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Python - Data Science and Machine Learning A-Z using Python Bootcamp
Academy of Skills

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Summary

Price
£12 inc VAT
Study method
Online, On Demand
Duration
25 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Certificate of completion - Free
  • Reed courses certificate of completion - Free
Additional info
  • Tutor is available to students
1 student purchased this course

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Overview

Do you wish you could learn a new skill set in order to get that Python - Data Science and Machine Learning A-Z using Python Bootcamp job? Do you wish to improve and monetize your Python - Data Science and Machine Learning A-Z using Python Bootcamp current abilities? Do you want to take a professionally created, industry-relevant course that you can take at any time and from any location? Continue reading!

Our teachers present the skills and frameworks that assist learners to overcome the relevant subject matters in this Python - Data Science and Machine Learning A-Z using Python Bootcamp Course. The entire Python - Data Science and Machine Learning A-Z using Python Bootcamp course is jam-packed with all of the necessary insights and examples from the theoretical and practical parts of the relevant subject; also, this Python - Data Science and Machine Learning A-Z using Python Bootcamp course is created for any creative student who needs it.

Academy of Skills will provide all the resources and structure essential for students to pass all sections of this Python - Data Science and Machine Learning A-Z using Python Bootcamp course. You'll get access to a varied group of well-known academics and industry professionals. Furthermore, you will collaborate with a diverse group of students from across the world to address real-world challenges. To ensure that you flourish in your job, we have packed the whole Python - Data Science and Machine Learning A-Z using Python Bootcamp course with crucial insights and examples of both theoretical and practical elements of Python - Data Science and Machine Learning A-Z using Python Bootcamp.

This premium online Python - Data Science and Machine Learning A-Z using Python Bootcamp course ensures the growth of your professional skills while also providing international accreditation. All of the themes and subtopics in Python - Data Science and Machine Learning A-Z using Python Bootcamp are organised scientifically, taking into account the psychology of the learner and their total learning experience. The Python - Data Science and Machine Learning A-Z using Python Bootcamp modules are all simple to comprehend, interactive, and bite-sized. You will be able to learn Python - Data Science and Machine Learning A-Z using Python Bootcamp at your own speed, from any location, using any device that is appropriate. The Academy of Skills offers an internationally recognized certification for this Python - Data Science and Machine Learning A-Z using Python Bootcamp course.

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Certificates

Certificate of completion

Digital certificate - Included

Please contact the Academy of Skills team via email to claim your free certificate.

Reed courses certificate of completion

Digital certificate - Included

Will be downloadable when all lectures have been completed

Curriculum

19
sections
111
lectures
25h
total

    • 1: Welcome & Course Overview 02:25
    • 2: Tips DS 00:40
    • 3: Course Introduction 07:21
    • 4: Set-up the Environment for the Course (lecture 1) 09:10
    • 5: Set-up the Environment for the Course (lecture 2) 25:19
    • 6: Download environment file and watch next lecture to setup -- super easy way 00:14
    • 7: Two other options to setup environment 03:34
    • 8: Important Note: 00:18
    • 9: Possible updates in the course 00:24

    • 10: Python data types Part 1 20:57
    • 11: Python Data Types Part 2 14:36
    • 12: Comparisons Operators, if, else, elif statement 12:18
    • 13: Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 15:35
    • 14: Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 20:05
    • 15: Python Essentials Exercises Overview 02:04
    • 16: Python Essentials Exercises Solutions 21:56

    • 17: What is Numpy? A brief introduction and installation instructions 03:04
    • 18: NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes 27:45
    • 19: NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 26:27
    • 20: NumPy Essentials - Arithmetic Operations & Universal Functions 07:18
    • 21: NumPy Essentials Exercises Overview 02:16
    • 22: NumPy Essentials Exercises Solutions 25:06

    • 23: What is pandas? A brief introduction and installation instructions 01:44
    • 24: Pandas Introduction 02:10
    • 25: Pandas Essentials - Pandas Data Structures - Series 20:15
    • 26: Pandas Essentials - Pandas Data Structures - DataFrame 29:47
    • 27: Pandas Essentials - Hierarchical Indexing 14:11
    • 28: Pandas Essentials - Handling Missing Data 11:59
    • 29: Pandas Essentials - Data Wrangling - Combining, merging, joining 20:14
    • 30: Pandas Essentials - Groupby 10:16
    • 31: Pandas Essentials - Useful Methods and Operations 26:18
    • 32: Pandas Essentials - Project 1 (Overview) Customer Purchases Data 08:29
    • 33: Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 30:44
    • 34: Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 04:25
    • 35: Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 17:36
    • 36: Pandas Essentials - Project 2 (Solutions Part 2) Chicago Payroll Data 18:10

