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This Data Science & Machine Learning with Python course is designed for professionals who want to thrive in their profession. The course covers all the essential skills and knowledge needed to become specialised in this sector.

£21.00
Course Access

Unlimited Duration

Last Updated

May 7, 2022

Students Enrolled

361

Course Duration

23 hours

Course Instructor
Certification

What you will learn from Python for Data Science and Machine Learning course?

  • Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
  • How to create a 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 its various practical applications
  • Learn Regression, Classification, Clustering and Sci-kit learn
  • K-Means Clustering
  • Building Custom Data Solutions
  • Probability and Hypothesis Testing

Course Media

Who is this Python for Data Science and Machine Learning for?

This 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 with Python.
 

Why Choose Python for Data Science and Machine Learning course?

  • Accredited by CPD
  • Conducted by industry experts
  • Get Instant E-certificate
  • Fully online, interactive course with Professional voice-over
  • Developed by qualified professionals
  • Self-paced learning and laptop, tablet, smartphone-friendly
  • 24/7 Tutor Support
   

Certification

By the successful completion of the course, you will get an instant accredited e-certificate. Our courses are fully accredited with updated industry knowledge and skills that aim at making you an expert in the field. The hard copy of the certificate is also available for £4.99 and can be sent to your address. The delivery charge is applicable which will be £3.99.
 

Career Path

This training course will lead you to many different career opportunities, here are few prospects:
  • Python Software Engineer- £60,000 per annum
  • Data Scientist - £73,420 per annum
  • Professional Python Developer-£89,977 per annum

