https://www.udemy.com/course/one-week-of-data-science

Master Data Science Fundamentals Quickly & Efficiently in one week! Course is Designed for Busy People

What you'll learn: 

Perform statistical analysis on real world datasets

Understand feature engineering strategies and tools

Perform one hot encoding and normalization

Understand the difference between normalization and standardization

Deal with missing data using pandas

Change pandas DataFrame datatypes

Define a function and apply it to a Pandas DataFrame column

Perform Pandas operations and filtering

Calculate and display correlation matrix heatmap

Perform data visualization using Seaborn and Matplotlib libraries

Plot single line plot, pie charts and multiple subplots using matplotlib

Plot pairplot, countplot, and correlation heatmaps using Seaborn

Plot distribution plot (distplot), Histograms and scatterplots

Understand machine learning regression fundamentals

Learn how to optimize model parameters using least sum of squares

Split the data into training and testing using SK Learn Library

Perform data visualization and basic exploratory data analysis

Build, train and test our first regression model in Scikit-Learn

Assess trained machine learning regression model performance

Understand the theory and intuition behind boosting

Train an XG-boost algorithm in Scikit-Learn to solve regression type problems

Train several machine learning models classifier models such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier

Assess trained model performance using various KPIs such as accuracy, precision, recall, F1-score, AUC and ROC.

Compare the performance of the classification model using various KPIs.

Apply autogluon to solve regression and classification type problems

Use AutoGluon library to perform prototyping of AI/ML models using few lines of code

Plot various models’ performance on model leaderboard

Optimize regression and classification models hyperparameters using SK-Learn

Learn the difference between various hyperparameters optimization strategies such as grid search, randomized search, and Bayesian optimization.

Perform hyperparameters optimization using Scikit-Learn library.

Understand bias variance trade-off and L1 and L2 regularization

Requirements:

Basic Programming skills in python

Description:

Do you want to Learn Data Science and build powerful applications Quickly and Efficiently?

Are you an absolute beginner who want to break into Data Science and looking for a course that includes all the basics you need?

Are you a busy aspiring entrepreneur who wants to maximize business revenues and reduce costs with Data Science but don’t have the time to get there quickly and efficiently?

If the answer is yes to any of these questions, then this course is for you!

Data Science is one of the hottest tech fields to be in right now!

The field is exploding with opportunities and career prospects.

Data Science is widely adopted in many sectors nowadays such as banking, healthcare, transportation, and technology.

In business, Data Science is applied to optimize business processes, maximize revenue, and reduce cost.

The purpose of this course is to provide you with knowledge of key aspects of data science in one week and in a practical, easy, quick, and efficient way.

This course is unique and exceptional in many ways, it includes several practice opportunities, quizzes, and final capstone projects.

Every day, we will spend 1-2 hours together and master a data science topic together.

First, we will start with Data Science essential starter pack, and we will master key Data Science Concepts including Data Science project lifecycle, what do recruiters look for and what kind of jobs available out there.

Next, we will understand exploratory data analysis and visualization techniques using Pandas, matplotlib and Seaborn libraries.

In the following section, we will learn about regression fundamentals, we will learn how to build, train, test and deploy regression models using Scikit Learn library.

In the following section, we will learn about hyperparameters optimization strategies such as grid search, randomized search, and Bayesian optimization.

Next, we will learn how to train several classification algorithms such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest Classifier, and Naïve bayes in SageMaker and SK-Learn libraries.

Next, we will cover Data Science on Autopilot! We will learn how to use AutoGluon library to prototype multiple AI/ML models and deploy the best one.

So who this course is for?

The course is targeted towards anyone wanting to gain a fundamental understanding of Data Science and solve practical real world business problems.

In this course:

You will have a true practical project-based learning experience, we will build over 10 projects together

You will have access to all the codes and slides

You will get a certificate of completion that you can post on your LinkedIn profile to showcase your skills in Data Science to employers.

All this comes with a 30-day money back guarantee so you can give a course a try risk free!

Check out the preview videos and the outline to get an idea of the projects we will be covering.

Enroll today and let’s harness the power of Data Science together!

Who this course is for:The course is targeted towards anyone wanting to gain a fundamental understanding of Data Science and solve practical real world business problemsBeginners Data Scientists wanting to advance their careers and build their portfolioSeasoned consultants wanting to transform businesses by leveraging Data ScienceTech enthusiasts who are passionate and new to Data science & AI and want to gain practical experience

Who this course is for:

The course is targeted towards anyone wanting to gain a fundamental understanding of Data Science and solve practical real world business problems

Beginners Data Scientists wanting to advance their careers and build their portfolio

Seasoned consultants wanting to transform businesses by leveraging Data Science

Tech enthusiasts who are passionate and new to Data science & AI and want to gain practical experience