    • 37: Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 13:13
    • 38: Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 22:28
    • 39: Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 21:36
    • 40: Matplotlib Essentials - Exercises Overview 05:45
    • 41: Matplotlib Essentials - Exercises Solutions 20:53
    • 42: Matplotlib Essentials (Optional) - Advance 00:18

    • 43: Seaborn - Introduction & Installation 03:37
    • 44: Seaborn - Distribution Plots 25:20
    • 45: Seaborn - Categorical Plots (Part 1) 20:50
    • 46: Seaborn - Categorical Plots (Part 2) 15:31
    • 47: Seaborn - Axis Grids 25:04
    • 48: Seaborn - Matrix Plots 13:25
    • 49: Seaborn - Regression Plots 11:29
    • 50: Seaborn - Controlling Figure Aesthetics 10:26
    • 51: Seaborn - Exercises Overview 04:15
    • 52: Seaborn - Exercise Solutions 18:58

    • 53: Pandas Built-in Data Visualization 33:32
    • 54: Pandas Data Visualization Exercises Overview 03:11
    • 55: Panda Data Visualization Exercises Solutions 13:10

    • 56: Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 19:22
    • 57: Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 13:36
    • 58: Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 10:32
    • 59: Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 36:58

    • 60: Project 1 - Oil vs Banks Stock Price during recession (Overview) 14:31
    • 61: Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 17:40
    • 62: Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 18:28
    • 63: Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 16:44
    • 64: Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 02:32

    • 65: Introduction to ML - What, Why and Types..... 14:43
    • 66: Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 14:40
    • 67: A note on student’s concerns and questions on FutureWarnings. 00:26
    • 68: scikit-learn - Linear Regression Model - Hands-on (Part 1) 17:05
    • 69: scikit-learn - Linear Regression Model Hands-on (Part 2) 19:25
    • 70: Good to know! How to save and load your trained Machine Learning Model! 01:02
    • 71: scikit-learn - Linear Regression Model (Insurance Data Project Overview) 08:23
    • 72: scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 29:47

    • 73: Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity...etc. 10:26
    • 74: Output of classification report in scikit-learn — A small change 00:30
    • 75: scikit-learn - Logistic Regression Model - Hands-on (Part 1) 16:55
    • 76: scikit-learn - Logistic Regression Model - Hands-on (Part 2) 19:46
    • 77: scikit-learn - Logistic Regression Model - Hands-on (Part 3) 11:23
    • 78: scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 04:43
    • 79: scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 14:35

    • 80: Theory: K Nearest Neighbors, Curse of dimensionality .... 08:22
    • 81: scikit-learn - K Nearest Neighbors - Hands-on 24:53
    • 82: scikt-learn - K Nearest Neighbors (Project Overview) 04:05
    • 83: scikit-learn - K Nearest Neighbors (Project Solutions) 13:33

    • 84: Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.... 17:57
    • 85: scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 18:40
    • 86: scikit-learn - Decision Tree and Random Forests (Project Overview) 04:49
    • 87: scikit-learn - Decision Tree and Random Forests (Project Solutions) 15:26

    • 88: Support Vector Machines (SVMs) - (Theory Lecture) 06:32
    • 89: scikit-learn - Support Vector Machines - Hands-on (SVMs) 30:14
    • 90: scikit-learn - Support Vector Machines (Project 1 Overview) 06:47
    • 91: scikit-learn - Support Vector Machines (Project 1 Solutions) 20:16
    • 92: scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 01:48

    • 93: Theory: K Means Clustering, Elbow method ..... 11:04
    • 94: scikit-learn - K Means Clustering - Hands-on 23:24
    • 95: scikit-learn - K Means Clustering (Project Overview) 07:16
    • 96: scikit-learn - K Means Clustering (Project Solutions) 22:00

    • 97: Theory: Principal Component Analysis (PCA) 09:13
    • 98: scikit-learn - Principal Component Analysis (PCA) - Hands-on 22:01
    • 99: scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 01:33
    • 100: scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 17:11

    • 101: Theory: Recommender Systems their Types and Importance 05:38
    • 102: Python for Recommender Systems - Hands-on (Part 1) 17:56
    • 103: Python for Recommender Systems - - Hands-on (Part 2) 19:01

    • 104: Natural Language Processing (NLP) - (Theory Lecture) 12:46
    • 105: NLTK - NLP-Challenges, Data Sources, Data Processing ..... 13:18
    • 106: NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 18:37
    • 107: NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.... 18:41
    • 108: NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes ... 13:03
    • 109: NLTK - NLP - Pipeline feature to assemble several steps for cross-validation... 09:04

    • 110: Please read, it's important! 00:39
    • 111: Thanks you for doing the course! 00:56

Course media

Description

With the help and knowledge of industry specialists, this novel Python - Data Science and Machine Learning A-Z using Python Bootcamp course has been put together. Python - Data Science and Machine Learning A-Z using Python Bootcamp has been meticulously created to fulfill the learning needs that will enable you to make a significant contribution to the area and carve out a successful career path.

