Data Science And Machine Learning Basic To Advanced

Published 10/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.59 GB | Duration: 5h 0m

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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