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  • 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.

  • 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 theoretical concept behind these algorithms followed by the practical implementation.

  • 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.

  • 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 the second is about predicting the insurance premium using a regression machine learning algorithm.

  • 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.
  • Basic understanding of Python Programming Language.
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  • Section 1 : Welcome and Course Overview 2 Lectures 00:08:05

    • Lecture 1 :
    • Lecture 2 :
    • Course Overview
  • Section 2 : Numpy 7 Lectures 00:59:53

    • 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
  • Section 3 : Pandas 5 Lectures 00:19:43

    • Lecture 1 :
    • Pandas Introduction and Installation
    • Lecture 2 :
    • Pandas Series
    • Lecture 3 :
    • Pandas dataframe
    • Lecture 4 :
    • ReadCSV Pandas
    • Lecture 5 :
    • Analyzing data frames
  • Section 4 : Data Visualization 3 Lectures 00:25:47

    • Lecture 1 :
    • Matplotlib Introduction
    • Lecture 2 :
    • Different type of plots in Matplotlib
    • Lecture 3 :
    • Seaborn
  • Section 5 : Data Preparation 3 Lectures 00:28:00

    • Lecture 1 :
    • Handling Missing Values
    • Lecture 2 :
    • Feature Encoding
    • Lecture 3 :
    • Feature Scaling
  • Section 6 : Machine Learning 13 Lectures 01:51:06

    • 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
  • Section 7 : Machine Learning Pipeline 1 Lectures 00:09:36

    • Lecture 1 :
    • Machine Learning Pipeline
  • Section 8 : Projects 2 Lectures 00:00:01

    • Lecture 1 :
    • Diabetes Prediction
    • Lecture 2 :
    • Insurance Cost Prediction
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My name is Raj Chhabria and I am a Computer Science Engineer with specialization in Data Science. I am an accomplished coder and programmer, and I enjoy using my skills to contribute to student community by my Udemy Courses. Here on this platform I intend to share my knowledge in most condensed form through my courses.
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