Data Science And Machine Learning Basic To Advanced

Complete Introduction to Data Science and Machine Learning from Basic to Advanced.
What you'll learn
Students will have 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.
Requirements
Basic understanding of Python Programming Language.
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.
Overview
Section 1: Welcome and Course Overview
Lecture 1 Welcome
Lecture 2 Course Overview
Section 2: Numpy
Lecture 3 Numpy Introduction and Installation
Lecture 4 Creating Arrays in Numpy
Lecture 5 Array Shape and Reshape
Lecture 6 Array Indexing
Lecture 7 Array Iterating
Lecture 8 Array Slicing
Lecture 9 Searching and Sorting
Section 3: Pandas
Lecture 10 Pandas Introduction and Installation
Lecture 11 Pandas Series
Lecture 12 Pandas DataFrame
Lecture 13 Pandas ReadCSV
Lecture 14 Pandas Analyzing DataFrames
Section 4: Data Visualization
Lecture 15 Matplotlib Introduction
Lecture 16 Different types of plots in Matplotlib
Lecture 17 Seaborn
Section 5: Data Preparation
Lecture 18 Handling Missing Values
Lecture 19 Feature Encoding
Lecture 20 Feature Scaling
Section 6: Machine Learning
Lecture 21 Machine Learning Introduction
Lecture 22 Supervised Machine Learning
Lecture 23 Unsupervised Machine Learning
Lecture 24 Train Test Split
Lecture 25 Regression Analysis
Lecture 26 Linear Regression
Lecture 27 Logistic Regression
Lecture 28 KNN
Lecture 29 SVM
Lecture 30 Decision Tree
Lecture 31 Random Forest
Lecture 32 K Means Clustering
Lecture 33 GridSearch CV
Section 7: Machine Learning Pipeline
Lecture 34 Machine Learning Pipeline
Section 8: Projects
Lecture 35 Diabetes Prediction
Lecture 36 Insurance Cost Prediction
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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