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Python For Data Science And Machine Learning Masterclass

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Python For Data Science And Machine Learning Masterclass, available at $19.99, has an average rating of 4.53, with 138 lectures, based on 51 reviews, and has 224 subscribers.

You will learn about Complete python masterclass from zero to Advanced Complete machine learning masterclass (zero to advanced level) Understand Python language basics and how they apply to data science. Practice iterative data science using Jupyter notebooks. You will be able to programe in Python Professionally Be able to use Python for data science and machine learning Analyze data using Python libraries like pandas and numpy. Create stunning data visualizations with matplotlib, folium, and seaborn. Build machine learning models using scipy and scikitlearn. Demonstrate proficiency in solving real life data science problems. This course is ideal for individuals who are Beginners with no previous programming experience looking to obtain the skills to get their first programming job. or Who want to improve their career options by learning the Machine learning. or Who want to improve their career options by learning the Python programming language. or Anyone looking to to build the minimum Python programming skills necessary as a pre-requisites for moving into machine learning, data science, and artificial intelligence. or Who want to learn complete machine learning concepts for academics and projects and real life solutions. or Who want to learn complete python concepts for academics or Who wants to create own predictive model based on machine learning. It is particularly useful for Beginners with no previous programming experience looking to obtain the skills to get their first programming job. or Who want to improve their career options by learning the Machine learning. or Who want to improve their career options by learning the Python programming language. or Anyone looking to to build the minimum Python programming skills necessary as a pre-requisites for moving into machine learning, data science, and artificial intelligence. or Who want to learn complete machine learning concepts for academics and projects and real life solutions. or Who want to learn complete python concepts for academics or Who wants to create own predictive model based on machine learning.

Enroll now: Python For Data Science And Machine Learning Masterclass

Summary

Title: Python For Data Science And Machine Learning Masterclass

Price: $19.99

Average Rating: 4.53

Number of Lectures: 138

Number of Published Lectures: 138

Number of Curriculum Items: 138

Number of Published Curriculum Objects: 138

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

  • Complete python masterclass from zero to Advanced
  • Complete machine learning masterclass (zero to advanced level)
  • Understand Python language basics and how they apply to data science.
  • Practice iterative data science using Jupyter notebooks.
  • You will be able to programe in Python Professionally
  • Be able to use Python for data science and machine learning
  • Analyze data using Python libraries like pandas and numpy.
  • Create stunning data visualizations with matplotlib, folium, and seaborn.
  • Build machine learning models using scipy and scikitlearn.
  • Demonstrate proficiency in solving real life data science problems.

Who Should Attend

  • Beginners with no previous programming experience looking to obtain the skills to get their first programming job.
  • Who want to improve their career options by learning the Machine learning.
  • Who want to improve their career options by learning the Python programming language.
  • Anyone looking to to build the minimum Python programming skills necessary as a pre-requisites for moving into machine learning, data science, and artificial intelligence.
  • Who want to learn complete machine learning concepts for academics and projects and real life solutions.
  • Who want to learn complete python concepts for academics
  • Who wants to create own predictive model based on machine learning.

Target Audiences

  • Beginners with no previous programming experience looking to obtain the skills to get their first programming job.
  • Who want to improve their career options by learning the Machine learning.
  • Who want to improve their career options by learning the Python programming language.
  • Anyone looking to to build the minimum Python programming skills necessary as a pre-requisites for moving into machine learning, data science, and artificial intelligence.
  • Who want to learn complete machine learning concepts for academics and projects and real life solutions.
  • Who want to learn complete python concepts for academics
  • Who wants to create own predictive model based on machine learning.

Data is at the heart of our digital economy and data science has been ranked as the hottest profession of the 21st century. Whether you are new to the job market or already in the workforce and looking to upskill yourself, this five course Data Science with Python Professional Certificate program is aimed at preparing you for a career in data science and machine learning. No prior computer programming experience required!

You will start by learning Python, the most popular language for data science. You will then develop skills for data analysis and data visualization and also get a practical introduction in machine learning. Finally, you will apply and demonstrate your knowledge of data science and machine learning with a capstone project involving a real life business problem.

This program is taught by experts and focused on hands-on learning and job readiness. As such you will work with real datasets and will be given no-charge access to tools like Jupyter notebooks in the IBM Cloud. You will utilize popular Python toolkits and libraries such as pandas, numpy, matplotlib, seaborn, folium, scipy, scikitlearn, and more.

Start developing data and analytical skills today and launch your career in data science!

This course is highly practical but it won’t neglect the theory. we’ll start with python basics, and then understand the complete concept of environment , variables , loops , conditions and more advance concept of python programming and machine learning and we install the needed software (on Windows, Linux and Mac OS X), then we’ll dive and start python programming straight away. From here onward you’ll learn everything by example, by analyzing and practicing different concepts such as operator, operand, conditional statements, looping ,data management .etc, so we’ll never have any boring dry theoretical lectures.

