Learn Python for Data Science Machine Learning from A-Z

Learn Python for Data Science & Machine Learning from A-Z, available at $49.99, has an average rating of 4.32, with 140 lectures, based on 1766 reviews, and has 112546 subscribers.

You will learn about Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant Learn data cleaning, processing, wrangling and manipulation How to create resume and land your first job as a Data Scientist How to use Python for Data Science How to write complex Python programs for practical industry scenarios Learn Plotting in Python (graphs, charts, plots, histograms etc) Learn to use NumPy for Numerical Data Machine Learning and its various practical applications Supervised vs Unsupervised Machine Learning Learn Regression, Classification, Clustering and Sci-kit learn Machine Learning Concepts and Algorithms K-Means Clustering Use Python to clean, analyze, and visualize data Building Custom Data Solutions Statistics for Data Science Probability and Hypothesis Testing This course is ideal for individuals who are Students who want to learn about Python for Data Science & Machine Learning It is particularly useful for Students who want to learn about Python for Data Science & Machine Learning.

Enroll now: Learn Python for Data Science & Machine Learning from A-Z

Summary

Title: Learn Python for Data Science & Machine Learning from A-Z

Price: $49.99

Average Rating: 4.32

Number of Lectures: 140

Number of Published Lectures: 140

Number of Curriculum Items: 140

Number of Published Curriculum Objects: 140

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will Learn

Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant

Learn data cleaning, processing, wrangling and manipulation

How to create resume and land your first job as a Data Scientist

How to use Python for Data Science

How to write complex Python programs for practical industry scenarios

Learn Plotting in Python (graphs, charts, plots, histograms etc)

Learn to use NumPy for Numerical Data

Machine Learning and its various practical applications

Supervised vs Unsupervised Machine Learning

Learn Regression, Classification, Clustering and Sci-kit learn

Machine Learning Concepts and Algorithms

K-Means Clustering

Use Python to clean, analyze, and visualize data

Building Custom Data Solutions

Statistics for Data Science

Probability and Hypothesis Testing

Who Should Attend

Students who want to learn about Python for Data Science & Machine Learning

Target Audiences

Students who want to learn about Python for Data Science & Machine Learning

Learn Python for Data Science & Machine Learning from A-Z

In this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner.

Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.

We’ll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +

NumPy —  A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.

Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.

NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.

This Machine Learning with Python course dives into the basics of machine learning using Python. You’ll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!

Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

Together we’re going to give you the foundational education that you need to know not just on how to write code in Python, analyze and visualize data and utilize machine learning algorithms but also how to get paid for your newly developed programming skills.

The course covers 5 main areas:

1: PYTHON FOR DS+ML COURSE INTRO

This intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles.

Intro to Data Science + Machine Learning with Python

Data Science Industry and Marketplace

Data Science Job Opportunities

How To Get a Data Science Job

Machine Learning Concepts & Algorithms

2: PYTHON DATA ANALYSIS/VISUALIZATION

This section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.

Python Crash Course

NumPy Data Analysis

Pandas Data Analysis

3: MATHEMATICS FOR DATA SCIENCE

This section gives you a full introduction to the mathematics for data science such as statistics and probability.

Descriptive Statistics

Measure of Variability

Inferential Statistics

Probability

Hypothesis Testing

4:  MACHINE LEARNING

This section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.

Intro to Machine Learning

Data Preprocessing

Linear Regression

Logistic Regression

K-Nearest Neighbors

Decision Trees

Ensemble Learning

Support Vector Machines

K-Means Clustering

PCA

5: STARTING A DATA SCIENCE CAREER

This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.

Creating a Resume

Creating a Cover Letter

Personal Branding

Freelancing + Freelance websites

Importance of Having a Website

Networking

By the end of the course you’ll be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Who is This Course For?

Lecture 2: Data Science + Machine Learning Marketplace

Lecture 3: Data Science Job Opportunities

Lecture 4: Data Science Job Roles

Lecture 5: What is a Data Scientist?

Lecture 6: How To Get a Data Science Job

Lecture 7: Data Science Projects Overview

Chapter 2: Data Science & Machine Learning Concepts

Lecture 1: Why We Use Python?

Lecture 2: What is Data Science?

Lecture 3: What is Machine Learning?

