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Python for Data Science & Machine Learning - Level 3

4( 4 REVIEWS )
153 STUDENTS
Overview Python is commonly used for website and software development, task automation, data analysis, and data visualisation. Python is the …

Overview

Python is commonly used for website and software development, task automation, data analysis, and data visualisation. Python is the number one programming language choice for machine learning, data science, and artificial intelligence. This course will teach you how easy it is to become a professional Python programmer and create programmes, applications, scripts, games, and much more.

Our main goal in this practical, hands-on course, where we blend practical work with solid theoretical training, is to provide you with the education not only to understand the ins and outs of Python Programming, but also to learn exactly how the software development industry works, the various roles within a team, and how to land a Python Programming job without a degree.

We understand that theory is important to build a solid foundation, but theory alone will not get the job done, which is why this course is jam-packed with practical hands-on examples and case studies that you can follow step by step.

Why You Should Consider Taking this Course at Study365?

Python for Data Science & Machine Learning – Level 3 is brought to you by Study365. We are a leading online education provider for several accredited organisations, and provide learners the opportunity to take this IAP course. At Study365, we give our fullest attention to the learners’ needs and ensure they have the necessary information required to proceed with the training. 

Our priority is on the advancement of knowledge and skills, we are bound to facilitate everything required to confirm a top-notch education service. We vouch for our learners an exclusive learning experience by paying close attention to each of their unique requirements and coming up with solutions every time. We provide them with state-of-the-art facilities using the latest technology. 

The benefits of studying with Study365 are ample. Here are just a few,

  • Unlimited 12 months access from anywhere
  • Save time and money on travel
  •  Learn at your own pace 
  •  Versatile and motivated learning
  • A culture of continuous improvement
  • About the Tutor
  • Learning Outcome
  • Who is this qualification for?
  • Prerequisites to take the course 
  • Method of Assessment
  • Certification
  • Awarding Body
  • Prospective careers 

Juan Galvan is a visionary, marketer and digital entrepreneur. He has been effective in enabling digital businesses to reach the next level of success. He believes in continued education and wants to share his extensive knowledge and experience as a coach, consultant and strategist with others. He aims to enable learners to expand their skill set in digital marketing, web development, programming and e-commerce. Juan Galvan will guide you to make critical business decisions, develop unique ways to deliver products in the marketplace and have clarity and confidence in your business.

  • Understanding of basic Python structures: strings, lists, and dictionaries
  • Learn to use Python Object-Oriented Programming (OOP)
  • Confidence to write Python scripts to perform automated actions
  • Learn to produce your own Python programmes from scratch
  • Students
  • Aspiring Python Developers
  • IT professionals
  • A basic understanding of English, ICT and numeracy would be beneficial 

This is a knowledge-based course, and thus, will contain no method of assessment. 

Once the course is completed, the learners get awarded with a certificate of  completion for 'Python for Data Science & Machine Learning - Level 3' by iAP.

The International Awards for Professionals (iAP) is an awarding body established in 1999 that aims to promote a high educational standard. They hope to create online education that is trustworthy and credible. They are focused on raising the standards of online education, and ensuring it is accessible to all. The iAP provides accreditation for a range of educational establishments, and monitors and continually develops the educational standards of such institutions. Their globally recognised certifications allow learners to acquire the skills and knowledge needed to gain employment in the chosen fields. 

On successful completion of this course, learners will have the knowledge and skills to enter the relevant job market, with the confidence to explore a wide range of industry-related professions. You can study related courses that will open the door to new and exciting opportunities and enhance your expertise in this subject, and add this as a skillset to your resume. Your skills will be recognised by top employers and organisations that will enable you to land a generous-paying job, gain plenty of benefits, and a wide array of opportunities. Given below are job titles you can compete for,