Course Curriculum

    • Who is This Course For? 00:03:00
    • Data Science + Machine Learning Marketplace 00:07:00
    • Data Science Job Opportunities 00:04:00
    • Data Science Job Roles 00:10:00
    • What is a Data Scientist? 00:17:00
    • How To Get a Data Science Job 00:18:00
    • Data Science Projects Overview 00:12:00
    • Why We Use Python? 00:03:00
    • What is Data Science? 00:13:00
    • What is Machine Learning? 00:14:00
    • Machine Learning Concepts & Algorithms 00:15:00
    • What is Deep Learning? 00:10:00
    • Machine Learning vs Deep Learning 00:11:00
    • What is Programming? 00:06:00
    • Why Python for Data Science? 00:05:00
    • What is Jupyter? 00:04:00
    • What is Google Colab? 00:03:00
    • Python Variables, Booleans and None 00:12:00
    • Getting Started with Google Colab 00:09:00
    • Python Operators 00:25:00
    • Python Numbers & Booleans 00:08:00
    • Python Strings 00:13:00
    • Python Conditional Statements 00:14:00
    • Python For Loops and While Loops 00:08:00
    • Python Lists 00:05:00
    • More about Lists 00:15:00
    • Python Tuples 00:11:00
    • Python Dictionaries 00:20:00
    • Python Sets 00:10:00
    • Compound Data Types & When to use each one? 00:13:00
    • Python Functions 00:14:00
    • Object Oriented Programming in Python 00:19:00
    • Intro To Statistics 00:07:00
    • Descriptive Statistics 00:07:00
    • Measure of Variability 00:12:00
    • Measure of Variability Continued 00:10:00
    • Measures of Variable Relationship 00:08:00
    • Inferential Statistics 00:15:00
    • Measure of Asymmetry 00:02:00
    • Sampling Distribution 00:08:00
    • What Exactly is Probability? 00:04:00
    • Expected Values 00:03:00
    • Relative Frequency 00:05:00
    • Hypothesis Testing Overview 00:09:00
    • Intro NumPy Array Data Types 00:12:00
    • NumPy Arrays 00:08:00
    • NumPy Arrays Basics 00:12:00
    • NumPy Array Indexing 00:09:00
    • NumPy Array Computations 00:06:00
    • Broadcasting 00:05:00
    • Introduction to Pandas 00:16:00
    • Introduction to Pandas Continued 00:18:00
    • Data Visualization Overview 00:25:00
    • Different Data Visualization Libraries in Python 00:13:00
    • Python Data Visualization Implementation 00:08:00
    • Introduction To Machine Learning 00:26:00
    • Exploratory Data Analysis 00:13:00
    • Feature Scaling 00:08:00
    • Data Cleaning 00:08:00
    • Feature Engineering 00:06:00
    • Linear Regression Intro 00:08:00
    • Gradient Descent 00:06:00
    • Linear Regression + Correlation Methods 00:27:00
    • Linear Regression Implementation 00:05:00
    • Logistic Regression 00:03:00
    • KNN Overview 00:03:00
    • parametric vs non-parametric models 00:03:00
    • EDA on Iris Dataset 00:22:00
    • The KNN Intuition 00:02:00
    • Implement the KNN algorithm from scratch 00:12:00
    • Compare the result with the sklearn library 00:04:00
    • Hyperparameter tuning using the cross-validation 00:11:00
    • The decision boundary visualization 00:05:00
    • Manhattan vs Euclidean Distance 00:11:00
    • Feature scaling in KNN 00:06:00
    • Curse of dimensionality 00:08:00
    • KNN use cases 00:04:00
    • KNN pros and cons 00:06:00
    • Decision Trees Section Overview 00:04:00
    • EDA on Adult Dataset 00:17:00
    • What is Entropy and Information Gain? 00:22:00
    • The Decision Tree ID3 algorithm from scratch Part 1 00:12:00
    • The Decision Tree ID3 algorithm from scratch Part 2 00:08:00
    • The Decision Tree ID3 algorithm from scratch Part 3 00:04:00
    • ID3 – Putting Everything Together 00:21:00
    • Evaluating our ID3 implementation 00:17:00
    • Compare with sklearn implementation 00:09:00
    • Visualizing the tree 00:10:00
    • Plot the features importance 00:06:00
    • Decision Trees Hyper-parameters 00:12:00
    • Pruning 00:17:00
    • [Optional] Gain Ration 00:03:00
    • Decision Trees Pros and Cons 00:08:00
    • [Project] Predict whether income exceeds $50K/yr – Overview 00:03:00
    • Ensemble Learning Section Overview 00:04:00
    • What is Ensemble Learning? 00:13:00
    • What is Bootstrap Sampling? 00:08:00
    • What is Bagging? 00:05:00
    • Out-of-Bag Error (OOB Error) 00:08:00
    • Implementing Random Forests from scratch Part 1 00:23:00
    • Implementing Random Forests from scratch Part 2 00:06:00
    • Compare with sklearn implementation 00:09:00
    • Random Forests Hyper-Parameters 00:04:00
    • Random Forests Pros and Cons 00:05:00
    • What is Boosting? 00:05:00
    • AdaBoost Part 1 00:04:00
    • AdaBoost Part 2 00:15:00
    • SVM Outline 00:05:00
    • SVM intuition 00:11:00
    • Hard vs Soft Margins 00:13:00
    • C hyper-parameter 00:04:00
    • Kernel Trick 00:12:00
    • SVM – Kernel Types 00:18:00
    • SVM with Linear Dataset (Iris) 00:13:00
    • SVM with Non-linear Dataset 00:13:00
    • SVM with Regression 00:06:00
    • [Project] Voice Gender Recognition using SVM 00:04:00
    • Unsupervised Machine Learning Intro 00:20:00
    • Representation of Clusters 00:20:00
    • Data Standardization 00:19:00
    • PCA Section Overview 00:05:00
    • What is PCA? 00:09:00
    • PCA Drawbacks 00:03:00
    • PCA Algorithm Steps (Mathematics) 00:13:00
    • Covariance Matrix vs SVD 00:04:00
    • PCA – Main Applications 00:02:00
    • PCA – Image Compression 00:27:00
    • PCA Data Preprocessing 00:14:00
    • PCA – Biplot and the Screen Plot 00:17:00
    • PCA – Feature Scaling and Screen Plot 00:09:00
    • PCA – Supervised vs Unsupervised 00:05:00
    • PCA – Visualization 00:08:00
    • Creating A Data Science Resume 00:07:00
    • Data Science Cover Letter 00:04:00
    • How to Contact Recruiters 00:04:00
    • Getting Started with Freelancing 00:04:00
    • Top Freelance Websites 00:05:00
    • Personal Branding 00:04:00
    • Networking Do’s and Don’ts 00:03:00
    • Importance of a Website 00:03:00
    • Resources 00:10:00
    • Get Your Certificate & Transcript 00:00:00

