Data Science with Machine Learning with R + Python

This Data Science & Machine Learning 2 courses bundle is designed for professionals who want to thrive in their Data Science & Machine Learning profession. This bundle course covers all the essential Data Science & Machine Learning skills and knowledge needed to become specialised in Data Science sector. You will learn real-life knowledge and expertise from the industry experts and practitioners from this course bundle.

This 2 courses bundle consists of the following courses:

• Course 1: Data Science & Machine Learning with Python
• Course 2: Data Science & Machine Learning with R

This 2 course bundle starts with the basic knowledge of Data Science & Machine Learning. In this course, you will get a complete idea of Data Science & Machine Learning with key concepts, strategies regarding use of it and in-depth knowledge. This 2-course bundle is completely an online course. You can access this course from any part of the world with just a smart device and the internet.

By the end of this course, you will get complete knowledge and marketable skills. This course also comes with an Accredited certificate, which will add extra value to your resume and help you stand out in the job market. In short, This 2 course bundle is the perfect course to fast-track your career. So, why are you waiting? Enrol in this course today!

What you will learn :

From the Data Science & Machine Learning with Python course, you will learn

• Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
• How to create resume and land your first job as a Data Scientist
• How to write complex Python programs for practical industry scenarios
• Learn to use NumPy for Numerical Data
• Supervised vs Unsupervised Machine Learning
• Machine Learning Concepts and Algorithms
• Use Python to clean, analyze, and visualize data
• Statistics for Data Science
• Learn data cleaning, processing, wrangling and manipulation
• How to use Python for Data Science
• Learn Plotting in Python (graphs, charts, plots, histograms etc)
• Machine Learning and it's various practical applications
• Learn Regression, Classification, Clustering and Sci-kit learn
• K-Means Clustering
• Building Custom Data Solutions
• Probability and Hypothesis Testing

From the Data Science & Machine Learning with R course, you will learn

• Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
• How to write complex R programs for practical industry scenarios
• Learn data cleaning, processing, wrangling and manipulation
• Learn Plotting in R (graphs, charts, plots, histograms etc)
• How to create resume and land your first job as a Data Scientist
• Step by step practical knowledge of R programming language
• Learn Machine Learning and it's various practical applications
• Building web apps and online, interactive dashboards with R Shiny
• Learn Data and File Management in R
• Use R to clean, analyze, and visualize data
• Learn the Tidyverse
• Learn Operators, Vectors, Lists and their application
• Data visualization (ggplot2)
• Data extraction and web scraping
• Full-stack data science development
• Building custom data solutions
• Automating dynamic report generation
• Data science for business

Why Choose this 2-course bundle?

• This 2-course bundle is accredited by CPD
• This bundle is conducted by industry experts
• After completing this courses bundle you will get instant Free e-certificates
• Lifetime access to this course materials
• No hidden fees with this 2 course bundle course
• This bundle course is designed by professionals.
• Get 24/7 Instant Learning Assistance and Tutor Support with this course
• This course is developed by highly experienced industry experts.
• All required course materials for this 2 courses bundle course are included in the initial fee
• This bundle course is accessible through a smartphone, tablet, and Laptop.
• This bundle course is fully online, so you can access this course from anywhere and any part of the world.

And you will also get these gifts

• Free PDF Certificate
• 12 months Course Access

Certificate of completion

Digital certificate - Included

Course Curriculum

*** Course 1: Data Science & Machine Learning with Python ***

Introduction

• Who is This Course For?
• Data Science + Machine Learning Marketplace
• Data Science Job Opportunities
• Data Science Job Roles
• What is a Data Scientist?
• How To Get a Data Science Job
• Data Science Projects Overview

Data Science & Machine Learning Concepts

• Why We Use Python?
• What is Data Science?
• What is Machine Learning?
• Machine Learning Concepts & Algorithms
• What is Deep Learning?
• Machine Learning vs Deep Learning

Python For Data Science

• What is Programming?
• Why Python for Data Science?
• What is Jupyter?
• What is Google Colab?
• Python Variables, Booleans and None
• Getting Started with Google Colab
• Python Operators
• Python Numbers & Booleans
• Python Strings
• Python Conditional Statements
• Python For Loops and While Loops
• Python Lists
• More about Lists
• Python Tuples
• Python Dictionaries
• Python Sets
• Compound Data Types & When to use each one? 00:12:00
• Python Functions
• Object Oriented Programming in Python

Statistics for Data Science

• Intro To Statistics
• Descriptive Statistics
• Measure of Variability
• Measure of Variability Continued
• Measures of Variable Relationship
• Inferential Statistics
• Measure of Asymmetry
• Sampling Distribution

Probability & Hypothesis Testing

• What Exactly is Probability?
• Expected Values
• Relative Frequency
• Hypothesis Testing Overview

NumPy Data Analysis

• Intro NumPy Array Data Types
• NumPy Arrays in Data Science
• NumPy Arrays Basics in Data Science
• NumPy Array Indexing in Data Science
• NumPy Array Computations in Data Science

Pandas Data Analysis

• Introduction to Pandas in Data Science
• Introduction to Pandas Continued in Data Science

Python Data Visualization

• Data Visualization Overview in Data Science
• Different Data Visualization Libraries in Python
• Python Data Visualization Implementation in Data Science

Machine Learning

• Introduction To Machine Learning

Data Loading & Exploration

• Exploratory Data Analysis

Data Cleaning

• Feature Scaling
• Data Cleaning

Feature Selecting and Engineering

• Feature Engineering

Linear and Logistic Regression

• Linear Regression Intro
• Gradient Descent
• Linear Regression + Correlation Methods