Python - Data Science and Machine Learning A-Z using Python Bootcamp course was created to help motivated students become the best in their personal and professional lives. Many students have already completed and enjoyed this Python - Data Science and Machine Learning A-Z using Python Bootcamp course. This Python - Data Science and Machine Learning A-Z using Python Bootcamp education gave them the tools they needed to advance to more gratifying and rewarding jobs. This one-of-a-kind Python - Data Science and Machine Learning A-Z using Python Bootcamp course is suitable for devoted and ambitious learners who want to be the best at their career or profession.

With the help and knowledge of industry leaders, the original Python - Data Science and Machine Learning A-Z using Python Bootcamp was created. This Python - Data Science and Machine Learning A-Z using Python Bootcamp has been meticulously created to suit all of the learning criteria for making a significant contribution to the associated subject and subsequent career path. By participating in this Python - Data Science and Machine Learning A-Z using Python Bootcamp course, the student will receive valuable knowledge and skills that will help them land their ideal career and establish a strong personal and professional reputation.

After enrolling in this Python - Data Science and Machine Learning A-Z using Python Bootcamp course, you may use our tutor's assistance to help you with any questions you may have, which you can send to our learner support staff through email. This Python - Data Science and Machine Learning A-Z using Python Bootcamp is one of our most popular online courses, created by professionals for the future-focused professional and designed to provide learners with the tools and frameworks they need to lead successfully in a fast changing environment.

Enroll in the Python - Data Science and Machine Learning A-Z using Python Bootcamp right now to advance your abilities.

Curriculum

Course Curriculum: Python - Data Science and Machine Learning A-Z using Python Bootcamp

Here is a curriculum breakdown of the Python - Data Science and Machine Learning A-Z using Python Bootcamp course:

  • Welcome, Course Introduction & overview, and Environment set-up
  • Python Essentials
  • Python for Data Analysis using NumPy
  • Python for Data Analysis using Pandas
  • Python for Data Visualization using matplotlib
  • Python for Data Visualization using Seaborn
  • Python for Data Visualization using pandas
  • Python for interactive & geographical plotting using Plotly and Cufflinks
  • Capstone Project - Python for Data Analysis & Visualization
  • Python for Machine Learning (ML) - scikit-learn - Linear Regression Model
  • Python for Machine Learning - scikit-learn - Logistic Regression Model
  • Python for Machine Learning - scikit-learn - K Nearest Neighbors
  • Python for Machine Learning - scikit-learn - Decision Tree and Random Forests
  • Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)
  • Python for Machine Learning - scikit-learn - K Means Clustering
  • Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)
  • Recommender Systems with Python - (Additional Topic)
  • Python for Natural Language Processing (NLP) - NLTK - (Additional Topic)
  • Thanks you and closing remarks

Certificate

Learners can request a free PDF certificate of completion after successfully completing the Python - Data Science and Machine Learning A-Z using Python Bootcamp course. An additional fee may be charged for Python - Data Science and Machine Learning A-Z using Python Bootcamp Hardcopy Certificate and includes Free Shipping in the UK.

Who is this course for?

The Python - Data Science and Machine Learning A-Z using Python Bootcamp training course is ideal for highly driven students who wish to improve your personal and professional abilities while also preparing for the career of their dreams! This Python - Data Science and Machine Learning A-Z using Python Bootcamp is also great for persons who want to learn more about this topic in depth and appreciate being up to speed on the newest facts and expertise.

Requirements

  • There are no official requirements for Python - Data Science and Machine Learning A-Z using Python Bootcamp
  • Python - Data Science and Machine Learning A-Z using Python Bootcamp requires basic Internet connection
  • Python - Data Science and Machine Learning A-Z using Python Bootcamp requires you to have access to a computer, tablet, or a mobile device
  • Knowledge of basic English

Career path

The Python - Data Science and Machine Learning A-Z using Python Bootcamp course is meant to prepare you for the job of your dreams, a promotion at work, or being self-employed and starting your own business. Courses from the Academy of Skills will help you enhance your profession and keep your skills current.

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Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
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