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

PRIVATE
Course Access

Unlimited Duration

Last Updated

June 14, 2022

Students Enrolled

153

Course Duration

23 hours, 1 minute

Course Instructor
Certification

What you will learn

  • 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

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 this 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  course support

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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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
    • Resources 00:10:00
    • 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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