Data ScienceMachine Learning

Data Science Tutorial for Beginners 2024

8 Modules31 Lessons7380 Learners

Learn Data Science for free with our data science tutorial. Explore essential skills, tools, and techniques to master Data Science and kickstart your career

Start LearningLast Updated: 12th November, 2024

Data Science is a powerful field that uncovers hidden insights in data to guide smarter decisions. Our data science tutorial will help you dive into this data-driven world, opening doors to endless opportunities.

AlmaBetter’s Data Science Tutorial for Beginners is an introductory course that provides an overview of the field of data science which includes modules such as Machine Learning, Supervised Learning, Unsupervised Learning, MLOPS, Regression and many more deep concepts of data science.

AlamBetter Tutorials are the best platform to learn data science from scratch. Our free Data Science Tutorial is the best way to learn data science online as it provides you the freedom to learn at your own pace and enjoy the learning to the fullest.

Modules Explored in Our Data Science Tutorial:

Getting Started with Machine Learning
Data preparation and EDA
Supervised Learning
Regression
Classification
Non-Linear Model
Unsupervised Learning
MLOPS

Course Curriculum

Module 1Getting Started with Machine Learning

Module 2Data preparation and EDA

Module 3Supervised Learning

Module 4Regression

Module 5Classification

Module 6Non-Linear Model

Module 7Unsupervised Learning

Module 8MLOPS

What is MLOPS?

Stakeholders and Terminologies Used in MLOPS

Start Learning

Summary

Written by Industry ExpertsSelf-paced Learning with Unlimited AccessEarn a Certificate Upon Compeletition

Start Learning

Data Science Tutorial for Beginners 2024

Course Curriculum

Module 1Getting Started with Machine Learning

Introduction to Machine Learning

Types of Machine Learning Model

Applications of Machine Learning

Module 2Data preparation and EDA

Data Cleaning in Data Science

Normalization in Machine Learning

Introduction to Exploratory Data Analysis (EDA)

Feature Engineering for Machine Learning

Module 3Supervised Learning

Introduction to Supervised Learning

Regression in Machine Learning

Classification in Data Science

Module 4Regression

Linear Regression in Machine learning

Understanding Bias Variance Tradeoff

Regularization in Machine Learning

Metrics to Measure Regression Models

Module 5Classification

Logistic Regression in Machine Learning

Decision Tree in Machine Learning

Ensembles of Decision Trees

Random Forest Algorithm in Machine Learning

AdaBoost Algorithm

Gradient Boosting Algorithm for Machine Learning

XGBoost Algorithm in Machine Learning

Metrics for Classification Model

Module 6Non-Linear Model

K-Nearest Neighbors(KNN)

Support Vector Machines (SVMs)

Naive Bayes Classifier

Module 7Unsupervised Learning

What is Unsupervised Learning?

K-means Clustering

Hierarchical Clustering in Machine Learning

Metrics for Unsupervised Learning

Module 8MLOPS

What is MLOPS?

Stakeholders and Terminologies Used in MLOPS

Summary