Course Description
Are you interested in advancing your skills in Data Science and Machine Learning with Python? Look no further than the Academy of Skills! Our online course is taught by top industry experts and academic professionals, ensuring you receive the most effective techniques to understand the subject thoroughly.
This online course: Data Science and Machine Learning with Python, provides a comprehensive introduction to the concepts, tools, and techniques of data science and machine learning using Python programming language. The course covers various topics such as statistics, data analysis, data visualization, machine learning algorithms, and more.
Our meticulously researched and prepared course is scientifically organized, taking into account the learner’s psychology and overall experience. With bite-sized modules that are audiovisual, simple to comprehend, and interactive, you can expect a rich learning experience.
At the Academy of Skills, we prioritize quality and care in our customer service. When you enrol in our online course, you receive full access for 365 days, full tutor support, and 24×7 customer service. Our skilled instructors are dedicated to answering all of your questions and making your learning experience a pleasant one.
Plus, upon completing the course, you will receive an electronic certificate that will enhance your expertise and CV, helping you obtain work in the appropriate sector. And there are no hidden costs or examination fees – we are entirely transparent and honest about all of the course costs.
Don’t miss out on the opportunity to learn from professionals and experts in Data Science and Machine Learning with Python. Enroll now in our top online courses for a super discounted price!
Why Study This Python Online Course?
The demand for data scientists and machine learning experts is growing rapidly, and Python is one of the most popular programming languages used in these fields. This course provides a thorough understanding of the fundamentals of data science and machine learning using Python, making it an excellent starting point for anyone looking to enter these fields.
Why Study with the Academy of Skills?
- Audio Visual Lesson
Easy to follow Audio Visual lesson with lesson control at your fingertip - Learn anything
Whether you want to develop your skills or learn a new activity, you have an online course. - Learn Anywhere
You can learn a new skill from anywhere, including the convenience of your couch. - Access to Top Instructors
Learn from alumni from prestigious universities and cultural institutions who will share their insights and knowledge. - Instant Certificate
Upon successful completion of each course, you will receive an instant digital certificate. - Affordable Courses
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Learn new skills at your own pace and on your own time; study anywhere and whenever you choose.
Will I Receive A Certificate of Completion for Data Science and Machine Learning with Python ?
Yes, after completion of this course, you will be able to request a certificate of completion from the Academy of Skills. You can order the certificate with our secure payment system. Use the link at the bottom of this page to order the Certificate. An exclusive discount is offered for pre-orders.
You can request for PDF Certificate, PDF Transcript, Hardcopy Certificate and Hardcopy Transcript after successfully completing the course. We offer Free UK Shipping for all Hardcopy orders. Visit the Certificate order page (https://academyofskills.org/certificate-transcript/) to purchase/claim your certificate.
Requirement
- There is no formal entry requirement.
- A basic understanding of programming concepts and the Python programming language
- Familiarity with mathematical concepts such as statistics and linear algebra will also be helpful
- Access to a computer with an internet connection and a modern web browser.
