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What you would learn in One Week of Data Science - New 2022 course?
Would you wish to master Data Science and build robust applications efficiently and quickly?
Are you a complete novice who is looking to get into Data Science and are looking for a program that covers all the essentials you need?
Are you an aspiring and busy businessperson who would like to maximize your business's profits and cut costs using Data Science but doesn't have enough time to do it quickly and effectively?
If you answered yes to one or more of these questions, the course could be perfect for you!
Data Science is among the top tech fields to be in today!
The industry is exploding with possibilities and career opportunities.
Data Science is widely adopted across many industries like healthcare, banking, transportation, and technology.
In the business world, Data Science is applied to improve processes in business, boost revenues, and decrease costs.
The goal of the course serves to equip participants with an understanding of the most important areas of Data Science within only one week by using a practical, simple, fast, and efficient method.
This course is distinctive and unique in many ways. It offers numerous exercises, quizzes, and capstone projects for the final exam.
Each day, we'll spend about an hour together and learn about data science in a group.
We will begin the beginning with Data Science essential starter pack. Then we will learn the most critical Data Science Concepts, including the Data Science project lifecycle, what recruiters want to know and what kinds of jobs are out there.
The next step is understanding visualization and exploratory data analysis methods employing Pandas and matplotlib along with Seaborn libraries.
In the next part, we'll discover the basics of regression, and we will be taught how to construct models, train, and test them, then implement regression models with the help of the Scikit Learn library.
In the next section, we will discuss optimization strategies for hyperparameters, such as grid search, and random search, along with Bayesian optimization.
Then, we'll learn how to train various classification algorithms like Logistic Regression and the Support Vector Machine, K-Nearest Neighbors, Random Forest Classifier, and Naive Bayes in SageMaker and SK-Learn libraries.
In the next installment, we will discuss Data Science on Autopilot! We will discover how to use the AutoGluon library to build various AI/ML models and then implement the most efficient one.
Conduct statistical analysis using real-world data
Learn about feature engineering strategies and tools
Use one hot encoding and normalization
Know the difference between standardization and normalization.
Find missing data by using pandas
Change pandas DataFrame datatypes
You can define a function and apply it to the Pandas DataFrame column.
Execute Pandas operation and filters
Calculate and display the correlation matrix heatmap
Create data visualizations with the help of Seaborn or Matplotlib libraries
Single line plots, pie charts, single line plots, and multiple subplots with matplotlib.
Combine pair plot, countplot, and correlation heatmaps with Seaborn
Distribution plots of plots (distplot) Histograms, histograms, and scatterplots
Know the basics of machine learning regression
Discover how to improve the parameters of your model by using the minimum amount of squares
Separate the data into testing and training by using SK Learn Library
Perform basic data visualization and data analysis
Train, build, and run our regression test using Scikit-Learn
Evaluate the performance of a machine learning regression model's performance
Learn the theory and reasoning that drive boosting
Learn an XG-boost-based algorithm to train in Scikit-Learn to solve regression-type problems.
Create a range of models of machine learning, classifiers, and models like Logistic Regression, the Support Vector Machine K-Nearest Neighbors as well as Random Forest Classifier
Test the performance of trained models with various KPIs, such as precision, accuracy recall, F1-score AUC, and ROC.
Examine the performance of the model by using different KPIs.
Use autogluon to solve the problems of classification and regression.
Utilize the AutoGluon library to create prototypes of AI/ML models in just an only a few lines of code
Track the performance of different models on the model's leaderboard
Improve classification and regression models' hyperparameters with the SK-Learn program.
Find out the differences between different optimization strategies for hyperparameters like grid search, random search, and Bayesian optimization.
Optimize your hyperparameters with the Scikit-Learn library.
Know bias-variance trade-off and regularization of L1 and L2
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