100+ Exercises – Python – Data Science – Scikit-learn – 2023

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