Beginning with Machine Learning, Data Science and Python

Beginning with Machine Learning, Data Science and Python, available at $19.99, has an average rating of 4, with 50 lectures, 3 quizzes, based on 163 reviews, and has 7935 subscribers.

You will learn about You will be able to apply data science algorithms for solving industry problems You will have a clear understanding of industry standards and best practices for predictive model building You will be able to derive key insights from data using exploratory data analysis techniques You will be able to efficiently handle data in a structured way using Pandas You will have a strong foundation of linear regression, multiple regression and logistic regression You will be able to use python scikit-learn for building different types of regression models You will be able to use cross validation techniques for comparing models, select parameters You will know about common pitfalls in modeling like over-fitting, bias-variance trade off etc.. You will be able to regularize models for reliable predictions This course is ideal for individuals who are Anyone willing to take the first step towards data science or Anyone willing to develop a solid foundation for data science or Anyone planning to build the first regression / machine learning models or Anyone willing to learn exploratory data analysis It is particularly useful for Anyone willing to take the first step towards data science or Anyone willing to develop a solid foundation for data science or Anyone planning to build the first regression / machine learning models or Anyone willing to learn exploratory data analysis.

Enroll now: Beginning with Machine Learning, Data Science and Python

Summary

Title: Beginning with Machine Learning, Data Science and Python

Price: $19.99

Average Rating: 4

Number of Lectures: 50

Number of Quizzes: 3

Number of Published Lectures: 49

Number of Published Quizzes: 3

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 52

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

You will be able to apply data science algorithms for solving industry problems

You will have a clear understanding of industry standards and best practices for predictive model building

You will be able to derive key insights from data using exploratory data analysis techniques

You will be able to efficiently handle data in a structured way using Pandas

You will have a strong foundation of linear regression, multiple regression and logistic regression

You will be able to use python scikit-learn for building different types of regression models

You will be able to use cross validation techniques for comparing models, select parameters

You will know about common pitfalls in modeling like over-fitting, bias-variance trade off etc..

You will be able to regularize models for reliable predictions

Who Should Attend

Anyone willing to take the first step towards data science

Anyone willing to develop a solid foundation for data science

Anyone planning to build the first regression / machine learning models

Anyone willing to learn exploratory data analysis

Target Audiences

Anyone willing to take the first step towards data science

Anyone willing to develop a solid foundation for data science

Anyone planning to build the first regression / machine learning models

Anyone willing to learn exploratory data analysis

85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). So naturally, 85% of the interview questions come from these topics as well.

This concise course, created by UNP, focuses on what matter most.This course will help you create a solid foundation of the essential topics of data science. With this solid foundation, you will go a long way, understand any method easily, and create your own predictive analytics models.

At the end of this course, you will be able to:

independently build machine learning and predictive analytics models

confidently appear for exploratory data analysis, foundational data science, python interviews

demonstrate mastery in exploratory data science and python

demonstrate mastery in logistic and linear regression, the workhorses of data science

This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications.

Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well. In addition, concepts of overfitting, regularization etc., are discussed in detail. These fundamental understandings are crucial as these can be applied to almost every machine learning method.

This course also provides an understanding of the industry standards, best practices for formulating, applying and maintaining data-driven solutions. It starts with a basic explanation of Machine Learning concepts and how to set up your environment. Next, data wrangling and EDA with Pandas are discussed with hands-on examples. Next, linear and logistic regression is discussed in detail and applied to solve real industry problems. Learning the industry standard best practices and evaluating the models for sustained development comes next.

Final learnings are around some of the core challenges and how to tackle them in an industry setup. This course supplies in-depth content that put the theory into practice.

Course Curriculum

Chapter 1: Working with Machine Learning

Lecture 1: Exploring Machine Learning and its Types

Lecture 2: Install Anaconda

Lecture 3: Python and Jupyter Demo

Chapter 2: Understanding Data Wrangling

Lecture 1: Introduction

Lecture 2: Reading from a CSV

Lecture 3: Selecting data and finding the most common complaint type

Lecture 4: Which borough has the most noise complaints?

Lecture 5: Which weekday do people bike the most?

Lecture 6: Which month was the snowiest?

Lecture 7: Cleaning Messy Data

Lecture 8: How to deal with timestamps

Lecture 9: Loading data from SQL databases

Lecture 10: Summary

Chapter 3: Linear Regression

Lecture 1: Introduction

Lecture 2: What is linear regression?

Lecture 3: The advertising dataset

Lecture 4: EDA questions on advertising data

Lecture 5: Simple Linear Regression

Lecture 6: Hypothesis testing and p-values

Lecture 7: R squared

Lecture 8: Multiple linear regression

Lecture 9: Model and feature selection

Lecture 10: Model evaluation

Lecture 11: Handling categorical features

Lecture 12: Summary

Chapter 4: Logistic Regression

Lecture 1: Introduction

Lecture 2: Predicting a continuous response

Lecture 3: Quick refresher on linear regression

Lecture 4: Predicting a categorical response

Lecture 5: Using logistic regression

Lecture 6: Probability, odds, log-odds

Lecture 7: What is logistic regression?

Lecture 8: Interpreting logistic regression

Lecture 9: Using logistic regression with categorical features

Lecture 10: Summary

Chapter 5: Cross Validation

Lecture 1: Introduction

Lecture 2: Train/test split

Lecture 3: K-fold cross-validation

Lecture 4: Cross-validation continued

Lecture 5: Summary

Chapter 6: Regularization

Lecture 1: Introduction

Lecture 2: Overfitting

Lecture 3: Overfitting with linear models

Lecture 4: Regularizing linear models

Lecture 5: Ridge and Lasso Regularization

Lecture 6: Regularization using scikit-learn

Lecture 7: Regularizing logistic models

Lecture 8: Pipeline and GridSearchCV

Lecture 9: Comparing regularized with unregularized models

Instructors

UNP United Network of Professionals
Publishing top-notch data science learning materials

Rating Distribution

1 stars: 8 votes

2 stars: 5 votes

3 stars: 34 votes

4 stars: 57 votes

5 stars: 59 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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