  • Python Developer
  • Data Scientist
  • Machine Learning Engineer

Python has been adopted by many non-programmers too such as,

  • Accountants
  • Scientists

Course Curriculum

1. Introduction To Python For Data Science & Machine Learning From A-Z
1.1 Who is this course for? FREE 00:03:00
1.2 Data science + machine learning marketplace FREE 00:07:00
1.3 Data science job opportunities FREE 00:04:00
1.4 Data science job roles FREE 00:10:00
1.5 What is a data scientist? FREE 00:17:00
1.6 How to get a data science job FREE 00:19:00
1.7 Data science projects overview 00:12:00
2. Data Science & Machine Learning Concepts
2.1 Why we use python 00:03:00
2.2 What is data science? 00:13:00
2.3 What is machine learning? 00:14:00
2.4 Machine learning concepts & algorithms 00:15:00
2.5 What is deep learning? 00:10:00
2.6 Machine learning vs deep learning 00:11:00
3. Python For Data Science
3.1. What is programming? 00:06:00
3.2. Why python for data science? 00:05:00
3.3. What is jupyter? 00:04:00
3.4. What is google colab? 00:03:00
3.5. Python variables, booleans 00:12:00
3.6. Getting started with google colab 00:09:00
3.7. Python operators 00:25:00
3.8. Python numbers & booleans 00:08:00
3.9. Python strings 00:13:00
3.10. Python conditional statements 00:14:00
3.11. Python for loops and while loops 00:08:00
3.12. Python lists 00:05:00
3.13. More about lists 00:15:00
3.14. Python tuples 00:11:00
3.15. Python dictionaries 00:20:00
3.16. Python sets 00:10:00
3.17. Compound data types & when to use each one? 00:13:00
3.18. Python functions 00:14:00
3.19. Object-oriented programming in python 00:19:00
4. Statistics for Data Science
4.1. Introduction to statistics 00:07:00
4.2. Descriptive statistics 00:07:00
4.3. Measure of variability 00:12:00
4.4. Measure of variability continued 00:10:00
4.5. Measures of variable relationship 00:08:00
4.6. Inferential statistics 00:15:00
4.7. Measure of asymmetry 00:02:00
4.8. Sampling distribution 00:08:00
5. Probability And Hypothesis Testing
5.1. What exactly is probability? 00:04:00
5.2. Expected values 00:03:00
5.3. Relative frequency 00:05:00
5.4. Hypothesis testing overview 00:09:00
6. NumPy Data Analysis
6.1. Intro numpy array data types 00:13:00
6.2. Numpy arrays 00:08:00
6.3. Numpy arrays basics 00:12:00
6.4. Numpy array indexing 00:09:00
6.5. Numpy array computations 00:06:00
6.6. Broadcasting 00:05:00
7. Pandas Data Analysis
7.1. Intro to pandas 00:16:00
7.2. Intro to pandas continued 00:18:00
8. Python Data Visualization
8.1. Data visualization overview 00:25:00
8.2. Different data visualization libraries in python 00:13:00
8.3. Python data visualization implementation 00:08:00
9. Introduction To Machine Learning
9.1. Introduction to machine learning 00:26:00
10. Data Loading & Exploration
10.1. Exploratory data analysis 00:13:00
11. Data Cleaning
11.1. Feature scaling 00:08:00
11.2. Data cleaning 00:08:00
12. Feature Selecting And Engineering
12.1. Feature engineering 00:06:00
13. Linear And Logistic Regression
13.1. Linear regression Intro 00:08:00
13.2. Gradient descent 00:06:00
13.3. Linear regression + correlation methods 00:27:00
13.4. Linear regression Implementation 00:05:00
13.5. Logistic regression 00:03:00
14. K Nearest Neighbors
14.1. Parametric vs non-parametric models 00:03:00
14.2. Eda on iris dataset 00:22:00
14.3. The knn intuition 00:02:00
14.4. Implement the knn algorithm from scratch 00:12:00
14.5. Compare the result with the sklearn library 00:04:00
14.6. Hyperparameter tuning using the cross-validation 00:11:00
14.7. The decision boundary visualization 00:05:00
14.8. Manhattan vs euclidean distance 00:11:00
14.9. Feature scaling in knn 00:06:00
14.10. Curse of dimensionality 00:08:00
