The Data Science and Machine Learning with Python is the optimum way to achieve deep insight and knowledge of this topic. Since completing the course, you will receive a professional certificate and will have learned from the top industry expert or academic professionals. Enrol now for a limited-time discounted price. Like all the courses of Amaar School, this Data Science and Machine Learning with Python is designed & developed with meticulous research & utmost care. All the topics and subtopics are organised scientifically considering the learner’s psychology & overall experience. All the modules are easily understandable, interactive and bite-sized.We don’t just provide courses at Amaar School; we ensure quality & care with a rich learning experience and satisfactory customer service. After purchasing a course from Amaar School, you get complete 1-year access with full tutor support.
Most of our expert instructors are always promised to answer all your queries and make your learning experience elegant. After completing the Data Science and Machine Learning with Python, you will instantly get an electronic certificate that will support you to get jobs in the relevant field by enriching both your knowledge & CV.
If you want to learn about this topic focusing on top-quality learning from the professionals and experts regarding the sector, you should consider this Data Science and Machine Learning with Python from Amaar School. There are no hidden fees, no hidden exam charges. We are very open and honest about all of the course expenses.
For the free course and free certificate, either apply for the Amaar School Scholarship or visit our free courses section, and digitally skill yourself.
Why People Love And Enrol The Course From Amaar School!
Informative & Interactive H.D. Quality Audiovisual Training Sessions Relevant to Your Career
- Developed By Qualified Industry Professionals Following the U.K. & E.U. Standards
- Real-Life Projects Based Active Learning to Get Real Employable & Marketable Skills
- Benefit from Instant Feedback System Through Mock Exams, Assignment Evaluation or Multiple-Choice Question
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Design of This Course
The course will be delivered through our online learning platform, accessible through any internet-connected device, for example, mobile, tablet computer or computer. It’s completely self-paced, and there are no official deadlines or teaching schedules. You are completely free to study the course at your own speed, but you must pass on the assessment test or project to earn the certificate.
After enrolling on this course, You are going to enjoy the combination of
- Video lessons
- Free Online Study Materials
- Mock Exams
- Multiple-choice Assessment or Practical Project
How is The Course Assessed?
To complete the course, you must pass an automated, multiple-choice assessment, or sometimes you need to create a project plan & proposal, then you have to finish that project; Our team will assist you where you get stuck.
The assessment or the project is delivered through our online learning platform. You will receive the results of your evaluation immediately upon completion.
Will I Receive a Certificate of Completion?
Upon successful completion, you will be qualified for the U.K. and internationally recognised professional qualification, and you can choose to make your achievement formal by obtaining your PDF Certificate at the cost of £5 and a Hard Copy Certificate price will depend on the course level (For example, Basic Course Certificate £9.99, Premium Course Certificate £19.99, Professional Course Certificate £14.99)
Why Study This Course?
Whether you’re an existing practitioner or a learner by curiosity, or an aspiring professional, this course will enhance your expertise and enrich your CV with critical skills and an accredited qualification attesting to your knowledge.
The Data Science and Machine Learning with Python is open for all. No formal entry required. All you need is a passion for learning, a good understanding of the English language, numeracy and essential I.T. skill, and we prefer you to be at least 16 or over the age of 16; In the case of under 16, we recommend you to talk to your parents or legal Guardian.
- Lectures 142
- Quizzes 0
- Duration 23 hours
- Skill level Expert
- Language English
- Students 294
- Assessments Yes
Introduction to Python for Data Science & Machine Learning from A-Z
Data Science & Machine Learning Concepts
Python For Data Science
- What is Programming?
- Why Python for Data Science?
- What is Jupyter?
- What is Google Colab?
- Python Variables, Booleans
- 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?
- Python Functions
- Object-Oriented Programming in Python
- Jupyter Notebook
Statistics for Data Science
Probability and Hypothesis Testing
NumPy Data Analysis
Pandas Data Analysis
Python Data Visualization
Introduction to Machine Learning
Data Loading & Exploration
Feature Selecting and Engineering
Linear and Logistic Regression
K Nearest Neighbors
- 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
- Feature scaling in KNN
- Curse of dimensionality
- KNN use cases
- KNN pros and cons
- KNN Overview
- 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 Important Features
- Decision Trees Hyper-parameters
- [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
- Compare with sklearn implementation
- Random Forests Hyper-Parameters
- Random Forests Pros and Cons
- What is Boosting?
- AdaBoost Part 1
- AdaBoost Part 2
Support Vector Machines
- 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