nearest Neighbors, and Naive Bayes.
- Performance metrics and evaluation (accuracy, precision, recall, F1 score).
- Clustering methods (K-Means, Hierarchical Clustering).
- Dimensionality Reduction techniques (PCA, t-SNE).
- Applications of unsupervised learning in data exploration.
- Ensemble Methods (Random Forest, Gradient Boosting, and XGBoost).
- Neural Networks and Deep Learning basics.
- Natural Language Processing (NLP) and Computer Vision basics.
- Cross-validation, hyperparameter tuning, and model selection.
- Techniques to prevent overfitting and underfitting (regularization).
- Evaluation metrics for different types of ML models.
- Introduction to deploying ML models in production environments.
- Popular tools and frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
- Ethics and best practices in machine learning.
End-to-end project using real data, covering the full pipeline from data preprocessing to model evaluation and deployment.
Skills Accuired
- Data Preprocessing: Cleaning, transforming, and preparing data for analysis.
- Statistical Analysis: Understanding distributions, correlations, and patterns within data.
- Supervised & Unsupervised Learning: Building models for regression, classification, and clustering tasks.
- Model Evaluation & Optimization: Assessing model accuracy, adjusting hyperparameters, and preventing overfitting.
- Programming with Python: Utilizing essential libraries like TensorFlow, Scikit-Learn, Pandas, and NumPy for data handling and modeling.
- Feature Engineering: Selecting and engineering features to improve model performance.
- Visualization & Interpretation: Using tools like Matplotlib and Seaborn to visualize data and model outcomes.
- Model Deployment: Deploying and integrating machine learning models in real-world applications.
Course Features
A Machine Learning course equips learners with essential skills in data handling, model building, and AI techniques through hands-on projects and real-world case studies. Covering core concepts like supervised and unsupervised learning, it uses Python libraries like Scikit-Learn and TensorFlow to help students build, evaluate, and optimize models. With practical examples from various industries, interactive assessments, and certification, the course prepares students for real-world applications in fields like finance, healthcare, and e-commerce. Access to mentorship and a supportive community further enhances learning, making it ideal for aspiring data scientists and machine learning practitioners.
Designed for all levels
hands-on learning
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