Are you looking to improve your machine learning skills and get hands-on experience in Python? Look no further than the 100+ Exercises - Python - Data Science - scikit-learn course, designed to test your Python programming skills and expand your knowledge of machine learning.
Course Overview
The course is built around over 100 exercises covering a wide range of topics: from preparing data for machine learning models to feature extraction and clustering. You'll also work with popular machine learning libraries such as numpy, pandas and scikit-learn, and explore topics that include regression, classification, clustering, and dimensionality reduction.
The course is designed for learners with a basic understanding of Python and eagerness to explore the world of machine learning. Each exercise comes with a solution to help you reinforce your understanding of key concepts.
Course Rating and Reviews
With an aggregate rating of 4.81986 and 85 reviews to date, it's clear that learners are finding great value in this course. Positive reviews highlight the course's comprehensive coverage of essential machine learning topics, the high-quality instruction, and useful exercises and solutions for reinforcement.
One reviewer raves that, "This course is an excellent way to jump into Python and get experience using scikit-learn and other data science libraries. The exercises are challenging, but the solutions are thorough and well-explained."
Another reviewer feels that "the course is great for people who want to get a better understanding of machine learning implementations in Python."
Course Topics
The course covers a wide range of topics, including:
- Preparing data to machine learning models
- Working with missing values and SimpleImputer class
- Categorization, regression, clustering and discretization
- Feature extraction using PolynomialFeatures class
- LabelEncoder, OneHotEncoder and StandardScaler classes
- Dummy encoding and data splitting into the train and test set
- LogisticRegression class with confusion matrices and classification reports
- LinearRegression class with MAE - Mean Absolute Error and MSE - Mean Squared Error
- Sigmoid function and calculations related to AUC and entropy
- DecisionTrees with GridSearchCV, RandomForest with CountVectorizer and TfidfVectorizer Classes
- KMeans, AgglomerativeClustering, HierarchicalClustering, DBSCAN for clustering
- Dimensionality reduction and PCA analysis
- Association Rules, LocalOutlierFactor class and IsolationForest class
- KNeighborsClassifier class and MultinomialNB class
- GradientBoostingRegressor class for regression
Course Description
The course description highlights that it is designed for people with a basic understanding of Python, numpy, pandas, and scikit-learn, with over 100 exercises and solutions to reinforce learning. In addition, it emphasizes that this is a great way for people who are learning machine learning and looking for new challenges.
The wide variety of topics covered in this course are useful both for learners seeking to gain practical experience in machine learning and those preparing for job interviews or looking to upskill in this field. Many popular machine learning topics are covered comprehensively.
Why Python?
According to the Stack Overflow Developer Survey 2021, Python is the most wanted programming language with NumPy being the second most used tool in the "Other Frameworks and Libraries" category. Python passed SQL to become the third most popular technology. Developers want to work with Python more than any other programming language.
APIs, automation, and data are among the essential areas for which Python is widely used in the world of technology. It is also simpler to acquire than other programming languages, with an elegant syntax and efficient coding in a modular fashion. Python is frequently devoted to scientific computing, data analysis, and machine learning, making it a valuable language for data scientists.
Why Take the Course?
Machine learning is a rapidly growing field that is becoming increasingly critical to the development of new technologies. With more businesses and firms relying on machine learning for strategic insights, the need for professionals with machine learning expertise is higher than ever before.
The 100+ Exercises - Python - Data Science - scikit-learn - 2023 course is an excellent way to acquire machine learning experience that will help set you apart and improve your career prospects. With comprehensive coverage of a wide range of topics and detailed solutions to exercises, this course is designed not only to teach you machine learning but to help you master it.
Whether you're a beginner looking to get started with machine learning or an experienced data scientist looking to stay up-to-date in this rapidly evolving field, the 100+ Exercises - Python - Data Science - scikit-learn course is an excellent way to build your skills and achieve your career goals.
