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.

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.

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

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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