Rating 4.52 out of 5 (21 ratings in Udemy)
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 …
Rating 4.52 out of 5 (21 ratings in Udemy)
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
Description
Do you want to learn Data Science and build robust applications Quickly and Efficiently?
Are you an absolute beginner who wants to break into Data Science and look 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?
This course is for you if the answer is yes to any of these questions!
Data Science is one of the hottest tech fields to be in now!
The field is exploding with opportunities and career prospects.
Data Science is widely adopted in many sectors such as banking, healthcare, transportation, and technology.
In business, Data Science is applied to optimize business processes, maximize revenue, and reduce cost.
This course aims to provide you with knowledge of critical 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.
First, we will start with the Data Science essential starter pack and master key Data Science Concepts, including Data Science project lifecycle, what recruiters look for, and what kind of jobs are available.
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 the Scikit Learn library.
In the following section, we will learn about hyperparameter 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 the AutoGluon library for prototyping multiple AI/ML models and deploying the best one.
So who this course is for?
The course targets anyone wanting to gain a fundamental understanding of Data Science and solve practical, real-world business problems.
In this course:
You will have an actual practical project-based learning experience. We will build over ten 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 cover.
Enroll today, and let’s harness the power of Data Science together!
Paid
Self paced
Beginner Level
English (US)
457
Rating 4.52 out of 5 (21 ratings in Udemy)
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