English | Size: 2.14 GB
Genre: eLearning
What you’ll learn
Machine learning describes systems that make predictions using a model trained on real-world data.
Machine learning isn’t just useful for predictive texting or smartphone voice recognition.
Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction.
Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithm
Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems.
First Step to the Project
Notebook Design to be Used in the Project
Examining the Project Topic
Recognizing Variables in Dataset
Required Python Libraries
Loading the Dataset
Initial analysis on the dataset
Examining Missing Values
Examining Unique Values
Separating variables (Numeric or Categorical)
Examining Statistics of Variables
Numeric Variables (Analysis with Distplot)
Categoric Variables (Analysis with Pie Chart)
Examining the Missing Data According to the Analysis Result
Numeric Variables – Target Variable (Analysis with FacetGrid)
Categoric Variables – Target Variable (Analysis with Count Plot)
Examining Numeric Variables Among Themselves (Analysis with Pair Plot)
Feature Scaling with the Robust Scaler Method for New Visualization
Creating a New DataFrame with the Melt() Function
Numerical – Categorical Variables (Analysis with Swarm Plot)
Numerical – Categorical Variables (Analysis with Box Plot)
Relationships between variables (Analysis with Heatmap)
Dropping Columns with Low Correlation
Visualizing Outliers
Dealing with Outliers
Determining Distributions of Numeric Variables
Transformation Operations on Unsymmetrical Data
Applying One Hot Encoding Method to Categorical Variables
Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms
Separating Data into Test and Training Set
Logistic Regression
Cross Validation for Logistic Regression Algorithm
Roc Curve and Area Under Curve (AUC) for Logistic Regression Algorithm
Hyperparameter Optimization (with GridSearchCV) for Logistic Regression Algorithm
Decision Tree Algorithm
Support Vector Machine Algorithm
Random Forest Algorithm
Hyperparameter Optimization (with GridSearchCV) for Random Forest Algorithm
Project Conclusion and Sharing
Who this course is for:
Anyone who wants to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Those who want to compete in data science and machine learning
Those who want to improve their CV in Data Science, Machine Learning, Python with Kaggle
Anyone who is interested in Artificial Intelligence, Machine Learning, Deep Learning, in short Data Science
Anyone who have a career goal in Data Science
Anyone who needs a complete guide on how to start and continue their career with machine learning
Students Interested in Beginning Data Science Applications in Python Environment
Students Wants to Learn the Application of Supervised Learning (Classification) on Real Data Using Python
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