Data Science and Machine Learning using Python – A Bootcamp

Data Science and Machine Learning using Python – A Bootcamp

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Overview

Data scientist is the most demanding job right now in the UK. The demand for the job is rising to 231%, almost tripled over the last five years. Become a Data Science specialist with this comprehensive Data Science and Machine Learning course. This Data Science and Machine Learning using Python course is designed by the industry experts and aims to equip you with all the essential knowledge and skills to become a successful Data Scientist. 

In this comprehensive Data Science and Machine Learning using Python course, you will get in-depth knowledge and understanding of Panda data structures, basic python programming for data science and machine learning, K Mean clustering, bootstrap, bagging, SciKit-Learn. arrays, built-in methods, array methods and attributes and much more. 

By the end of this Data Science and Machine learning course you will develop strong solid understanding and working knowledge of data science concepts using python. You will be able to complete more faster than before with clear understanding on data science and machine learning. The accredited certification from this course will enhance the value of your CV and make you worthy in the job market. So, enrol in the Data Science and Machine learning course today and enter the amazing world of data science.

Highlights of the Data Science and Machine Learning using Python course:             

  • Develop your understanding on Python to analyse data
  • Learn how to use Python to create visualisation and machine learning for informed decision making. 
  • Understand Numpy to facilitate numeric data 
  • Learn how to use Pandas for data analysis 
  • Learn key concept and strategies of Plotting for data analysis 
  • Understand Skit- Learn and how to implement it for machine learning 
  • Get clear understanding of natural language processing and spam filters 
  • Explore Plotty and learn how to make interactive dynamic visualizations 

 

Course design

The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

You are taught through a combination of

  • Online study materials
  • Mock exams
  • Multiple-choice assessment

How is the course assessed?

To successfully complete the course you must pass an automated, multiple-choice assessment. The assessment is delivered through our online learning platform. You will receive the results of your assessment immediately upon completion.

Will I receive a certificate of completion?

Upon successful completion, you will qualify for the UK and internationally-recognised professional qualification and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £9 and Hardcopy Certificate for £15.

Why study this course

Whether you’re an existing practitioner or aspiring professional, this course will enhance your expertise and boost your CV with key skills and an accredited qualification attesting to your knowledge.

The Data Science and Machine Learning using Python course is open to all, with no formal entry requirements. All you need is a passion for learning, a good understanding of the English language, numeracy and IT, and to be over the age of 16.

Who is this Electricity Course for?

This Data Science course is suitable for anyone who is interested in the trade of data science and machine learning and wants to have a better understanding of the fundamentals. It is also great for working professionals who have practical knowledge but require a theoretical understanding of electricity along with a CPD accredited certification to boost up his resume. 

Requirements

There are no specific prerequisites to attend this course, as it is designed as an introductory course for anyone interested in the trade. All the modules are fully accessible from any internet-enabled smart device.

Career path

This course will boost up your resume if you are looking towards a career as an electrician or want to acquire extensive knowledge to work in the electrical engineering field. The certificate you will obtain is CPD accredited, it will help you stand out in the ever-competitive job market from your peers.

