Master Machine Learning – Unleash the Power of Data Science for Predictive Modeling!
Description
The “100+ Exercises – Python – Data Science – Scikit-learn” course is a comprehensive, hands-on guide to one of the most essential libraries for machine learning in Python, Scikit-learn. This course employs a practical, exercise-driven approach that helps learners understand and apply various machine learning algorithms and techniques.
The course is organized into different sections, each devoted to a specific aspect of the Scikit-learn library. It covers everything from data preprocessing, including feature extraction and selection, to various machine learning models such as linear regression, decision trees, support vector machines, and ensemble methods, to model evaluation and hyperparameter tuning.
Each section is packed with carefully designed exercises that reinforce each concept and give you the chance to apply what you’ve learned. You will solve real-world problems that mirror the challenges faced by data scientists in the field. Detailed solutions accompany each exercise, enabling you to compare your work and gain a better understanding of how to best use Scikit-learn for machine learning tasks.
The “100+ Exercises – Python – Data Science – Scikit-learn” course is perfect for anyone interested in expanding their data science toolkit. Whether you’re a beginner looking to dive into machine learning, or a seasoned data scientist wanting to refine your skills, this course offers an enriching learning experience.
Scikit-learn – Unleash the Power of Machine Learning!
Scikit-learn is a versatile machine learning library in Python that provides a wide range of algorithms and tools for building and implementing machine learning models. It is widely used by data scientists, researchers, and developers to solve complex problems through classification, regression, clustering, and more. With Scikit-learn, you can efficiently preprocess data, select appropriate features, train and evaluate models, and perform model selection and hyperparameter tuning. It offers a consistent API, making it easy to experiment with different algorithms and techniques. Scikit-learn also provides useful utilities for data preprocessing, model evaluation, and model persistence. Its user-friendly interface and extensive documentation make it a go-to choice for machine learning practitioners looking to leverage the power of Python for their projects.
Topics you will find in this course:
- preparing data to machine learning models
- working with missing values, SimpleImputer class
- classification, regression, clustering
- discretization
- feature extraction
- PolynomialFeatures class
- LabelEncoder class
- OneHotEncoder class
- StandardScaler class
- dummy encoding
- splitting data into train and test set
- LogisticRegression class
- confusion matrix
- classification report
- LinearRegression class
- MAE – Mean Absolute Error
- MSE – Mean Squared Error
- sigmoid() function
- entorpy
- accuracy score
- DecisionTreeClassifier class
- GridSearchCV class
- RandomForestClassifier class
- CountVectorizer class
- TfidfVectorizer class
- KMeans class
- AgglomerativeClustering class
- HierarchicalClustering class
- DBSCAN class
- dimensionality reduction, PCA analysis
- Association Rules
- LocalOutlierFactor class
- IsolationForest class
- KNeighborsClassifier class
- MultinomialNB class
- GradientBoostingRegressor class
Who this course is for:
- data scientists or machine learning practitioners who want to enhance their skills in using the Scikit-learn library for building and evaluating machine learning models in Python
- students or individuals with a background in data science, machine learning, or related fields who want to gain hands-on experience in applying machine learning techniques using Scikit-learn
- programmers or software developers who are interested in data science and want to learn how to use Scikit-learn for tasks such as data preprocessing, model training, and evaluation
- professionals working in industries such as finance, healthcare, or marketing, where machine learning is applied, and who want to learn how to leverage Scikit-learn for their data analysis and modeling needs
- self-learners who are passionate about data science and want to develop proficiency in using Scikit-learn for various machine learning tasks, including classification, regression, clustering, and model evaluation
- researchers or scientists who want to apply machine learning techniques to their research problems and utilize Scikit-learn as a tool for model building and evaluation