The course is divided into a number of sections, each section covers a complete python programming field and complete machine learning field, in each of these sections you’ll first learn basic python, and how to practicallyapply the concept of python on your lab, not only that but you’ll also learn how to apply python for data science and machine learning. By the end of the course you will have a strong foundation in most python programming and machine learning fields.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Promo video

Lecture 2: Understand Data Science

Lecture 3: Applications of DS

Lecture 4: Introduction to Python

Chapter 2: Installing notebook

Lecture 1: For windows

Lecture 2: For mac

Lecture 3: For Linux

Chapter 3: Lets Learn Python First

Lecture 1: Operators

Lecture 2: Implementation of Operators

Lecture 3: Variables and Variable naming conventions

Lecture 4: Implementation of Variables

Lecture 5: Conditional statement

Lecture 6: Implementation of Conditional statement

Lecture 7: Looping statements

Lecture 8: Implementation of Looping statements

Lecture 9: Functions

Lecture 10: Implementation of Function

Lecture 11: Data Structure

Lecture 12: Lists in python

Lecture 13: Implementation of List

Lecture 14: Dictionary in python

Lecture 15: Implementation of Dictionary

Lecture 16: Libraries in python

Lecture 17: Pandas in python

Lecture 18: Reading CSV files

Lecture 19: Dataframe in Python

Lecture 20: Implementation of Dataframe

Lecture 21: Indexing

Chapter 4: Let's Launch into Machine Learning

Lecture 1: Predictive Modeling

Lecture 2: Types of Predictive Models

Lecture 3: stages of Predictive Modeling

Lecture 4: Hypothesis Generation

Lecture 5: Data Extraction

Lecture 6: Data Exploration

Lecture 7: Reading the data into Python

Lecture 8: Reading the data into Python Implementation

Lecture 9: Variable Identification

Lecture 10: Implementation of Variable Identification

Lecture 11: Univariate analysis for Continuous Variables

Lecture 12: Implementation of Univariate Analysis for Continuous Variables

Lecture 13: Understanding Univariate Analysis for categorical variables

Lecture 14: Implementation of Univariate analysis for Categorical Variables

Lecture 15: Understanding Bivariate Analysis

Lecture 16: Implementation of Bivariate Analysis

Lecture 17: Understanding and treating missing values

Lecture 18: Implementation of Treating missing values

Lecture 19: Understanding Outlier Treatment

Lecture 20: Outlier Treatment in Python

Lecture 21: Understanding Variable Transformation

Lecture 22: Variable Transformation in Python

Lecture 23: Basics of Model Building

Lecture 24: Introduction to Problem Statement

Lecture 25: Data Manipulation

Lecture 26: Data Exploration – Bivariate

Lecture 27: Machine Learning Pipeline

Lecture 28: Preparing the Dataset

Lecture 29: Benchmark regression

Lecture 30: Regression implementation on notebook

Lecture 31: Classification Benchmark

Lecture 32: Classification Notebook

Lecture 33: Introduction to Evaluation Metrics

Lecture 34: Confusion Matrix

Lecture 35: Accuracy

Lecture 36: Alternatives of Accuracy

Lecture 37: Precision and Recall

Lecture 38: Thresholding

Lecture 39: AUC ROC

Lecture 40: Log loss

Lecture 41: Evaluation Metrics for regression Final

Lecture 42: R2 and Adjusted R2

Lecture 43: Introduction to kNN

Lecture 44: Building a kNN model

Lecture 45: Determining right value of k

Lecture 46: How to calculate distance

Lecture 47: Issue with distance based algorithms

Lecture 48: Introduction to sklearn

Lecture 49: Dealing with missing values and strings

Lecture 50: Implementing kNN Algorithm

Lecture 51: Introduction to Overfitting and Underfitting Models

Lecture 52: Visualizing overfitting and underfitting using knn

Lecture 53: Selecting the right Model

Lecture 54: What is Validation

Lecture 55: Understanding Hold-Out Validation

Lecture 56: Implementing Hold-out Validation

Lecture 57: Understanding k-fold cross validation

Lecture 58: Implementing k-fold cross validation

Lecture 59: Bias Variance Tradeoff

Lecture 60: Introduction to Linear Model

Lecture 61: Understanding Cost function

Lecture 62: Understanding Gradient descent

Lecture 63: Gradient Des in Linear Regression

Lecture 64: Convexity of cost function

Lecture 65: Assumptions of Linear Regression

Lecture 66: Implementing LInear Regression

Lecture 67: Introduction to Logistic Regression

Lecture 68: Odds ratio

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  • 5 stars: 41 votes

Frequently Asked Questions


How long do I have access to the course materials?


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


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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