14.11. Knn use cases 00:04:00
14.12. Knn pros and cons 00:06:00
15. Decision Trees
15.1. Decision Trees Section Overview 00:04:00
15.2. EDA on Adult Dataset 00:17:00
15.3. What is Entropy and Information Gain? 00:22:00
15.4. The Decision Tree ID3 algorithm from scratch Part 1 00:12:00
15.5. The Decision Tree ID3 algorithm from scratch Part 2 00:08:00
15.6. The Decision Tree ID3 algorithm from scratch Part 3 00:04:00
15.7. ID3 – Putting Everything Together 00:21:00
15.8. Evaluating our ID3 implementation 00:17:00
15.9. Compare with Sklearn implementation 00:09:00
15.10. Visualizing the tree 00:10:00
15.11. Plot the Important Features 00:06:00
15.12. Decision Trees Hyper-parameters 00:12:00
15.13. Pruning 00:17:00
15.14. [Optional] Gain Ration 00:03:00
15.15. Decision Trees Pros and Cons 00:08:00
15.16. [Project] Predict whether income exceeds $50K/yr – Overview 00:03:00
16. Ensemble Learning And Random Forests
16.1. Ensemble Learning Section Overview 00:04:00
16.2. What is Ensemble Learning? 00:13:00
16.3. What is Bootstrap Sampling? 00:08:00
16.4. What is Bagging? 00:05:00
16.5. Out-of-Bag Error (OOB Error) 00:08:00
16.6. Implementing Random Forests from scratch Part 1 00:23:00
16.7. Implementing Random Forests from scratch Part 2 00:06:00
16.8. Compare with sklearn implementation 00:04:00
16.9. Random Forests Hyper-Parameters 00:04:00
16.10. Random Forests Pros and Cons 00:05:00
16.11. What is Boosting? 00:05:00
16.12. AdaBoost Part 1 00:04:00
16.13. AdaBoost Part 2 00:15:00
17. Support Vector Machines
17.1. SVM Outline 00:05:00
17.2. SVM intuition 00:12:00
17.3. Hard vs Soft Margins 00:13:00
17.4. C hyper-parameter 00:04:00
17.5. Kernel Trick 00:12:00
17.6. SVM – Kernel Types 00:18:00
17.7. SVM with Linear Dataset (Iris) 00:14:00
17.8. SVM with Non-linear Dataset 00:13:00
17.9. SVM with Regression 00:06:00
17.10. [Project] Voice Gender Recognition using SVM 00:04:00
18. K-Means
18.1 Unsupervised Machine Learning Introduction 00:20:00
18.2 Unsupervised Machine Learning Continued 00:20:00
18.3 Data Standardization 00:19:00
19. PCA
19.1. PCA Section Overview 00:05:00
19.2. What is PCA? 00:10:00
19.3. PCA Drawbacks 00:04:00
19.4. PCA Algorithm Steps (Mathematics) 00:13:00
19.5. Covariance Matrix vs SVD 00:05:00
19.6. PCA – Main Applications 00:03:00
19.7. PCA – Image Compression 00:27:00
19.8. PCA Data Preprocessing 00:15:00
19.9. PCA – Biplot and the Screen Plot 00:17:00
19.10. PCA – Feature Scaling and Screen Plot 00:09:00
19.11. PCA – Supervised vs Unsupervised 00:05:00
19.12. PCA – Visualization 00:08:00
20. Data Science Career
20.1. 1.Creating A Data Science Resume 00:07:00
20.2. Data Science Cover Letter 00:04:00
20.3. How to Contact Recruiters 00:04:00
20.4. Getting Started with Freelancing 00:04:00
20.5. Top Freelance Websites 00:06:00
20.6. Personal Branding 00:04:00
20.7. Networking 00:04:00
20.8. Importance of a Website 00:03:00
Completion Certificate Request
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Students feedback

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Average rating (4)
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1 Star

    Addison Dixon

    October 14, 2021
    Build skills

    I recommend this to everyone wanting to build solid foundations in machine learning.

    Harper Wood

    August 19, 2021
    Learning journey

    This course made my learning journey interesting.

    Ashton Bailey

    August 14, 2021
    Always Helpful

    Your team is always so nice and amazing all throughout the learning

    Dane Marsh

    June 18, 2021
    Great skills

    This resource is vital for developing the best skills required by this challenging industry.

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