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This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
HD Video Recorded Lectures- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Chat Support
- Cancel Plan Anytime
This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
- HD Video Recorded Lectures
- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Chat Support
- Cancel Plan Anytime
What you'll learn?
- Students will develop understanding of libraries used for Data Analysis like Pandas and Numpy.
- Learn to create impactful visualizations using Matplotlib and Seaborn. By creating these visualizations you will be able to derive better conclusions from data.
- After this course you will learn to build complete Data Science Pipeline from Data preparation to building the best Machine Learning Model.
- The course contains practical section after every new concept discussed and the course also has two projects at the end.
Course Overview
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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.
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Learn to create impactful visualizations using Matplotlib and Seaborn. Creating impactful visualizations is a crucial step in developing a better understanding about your data.
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This course covers all Data Preprocessing steps like working with missing values, Feature Encoding and Feature Scaling.
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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 theoretical concept behind these algorithms followed by the practical implementation.
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Learn to how to choose the best hyperparameters for your Machine Learning Model using GridSearch CV. Choosing the best hyperparameters is an important step in increasing the accuracy of your Machine Learning Model.
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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.
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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 the second is about predicting the insurance premium using a regression machine learning algorithm.
Pre-requisites
- Basic understanding of Python Programming Language.Anyone who is looking to start his or her Data Science and Machine Learning Journey. People who are at intermediate level and already have some basic understanding of Data Science will also find this course helpful.
Target Audience
- Basic understanding of Python Programming Language.
Curriculum 36 Lectures 04:22:11
-
Section 1 : Welcome and Course Overview
- Lecture 2 :
- Course Overview
-
Section 2 : Numpy
- Lecture 1 :
- Numpy Introduction and Installation
- Lecture 2 :
- Creating Arrays in Numpy
- Lecture 3 :
- Array Shape and Reshape
- Lecture 4 :
- Array Indexing
- Lecture 5 :
- Array Iterating
- Lecture 6 :
- Array Slicing
- Lecture 7 :
- Searching and Sorting
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Section 3 : Pandas
- Lecture 1 :
- Pandas Introduction and Installation
- Lecture 2 :
- Pandas Series
- Lecture 3 :
- Pandas dataframe
- Lecture 4 :
- ReadCSV Pandas
- Lecture 5 :
- Analyzing data frames
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Section 4 : Data Visualization
- Lecture 1 :
- Matplotlib Introduction
- Lecture 2 :
- Different type of plots in Matplotlib
- Lecture 3 :
- Seaborn
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Section 5 : Data Preparation
- Lecture 1 :
- Handling Missing Values
- Lecture 2 :
- Feature Encoding
- Lecture 3 :
- Feature Scaling
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Section 6 : Machine Learning
- Lecture 1 :
- Machine Learning Introduction
- Lecture 2 :
- Supervised machine learning
- Lecture 3 :
- Unsupervised machine learning
- Lecture 4 :
- Train Test Split
- Lecture 5 :
- Regression Analysis
- Lecture 6 :
- Linear Regression
- Lecture 7 :
- Logistic Regression
- Lecture 8 :
- KNN
- Lecture 9 :
- SVM
- Lecture 10 :
- Decision Tree
- Lecture 11 :
- Random Forest
- Lecture 12 :
- K Means Clustering
- Lecture 13 :
- Grid Search CV
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Section 7 : Machine Learning Pipeline
- Lecture 1 :
- Machine Learning Pipeline
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Section 8 : Projects
- Lecture 1 :
- Diabetes Prediction
- Lecture 2 :
- Insurance Cost Prediction
Our learners work at
Frequently Asked Questions
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Yes. All of the courses comes with online video based lectures created by certified instructors. Instructors have crafted these courses with a blend of high quality interactive videos, lectures, quizzes & real world projects to give you an indepth knowledge about the topic. -
Can i play & pause the course as per my convenience?
Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on. -
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