Instructor

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Students
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Course Curriculum

    • Who is This Course For? 00:03:00
    • Data Science + Machine Learning Marketplace 00:07:00
    • Data Science Job Opportunities 00:04:00
    • Data Science Job Roles 00:10:00
    • What is a Data Scientist? 00:17:00
    • How To Get a Data Science Job 00:18:00
    • Data Science Projects Overview 00:12:00
    • Why We Use Python? 00:03:00
    • What is Data Science? 00:13:00
    • What is Machine Learning? 00:14:00
    • Machine Learning Concepts & Algorithms 00:15:00
    • What is Deep Learning? 00:10:00
    • Machine Learning vs Deep Learning 00:11:00
    • What is Programming? 00:06:00
    • Why Python for Data Science? 00:05:00
    • What is Jupyter? 00:04:00
    • What is Google Colab? 00:03:00
    • Python Variables, Booleans and None 00:12:00
    • Getting Started with Google Colab 00:09:00
    • Python Operators 00:25:00
    • Python Numbers & Booleans 00:08:00
    • Python Strings 00:13:00
    • Python Conditional Statements 00:14:00
    • Python For Loops and While Loops 00:08:00
    • Python Lists 00:05:00
    • More about Lists 00:15:00
    • Python Tuples 00:11:00
    • Python Dictionaries 00:20:00
    • Python Sets 00:10:00
    • Compound Data Types & When to use each one? 00:13:00
    • Python Functions 00:14:00
    • Object Oriented Programming in Python 00:19:00
    • Intro To Statistics 00:07:00
    • Descriptive Statistics 00:07:00
    • Measure of Variability 00:12:00
    • Measure of Variability Continued 00:10:00
    • Measures of Variable Relationship 00:08:00
    • Inferential Statistics 00:15:00
    • Measure of Asymmetry 00:02:00
    • Sampling Distribution 00:08:00
    • What Exactly is Probability? 00:04:00
    • Expected Values 00:03:00
    • Relative Frequency 00:05:00
    • Hypothesis Testing Overview 00:09:00
    • Intro NumPy Array Data Types 00:12:00
    • NumPy Arrays 00:08:00
    • NumPy Arrays Basics 00:12:00
    • NumPy Array Indexing 00:09:00
    • NumPy Array Computations 00:06:00
    • Broadcasting 00:05:00
    • Introduction to Pandas 00:16:00
    • Introduction to Pandas Continued 00:18:00
    • Data Visualization Overview 00:25:00
    • Different Data Visualization Libraries in Python 00:13:00
    • Python Data Visualization Implementation 00:08:00
    • Introduction To Machine Learning 00:26:00
    • Exploratory Data Analysis 00:13:00
    • Feature Scaling 00:08:00
    • Data Cleaning 00:08:00
    • Feature Engineering 00:06:00
    • Linear Regression Intro 00:08:00
    • Gradient Descent 00:06:00
    • Linear Regression + Correlation Methods 00:27:00
    • Linear Regression Implementation 00:05:00
    • Logistic Regression 00:03:00
    • KNN Overview 00:03:00
    • parametric vs non-parametric models 00:03:00
    • EDA on Iris Dataset 00:22:00
    • The KNN Intuition 00:02:00
    • Implement the KNN algorithm from scratch 00:12:00
    • Compare the result with the sklearn library 00:04:00
    • Hyperparameter tuning using the cross-validation 00:11:00
    • The decision boundary visualization 00:05:00
    • Manhattan vs Euclidean Distance 00:11:00
    • Feature scaling in KNN 00:06:00
    • Curse of dimensionality 00:08:00
    • KNN use cases 00:04:00
    • KNN pros and cons 00:06:00
    • Decision Trees Section Overview 00:04:00
    • EDA on Adult Dataset 00:17:00
    • What is Entropy and Information Gain? 00:22:00
