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Data Science & Machine Learning with Python from A-Z course is the perfect course for the professionals. Learn key concepts, strategies regarding use of  Data Science & Machine Learning with Python and boost your career with a marketable skill. Learn from the experts and become an expert.

£10.50
Course Access

Unlimited Duration

Last Updated

May 27, 2022

Students Enrolled

5

Course Duration

22 hours, 51 minutes

Course Instructor
Certification

What 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

Is this course for you?

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 Data Science & Machine Learning from A-Z course?

  • 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

Assessment

You have to attend an online assessment by the end of the course. Assessments will instantly provide results. The successful assessment requirement is 60%. Learners are allowed multiple attempts to encourage them to achieve the desired score. However, every attempt will make the previous result invalid. This multiple assessment attempt is free of charge.

Certification

Upon successful completion of this course, an instant e-certificate will be generated free of charge. The digital version of the course transcript is available for £2.99. We can post the printed copy to your address. A delivery charge of £4.99 for the UK & £12.99 outside of the UK is applied.

Instructor

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4.79 4.788095238095239
5353
Students
Digital Entrepreneur | Marketer | Visionary

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Course Content

    • 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
    • Get Your Certificate & Transcript 00:01:00

Course Reviews

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  1. Onique CampbellJuly 8, 2021 at 9:54 am

    Data Science And Machine Learning

    4

    I enjoy the beginning part of the course related to python learning and computer programming as it was easy to digest and grasp. however the machine learning segment was not intuitive enough and i felt unknowing as a lot of stuff was not explained in detail. however overall good course.

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