One Week of Data Science - New 2022!
Last Updated 07/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 134 lectures (12h 52m) | Size: 8.4 GB

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 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

Homepage

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