    • The Decision Tree ID3 algorithm from scratch Part 1 00:12:00
    • The Decision Tree ID3 algorithm from scratch Part 2 00:08:00
    • The Decision Tree ID3 algorithm from scratch Part 3 00:04:00
    • ID3 – Putting Everything Together 00:21:00
    • Evaluating our ID3 implementation 00:17:00
    • Compare with sklearn implementation 00:09:00
    • Visualizing the tree 00:10:00
    • Plot the features importance 00:06:00
    • Decision Trees Hyper-parameters 00:12:00
    • Pruning 00:17:00
    • [Optional] Gain Ration 00:03:00
    • Decision Trees Pros and Cons 00:08:00
    • [Project] Predict whether income exceeds $50K/yr – Overview 00:03:00
    • Ensemble Learning Section Overview 00:04:00
    • What is Ensemble Learning? 00:13:00
    • What is Bootstrap Sampling? 00:08:00
    • What is Bagging? 00:05:00
    • Out-of-Bag Error (OOB Error) 00:08:00
    • Implementing Random Forests from scratch Part 1 00:23:00
    • Implementing Random Forests from scratch Part 2 00:06:00
    • Compare with sklearn implementation 00:09:00
    • Random Forests Hyper-Parameters 00:04:00
    • Random Forests Pros and Cons 00:05:00
    • What is Boosting? 00:05:00
    • AdaBoost Part 1 00:04:00
    • AdaBoost Part 2 00:15:00
    • SVM Outline 00:05:00
    • SVM intuition 00:11:00
    • Hard vs Soft Margins 00:13:00
    • C hyper-parameter 00:04:00
    • Kernel Trick 00:12:00
    • SVM – Kernel Types 00:18:00
    • SVM with Linear Dataset (Iris) 00:13:00
    • SVM with Non-linear Dataset 00:13:00
    • SVM with Regression 00:06:00
    • [Project] Voice Gender Recognition using SVM 00:04:00
    • Unsupervised Machine Learning Intro 00:20:00
    • Representation of Clusters 00:20:00
    • Data Standardization 00:19:00
    • PCA Section Overview 00:05:00
    • What is PCA? 00:09:00
    • PCA Drawbacks 00:03:00
    • PCA Algorithm Steps (Mathematics) 00:13:00
    • Covariance Matrix vs SVD 00:04:00
    • PCA – Main Applications 00:02:00
    • PCA – Image Compression 00:27:00
    • PCA Data Preprocessing 00:14:00
    • PCA – Biplot and the Screen Plot 00:17:00
    • PCA – Feature Scaling and Screen Plot 00:09:00
    • PCA – Supervised vs Unsupervised 00:05:00
    • PCA – Visualization 00:08:00
    • Creating A Data Science Resume 00:07:00
    • Data Science Cover Letter 00:04:00
    • How to Contact Recruiters 00:04:00
    • Getting Started with Freelancing 00:04:00
    • Top Freelance Websites 00:05:00
    • Personal Branding 00:04:00
    • Networking Do’s and Don’ts 00:03:00
    • Importance of a Website 00:03:00
    • Resources 00:10:00
    • Get Your Certificate & Transcript 00:00:00

Course Reviews

4

4
4 ratings
  • 5 stars1
  • 4 stars1
  • 3 stars1
  • 2 stars0
  • 1 stars0
  1. Data Science & Machine Learning with Python

    3

    While the course covers a great deal of this field, it does not include any marked or unmarked exercises to perform . This does not embed knowledge very well., nor test it. The lessons jump about a bit as well. If you want a proper certification I would look elsewhere, perhaps Data-camp / Data-quest/ Coursera etc . or even Kaggle, which have marked exercises. As I only paid about £9 for the course I cannot complain too much.

  2. Wondeful course

    5

    Great course still studying it

  3. Narges Khadem HosseiniJuly 3, 2021 at 8:22 am

    Machine learning and data science

    4

    The information was very beneficial, better off having English subtitles.

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