Course Curriculum

Welcome, Course Introduction & overview, and Environment set-up
Welcome & Course Overview 00:07:00
Set-up the Environment for the Course (lecture 1) 00:09:00
Set-up the Environment for the Course (lecture 2) 00:25:00
Two other options to setup environment 00:04:00
Python Essentials
Python data types Part 1 00:21:00
Python Data Types Part 2 00:15:00
Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00
Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00
Python Essentials Exercises Overview 00:02:00
Python Essentials Exercises Solutions 00:22:00
Python for Data Analysis using NumPy
What is Numpy? A brief introduction and installation instructions. 00:03:00
NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes. 00:28:00
NumPy Essentials – Indexing, slicing, broadcasting & boolean masking 00:26:00
NumPy Essentials – Arithmetic Operations & Universal Functions 00:07:00
NumPy Essentials Exercises Overview 00:02:00
NumPy Essentials Exercises Solutions 00:25:00
Python for Data Analysis using Pandas
What is pandas? A brief introduction and installation instructions. 00:02:00
Pandas Introduction 00:02:00
Pandas Essentials – Pandas Data Structures – Series 00:20:00
Pandas Essentials – Pandas Data Structures – DataFrame 00:30:00
Pandas Essentials – Handling Missing Data 00:12:00
Pandas Essentials – Data Wrangling – Combining, merging, joining 00:20:00
Pandas Essentials – Groupby 00:10:00
Pandas Essentials – Useful Methods and Operations 00:26:00
Pandas Essentials – Project 1 (Overview) Customer Purchases Data 00:08:00
Pandas Essentials – Project 1 (Solutions) Customer Purchases Data 00:31:00
Pandas Essentials – Project 2 (Overview) Chicago Payroll Data 00:04:00
Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00
Python for Data Visualization using matplotlib
Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach 00:13:00
Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach 00:22:00
Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach 00:22:00
Matplotlib Essentials – Exercises Overview 00:06:00
Matplotlib Essentials – Exercises Solutions 00:21:00
Python for Data Visualization using Seaborn
Seaborn – Introduction & Installation 00:04:00
Seaborn – Distribution Plots 00:25:00
Seaborn – Categorical Plots (Part 1) 00:21:00
Seaborn – Categorical Plots (Part 2) 00:16:00
Seborn-Axis Grids 00:25:00
Seaborn – Matrix Plots 00:13:00
Seaborn – Regression Plots 00:11:00
Seaborn – Controlling Figure Aesthetics 00:10:00
Seaborn – Exercises Overview 00:04:00
Seaborn – Exercise Solutions 00:19:00
Python for Data Visualization using pandas
Pandas Built-in Data Visualization 00:34:00
Pandas Data Visualization Exercises Overview 00:03:00
Panda Data Visualization Exercises Solutions 00:13:00
Python for interactive & geographical plotting using Plotly and Cufflinks
Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1) 00:19:00
Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2) 00:14:00
Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview) 00:11:00
Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions) 00:37:00
Capstone Project - Python for Data Analysis & Visualization
Project 1 – Oil vs Banks Stock Price during recession (Overview) 00:15:00
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00
Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview) 00:03:00
Python for Machine Learning (ML) - scikit-learn - Linear Regression Model
Introduction to ML – What, Why and Types….. 00:15:00
Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00
scikit-learn – Linear Regression Model – Hands-on (Part 1) 00:17:00
scikit-learn – Linear Regression Model Hands-on (Part 2) 00:19:00
Good to know! How to save and load your trained Machine Learning Model! 00:01:00
scikit-learn – Linear Regression Model (Insurance Data Project Overview) 00:08:00
scikit-learn – Linear Regression Model (Insurance Data Project Solutions) 00:30:00
Python for Machine Learning - scikit-learn - Logistic Regression Model
Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. 00:10:00
scikit-learn – Logistic Regression Model – Hands-on (Part 1) 00:17:00
scikit-learn – Logistic Regression Model – Hands-on (Part 2) 00:20:00
scikit-learn – Logistic Regression Model – Hands-on (Part 3) 00:11:00
scikit-learn – Logistic Regression Model – Hands-on (Project Overview) 00:05:00
scikit-learn – Logistic Regression Model – Hands-on (Project Solutions) 00:15:00
Python for Machine Learning - scikit-learn - K Nearest Neighbors
Theory: K Nearest Neighbors, Curse of dimensionality …. 00:08:00
scikit-learn – K Nearest Neighbors – Hands-on 00:25:00
scikt-learn – K Nearest Neighbors (Project Overview) 00:04:00
scikit-learn – K Nearest Neighbors (Project Solutions) 00:14:00
Python for Machine Learning - scikit-learn - Decision Tree and Random Forests
Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. 00:18:00
scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1) 00:19:00
scikit-learn – Decision Tree and Random Forests (Project Overview) 00:05:00
scikit-learn – Decision Tree and Random Forests (Project Solutions) 00:15:00
Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)
Support Vector Machines (SVMs) – (Theory Lecture) 00:07:00
scikit-learn – Support Vector Machines – Hands-on (SVMs) 00:30:00
scikit-learn – Support Vector Machines (Project 1 Overview) 00:07:00
scikit-learn – Support Vector Machines (Project 1 Solutions) 00:20:00
scikit-learn – Support Vector Machines (Optional Project 2 – Overview) 00:02:00
Python for Machine Learning - scikit-learn - K Means Clustering
Theory: K Means Clustering, Elbow method ….. 00:11:00
scikit-learn – K Means Clustering – Hands-on 00:23:00
scikit-learn – K Means Clustering (Project Overview) 00:07:00
scikit-learn – K Means Clustering (Project Solutions) 00:22:00
Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)
Theory: Principal Component Analysis (PCA) 00:09:00
scikit-learn – Principal Component Analysis (PCA) – Hands-on 00:22:00
scikit-learn – Principal Component Analysis (PCA) – (Project Overview) 00:02:00
scikit-learn – Principal Component Analysis (PCA) – (Project Solutions) 00:17:00
Recommender Systems with Python - (Additional Topic)
Theory: Recommender Systems their Types and Importance 00:06:00
Python for Recommender Systems – Hands-on (Part 1) 00:18:00
Python for Recommender Systems – – Hands-on (Part 2) 00:19:00
Python for Natural Language Processing (NLP) - NLTK - (Additional Topic)
Natural Language Processing (NLP) – (Theory Lecture) 00:13:00
NLTK – NLP-Challenges, Data Sources, Data Processing ….. 00:13:00
NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00
NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW…. 00:19:00
NLTK – BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … 00:13:00
NLTK – NLP – Pipeline feature to assemble several steps for cross-validation… 00:09:00

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