Short Description
Improve your machine learning skills and solve over 100 exercises in python, numpy, pandas and scikit-learn!
What you’ll learn
- solve over 100 exercises in numpy, pandas and scikit-learn
- deal with real programming problems in data science
- work with documentation and Stack Overflow
- guaranteed instructor support
This course includes:
- 28 mins on-demand video
- 106 articles
- 102 coding exercises
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
Requirements
- completed course ‘200+ Exercises – Programming in Python – from A to Z’
- completed course ‘210+ Exercises – Python Standard Libraries – from A to Z’
- completed course ‘150+ Exercises – Object Oriented Programming in Python – OOP’
- completed course ‘100+ Exercises – Python Programming – Data Science – NumPy’
- completed course ‘100+ Exercises – Python Programming – Data Science – Pandas’
- basic knowledge of scikit-learn and machine learning concepts
Description
RECOMMENDED LEARNING PATH
PYTHON DEVELOPER:
- 200+ Exercises – Programming in Python – from A to Z
- 210+ Exercises – Python Standard Libraries – from A to Z
- 150+ Exercises – Object Oriented Programming in Python – OOP
- 150+ Exercises – Data Structures in Python – Hands-On
- 100+ Exercises – Advanced Python Programming
- 100+ Exercises – Unit tests in Python – unittest framework
- 100+ Exercises – Python Programming – Data Science – NumPy
- 100+ Exercises – Python Programming – Data Science – Pandas
- 100+ Exercises – Python – Data Science – scikit-learn
- 250+ Exercises – Data Science Bootcamp in Python
SQL DEVELOPER:
- SQL Bootcamp – Hands-On Exercises – SQLite – Part I
- SQL Bootcamp – Hands-On Exercises – SQLite – Part II
COURSE DESCRIPTION
100+ Exercises – Python – Data Science – scikit-learn
Welcome to the course 100+ Exercises – Python – Data Science – scikit-learn where you can test your Python programming skills in machine learning, specifically in scikit-learn package.
Topics you will find in the exercises:
- 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
This course is designed for people who have basic knowledge in Python, numpy, pandas and scikit-learn. It consists of over 100 exercises with solutions.
This is a great test for people who are learning machine learning and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course.
If you’re wondering if it’s worth taking a step towards Python, don’t hesitate any longer and take the challenge today.
Who this course is for:
- everyone who wants to learn by doing
- everyone who wants to improve their Python programming skills
- everyone who wants to improve their data science skills
- everyone who wants to improve their machine learning skills
- everyone who wants to prepare for an interview
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