K Nearest Neighbors

• KNN Overview
• parametric vs non-parametric models
• EDA on Iris Dataset
• The KNN Intuition
• Implement the KNN algorithm from scratch
• Compare the result with the sklearn library
• Hyperparameter tuning using the cross-validation
• The decision boundary visualization
• Manhattan vs Euclidean Distance

Decision Trees

• Decision Trees Section Overview
• EDA on Adult Dataset
• What is Entropy and Information Gain?
• The Decision Tree ID3 algorithm from scratch Part 1
• The Decision Tree ID3 algorithm from scratch Part 2
• The Decision Tree ID3 algorithm from scratch Part 3
• ID3 – Putting Everything Together
• Evaluating our ID3 implementation
• Compare with sklearn implementation
• Visualizing the tree
• Plot the features importance
• Decision Trees Hyper-parameters
• Pruning
• [Optional] Gain Ration
• Decision Trees Pros and Cons
• [Project] Predict whether income exceeds $50K/yr – Overview

Ensemble Learning and Random Forests

• Ensemble Learning Section Overview
• What is Ensemble Learning?
• What is Bootstrap Sampling?
• What is Bagging?
• Out-of-Bag Error (OOB Error)
• Implementing Random Forests from scratch Part 1
• Implementing Random Forests from scratch Part 2
• What is Boosting?
• AdaBoost Part 1
• AdaBoost Part 2

Support Vector Machines

• SVM Outline
• SVM intuition
• Hard vs Soft Margins
• C hyper-parameter
• Kernel Trick
• SVM – Kernel Types
• SVM with Linear Dataset (Iris)
• SVM with Non-linear Dataset
• SVM with Regression
• [Project] Voice Gender Recognition using SVM

K-means

• Unsupervised Machine Learning Intro
• Unsupervised Machine Learning Continued
• Data Standardization

PCA

• PCA Section Overview
• What is PCA?
• PCA Drawbacks
• PCA Algorithm Steps (Mathematics)
• Covariance Matrix vs SVD
• PCA – Main Applications
• PCA – Image Compression
• PCA Data Preprocessing
• PCA – Biplot and the Screen Plot
• PCA – Feature Scaling and Screen Plot
• PCA – Supervised vs Unsupervised
• PCA – Visualization

Data Science Career

• Creating A Data Science Resume
• Data Science Cover Letter
• How to Contact Recruiters
• Getting Started with Freelancing
• Top Freelance Websites
• Personal Branding
• Networking Do’s and Don’ts
• Importance of a Website

Course Curriculum

*** Course 2: Data Science & Machine Learning with R ***

Data Science and Machine Learning Course Intro

• Data Science and Machine Learning Intro Section Overview
• What is Data Science?
• Machine Learning Overview
• Data Science + Machine Learning Marketplace
• Who is This Course For?
• Data Science and Machine Learning Job Opportunities

Getting Started with R

• Getting Started with R in Data Science
• R Basics in Data Science
• Working with Files in Data Science
• R Studio in Data Science
• Tidyverse Overview in Data Science
• Additional Resources in Data Science

Data Types and Structures in R

• Data Types and Structures in R Section Overview
• Basic Types in Data Science
• Vectors Part One in Data Science
• Vectors Part Two in Data Science
• Vectors: Missing Values in Data Science
• Vectors: Coercion in Data Science
• Vectors: Naming in Data Science
• Vectors: Misc. in Data Science
• Working with Matrices in Data Science
• Working with Lists in Data Science

Intermediate R

• Intermedia R Section Introduction
• Relational Operators
• Logical Operators
• Conditional Statements
• Working with Loops
• Working with Functions
• Working with Packages

Data Manipulation in R

• Data Manipulation Section Intro
• Tidy Data in Data Science
• The Pipe Operator in Data Science
• {dplyr}: The Filter Verb in Data Science
• {dplyr}: The Select Verb in Data Science
• Web Scraping: {rvest} in Data Science
• JSON Parsing: {jsonlite} in Data Science

Data Visualization in R

• Data Visualization in R Section Intro

Creating Reports with R Markdown

• Introduction to R Markdown

Building Webapps with R Shiny

• Introduction to R Shiny

Introduction to Machine Learning

• Introduction to Machine Learning Part One

Data Preprocessing

• Data Preprocessing Intro

Linear Regression: A Simple Model

• Linear Regression: A Simple Model Intro

Exploratory Data Analysis

• Exploratory Data Analysis Intro

Linear Regression - A Real Model

• Linear Regression – Real Model Section Intro

Logistic Regression

• Introduction to Logistic Regression

Certificates:

Course Central is proud to offer a Certificate of Completion to all who complete courses successfully. Course Central tracks the learner’s course progress. However, the learner is responsible for validating the completion and understanding of the course. All Certificates of Completion can be validated from the Course Central website using the validation code.

Transcripts:

A Transcript for the course with completed module details can be requested for as little as £4.99. Please note that all course Certificates and Transcripts will be titled as published on the Course Central platform.

This bundle course is ideal for those who work in or aspire to work in the following professions:

• Data Scientist
• Data Analyst
• Software Developer
• Data Engineer
• Anyone who wants to learn Data Science & Machine Learning.

• Students should have basic computer skills
• Students would benefit from having prior Python & R Experience but not necessary

This training course will lead you to many different career opportunities, here are few prospects:

• Data Science Consultant - £50,000 to £100,000 per annum
• Data Science Manager - £80,000 to £90,000 per annum
• Data Science Analyst - £30,000 to £50,000 per annum