Last Updated on May 10, 2024 by GeeksGod

Course : Data Science and Machine Learning Basic to Advanced

Learn how to use Numpy and Pandas for Data Analysis

This comprehensive course will teach you how to effectively use Numpy and Pandas for data analysis. By mastering these powerful Python libraries, you will gain valuable skills that are essential in the field of data analysis.

Creating Impactful Visualizations with Matplotlib and Seaborn

In addition to learning Numpy and Pandas, this course will also cover how to create impactful visualizations using Matplotlib and Seaborn. Visualizations are a crucial tool in data analysis as they help provide a better understanding of the data. Through this course, you will learn how to create meaningful visualizations that will enhance your data analysis skills.

Data Preprocessing: Handling Missing Values, Feature Encoding, and Feature Scaling

Data preprocessing is an integral step in data analysis. In this course, you will learn all the essential techniques for handling missing values, feature encoding, and feature scaling. These preprocessing steps are essential for ensuring the quality and accuracy of your data analysis.

Exploring Different Machine Learning Models

In order to make accurate predictions and gain insights from data, it is important to understand different machine learning models. This course will introduce you to various machine learning models such as Random Forest, Decision Trees, KNN, SVM, Linear Regression, and Logistic Regression. Through both theoretical explanations and practical implementations, you will gain a comprehensive understanding of these models.

Hyperparameter Tuning with GridSearch CV

Choosing the best hyperparameters for your machine learning models is crucial in achieving optimal performance. This course will teach you how to use GridSearch CV to tune the hyperparameters of your models. By finding the best combination of hyperparameters, you can significantly improve the accuracy and efficiency of your machine learning models.

Building a Complete Machine Learning Pipeline

A machine learning pipeline is a series of steps that are performed in sequence to build a successful machine learning project. This course will guide you through building a complete machine learning pipeline, starting from data collection to data preprocessing and model building. By understanding the entire pipeline, you will be able to develop large-scale machine learning projects with ease.

Two Real-World Projects

In order to apply the knowledge and skills learned in this course, you will have the opportunity to work on two real-world projects. The first project involves diabetes prediction using a classification machine learning algorithm. The second project focuses on predicting insurance premiums using a regression machine learning algorithm. These projects will allow you to showcase your expertise in the field and gain practical experience with real data.

Claim your Free Udemy Coupon and Advance Your AI Skills Today!

This course offers valuable knowledge and skills in the field of data analysis and machine learning. By enrolling today, you can take advantage of a Free Udemy Coupon and enhance your AI skills. Don’t miss out on this opportunity to improve your search engine ranking and advance your career in the ever-growing field of AI.

Learn how to use Numpy and Pandas for Data Analysis. This will cover all basic concepts of Numpy and Pandas that are useful in data analysis.Learn to create impactful visualizations using Matplotlib and Seaborn. Creating impactful visualizations is a crucial step in developing a better understanding about your data.This course covers all Data Preprocessing steps like working with missing values, Feature Encoding and Feature Scaling.Learn about different Machine Learning Models like Random Forest, Decision Trees, KNN, SVM, Linear Regression, Logistic regression etc… All the video sessions will first discuss the basic theory concept behind these algorithms followed by the practical implementation.Learn to how to choose the best hyper parameters for your Machine Learning Model using GridSearch CV. Choosing the best hyper parameters is an important step in increasing the accuracy of your Machine Learning Model.You will learn to build a complete Machine Learning Pipeline from Data collection to Data Preprocessing to Model Building. ML Pipeline is an important concept that is extensively used while building large scale ML projects.This course has two projects at the end that will be built using all concepts taught in this course. The first project is about Diabetes Prediction using a classification machine learning algorithm and second is about prediciting the insurance premium using a regression machine learning algorithm.