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The course “Data Science and Machine Learning Basic to Advanced” is a comprehensive training program designed for anyone who wants to learn the fundamentals of data science and machine learning, as well as advanced topics.
The course covers various topics, including data preprocessing, exploratory data analysis, supervised and unsupervised learning, deep learning, and natural language processing. Students will learn how to apply various algorithms and techniques to analyze and make predictions from large datasets, as well as best practices for data visualization and model evaluation.
The course also covers how to use popular programming languages such as Python and R, as well as popular libraries like NumPy, Pandas, Scikit-learn, and TensorFlow. With the skills and knowledge gained from this course, students can pursue a career in data science, machine learning, or artificial intelligence.
This course is ideal for students, working professionals, or anyone who wants to learn how to apply data science and machine learning techniques to solve real-world problems.
Data Science and Machine Learning Basic to Advanced Course Description
Learn how to use Numpy and Pandas for Data Analysis. This will cover all basic concepts of Numpy and Pandas that are useful in data analysis.
Learn to create impactful visualizations using Matplotlib and Seaborn. Creating impactful visualizations is a crucial step in developing a better understanding about your data.
This course covers all Data Preprocessing steps like working with missing values, Feature Encoding and Feature Scaling.
Learn about different Machine Learning Models like Random Forest, Decision Trees, KNN, SVM, Linear Regression, Logistic regression etc… All the video sessions will first discuss the basic theory concept behind these algorithms followed by the practical implementation.
Learn to how to choose the best hyper parameters for your Machine Learning Model using GridSearch CV. Choosing the best hyper parameters is an important step in increasing the accuracy of your Machine Learning Model.
You will learn to build a complete Machine Learning Pipeline from Data collection to Data Preprocessing to Model Building. ML Pipeline is an important concept that is extensively used while building large scale ML projects.
This course has two projects at the end that will be built using all concepts taught in this course. The first project is about Diabetes Prediction using a classification machine learning algorithm and second is about prediciting the insurance premium using a regression machine learning algorithm.
