Data Science and Machine Learning Basic to Advanced
Data Science and Machine Learning Basic to Advanced, available at $74.99, has an average rating of 4.1, with 36 lectures, based on 620 reviews, and has 48169 subscribers.
You will learn about 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. This course is ideal for individuals who are 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. It is particularly useful for 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.
Enroll now: Data Science and Machine Learning Basic to Advanced
Summary
Title: Data Science and Machine Learning Basic to Advanced
Price: $74.99
Average Rating: 4.1
Number of Lectures: 36
Number of Published Lectures: 36
Number of Curriculum Items: 36
Number of Published Curriculum Objects: 36
Original Price: ₹3,999
Quality Status: approved
Status: Live
What You Will 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.
Who Should Attend
- 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 Audiences
- 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.
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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 theory concept behind these algorithms followed by the practical implementation.
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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.
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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 second is about prediciting the insurance premium using a regression machine learning algorithm.
Course Curriculum
Chapter 1: Welcome and Course Overview
Lecture 1: Welcome
Lecture 2: Course Overview
Chapter 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
Chapter 3: Pandas
Lecture 1: Pandas Introduction and Installation
Lecture 2: Pandas Series
Lecture 3: Pandas DataFrame
Lecture 4: Pandas ReadCSV
Lecture 5: Pandas Analyzing DataFrames
Chapter 4: Data Visualization
Lecture 1: Matplotlib Introduction
Lecture 2: Different types of plots in Matplotlib
Lecture 3: Seaborn
Chapter 5: Data Preparation
Lecture 1: Handling Missing Values
Lecture 2: Feature Encoding
Lecture 3: Feature Scaling
Chapter 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: GridSearch CV
Chapter 7: Machine Learning Pipeline
Lecture 1: Machine Learning Pipeline
Chapter 8: Projects
Lecture 1: Diabetes Prediction
Lecture 2: Insurance Cost Prediction
Instructors
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Raj Chhabria
Computer Science Engineer with Specialization in DataScience
Rating Distribution
- 1 stars: 7 votes
- 2 stars: 9 votes
- 3 stars: 102 votes
- 4 stars: 243 votes
- 5 stars: 259 votes
Frequently Asked Questions
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