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.
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,
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.
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 has been adopted by many non-programmers too such as,
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 | |||
Completion Certificate Request |
Addison Dixon
I recommend this to everyone wanting to build solid foundations in machine learning.
Harper Wood
This course made my learning journey interesting.
Ashton Bailey
Your team is always so nice and amazing all throughout the learning
Dane Marsh
This resource is vital for developing the best skills required by this challenging industry.