Lecture 4: Machine Learning Concepts & Algorithms

Lecture 5: What is Deep Learning?

Lecture 6: Machine Learning vs Deep Learning

Chapter 3: Python For Data Science

Lecture 1: What is Programming?

Lecture 2: Why Python for Data Science?

Lecture 3: What is Jupyter?

Lecture 4: What is Google Colab?

Lecture 5: Python Variables, Booleans and None

Lecture 6: Getting Started with Google Colab

Lecture 7: Python Operators

Lecture 8: Python Numbers & Booleans

Lecture 9: Python Strings

Lecture 10: Python Conditional Statements

Lecture 11: Python For Loops and While Loops

Lecture 12: Python Lists

Lecture 13: More about Lists

Lecture 14: Python Tuples

Lecture 15: Python Dictionaries

Lecture 16: Python Sets

Lecture 17: Compound Data Types & When to use each one?

Lecture 18: Python Functions

Lecture 19: Object Oriented Programming in Python

Chapter 4: Statistics for Data Science

Lecture 1: Intro To Statistics

Lecture 2: Descriptive Statistics

Lecture 3: Measure of Variability

Lecture 4: Measure of Variability Continued

Lecture 5: Measures of Variable Relationship

Lecture 6: Inferential Statistics

Lecture 7: Measure of Asymmetry

Lecture 8: Sampling Distribution

Chapter 5: Probability & Hypothesis Testing

Lecture 1: What Exactly is Probability?

Lecture 2: Expected Values

Lecture 3: Relative Frequency

Lecture 4: Hypothesis Testing Overview

Chapter 6: NumPy Data Analysis

Lecture 1: Intro NumPy Array Data Types

Lecture 2: NumPy Arrays

Lecture 3: NumPy Arrays Basics

Lecture 4: NumPy Array Indexing

Lecture 5: NumPy Array Computations

Lecture 6: Broadcasting

Chapter 7: Pandas Data Analysis

Lecture 1: Introduction to Pandas

Lecture 2: Introduction to Pandas Continued

Chapter 8: Python Data Visualization

Lecture 1: Data Visualization Overview

Lecture 2: Different Data Visualization Libraries in Python

Lecture 3: Python Data Visualization Implementation

Chapter 9: Machine Learning

Lecture 1: Introduction To Machine Learning

Chapter 10: Data Loading & Exploration

Lecture 1: Exploratory Data Analysis

Chapter 11: Data Cleaning

Lecture 1: Feature Scaling

Lecture 2: Data Cleaning

Chapter 12: Feature Selecting and Engineering

Lecture 1: Feature Engineering

Chapter 13: Linear and Logistic Regression

Lecture 1: Linear Regression Intro

Lecture 2: Gradient Descent

Lecture 3: Linear Regression + Correlation Methods

Lecture 4: Linear Regression Implementation

Lecture 5: Logistic Regression

Chapter 14: K Nearest Neighbors

Lecture 1: KNN Overview

Lecture 2: parametric vs non-parametric models

Lecture 3: EDA on Iris Dataset

Lecture 4: The KNN Intuition

Lecture 5: Implement the KNN algorithm from scratch

Lecture 6: Compare the result with the sklearn library

Lecture 7: Hyperparameter tuning using the cross-validation

Lecture 8: The decision boundary visualization

Lecture 9: Manhattan vs Euclidean Distance

Lecture 10: Feature scaling in KNN

Lecture 11: Curse of dimensionality

Lecture 12: KNN use cases

Lecture 13: KNN pros and cons

Chapter 15: Decision Trees

Lecture 1: Decision Trees Section Overview

Lecture 2: EDA on Adult Dataset

Lecture 3: What is Entropy and Information Gain?

Lecture 4: The Decision Tree ID3 algorithm from scratch Part 1

Lecture 5: The Decision Tree ID3 algorithm from scratch Part 2

Lecture 6: The Decision Tree ID3 algorithm from scratch Part 3

Lecture 7: ID3 – Putting Everything Together

Instructors

Juan E. Galvan
Digital Entrepreneur | Business Coach

Ahmed Wael
Python Instructor | ML Engineer | University TA | Freelancer

Rating Distribution

1 stars: 24 votes

2 stars: 33 votes

3 stars: 215 votes

4 stars: 641 votes

5 stars: 853 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!