Course Curriculum
| INTRODUCTION TO PYTHON FOR DATA SCIENCE AND MACHINE LEARNING FROM A-Z | |||
| Who is this course for? | 00:03:00 | ||
| Data Science + Machine Learning Marketplace | 00:07:00 | ||
| Data Science Job Opportunities | 00:05:00 | ||
| Data Science Job Roles | 00:11:00 | ||
| What is a Data Scientist? | 00:17:00 | ||
| How To Get a Data Science Job | 00:19:00 | ||
| Data Science Projects Overview | 00:12:00 | ||
| DATA SCIENCE AND MACHINE LEARNING CONCEPTS | |||
| Why We Use Python | 00:04:00 | ||
| What is Data Science? | 00:14:00 | ||
| What is Machine Learning? | 00:15:00 | ||
| Machine Learning Concepts and Algorithms | 00:15:00 | ||
| What is Deep Learning? | 00:10:00 | ||
| Machine Learning vs Deep Learning | 00:12:00 | ||
| PYTHON FOR DATA SCIENCE | |||
| What is Programming? | 00:07:00 | ||
| Why Python for Data Science? | 00:05:00 | ||
| What is Jupyter? | 00:04:00 | ||
| What is Google Colab? | 00:04:00 | ||
| Jupyter Notebook | 00:19:00 | ||
| Python Variables, Booleans | 00:12:00 | ||
| Getting Started with Google Colab | 00:10:00 | ||
| Python Operators | 00:26:00 | ||
| Python Numbers and Booleans | 00:08:00 | ||
| Python Strings | 00:14:00 | ||
| Python Conditional Statements | 00:14:00 | ||
| Python For Loops and While Loops | 00:09:00 | ||
| Python Lists | 00:06:00 | ||
| More about Lists | 00:16:00 | ||
| Python Tuples | 00:12:00 | ||
| Python Dictionaries | 00:21:00 | ||
| Python Sets | 00:10:00 | ||
| Compound Data Types and When to use each one? | 00:13:00 | ||
| Python Functions | 00:15:00 | ||
| Object-Oriented Programming in Python | 00:19:00 | ||
| STATISTICS FOR DATA SCIENCE | |||
| Intro to Statistics | 00:08:00 | ||
| Descriptive Statistics | 00:07:00 | ||
| Measure of Variability | 00:13:00 | ||
| Measure of Variability Continued | 00:10:00 | ||
| Measures of Variable Relationship | 00:08:00 | ||
| Inferential Statistics | 00:16:00 | ||
| Measure of Asymmetry | 00:02:00 | ||
| Sampling Distribution | 00:08:00 | ||
| PROBABILITY AND HYPOTHESIS TESTING | |||
| What Exactly is Probability? | 00:04:00 | ||
| Expected Values | 00:03:00 | ||
| Relative Frequency | 00:06:00 | ||
| NUMPY DATA ANALYSIS | |||
| Hypothesis Testing Overview | 00:10:00 | ||
| Intro NumPy Array Data Types | 00:13:00 | ||
| NumPy Arrays | 00:09:00 | ||
| NumPy Arrays Basics | 00:12:00 | ||
| NumPy Array Indexing | 00:10:00 | ||
| NumPy Array Computations | 00:06:00 | ||
| Broadcasting | 00:05:00 | ||
| PANDAS DATA ANALYSIS | |||
| Intro To Pandas | 00:16:00 | ||
| Intro To Pandas Continued | 00:19:00 | ||
| PYTHON DATA VISUALIZATION | |||
| Data Visualization Overview | 00:25:00 | ||
| Different Data Visualization Libraries in Python | 00:13:00 | ||
| Python Data Visualization Implementation | 00:09:00 | ||
| INTRODUCTION TO MACHINE LEARNING | |||
| Intro to Machine Learning | 00:27:00 | ||
| DATA LOADING AND EXPLORATION | |||
| Exploratory Data Analysis | 00:14:00 | ||
| Feature Scaling | 00:08:00 | ||
| DATA CLEANING | |||
| Data Cleaning | 00:08:00 | ||
| FEATURE SELECTING AND ENGINEERING | |||
| Feature Engineering | 00:07:00 | ||
| LINEAR AND LOGISTIC REGRESSION | |||
| Linear Regression Intro | 00:09:00 | ||
| Gradient Descent | 00:06:00 | ||
| Linear Regression + Correlation Methods | 00:27:00 | ||
| Linear Regression Implemenation | 00:06:00 | ||
| Logistic Regression | 00:04:00 | ||
| K NEAREST NEIGHBORS | |||
| KNN Overview | 00:04:00 | ||
| Parametic vs Non-Parametic Models | 00:04:00 | ||
| EDA on Iris Dataset | 00:23:00 | ||
| KNN – Intuition | 00:03:00 | ||
| Implement the KNN algorithm from scratch | 00:12:00 | ||
| Compare the Reuslt with 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 | 00:12:00 | ||
| Feature scaling in KNN | 00:07:00 | ||
| Curse of dimensionality | 00:09:00 | ||
| KNN use cases | 00:04:00 | ||
| KNN pros and cons | 00:06:00 | ||
| DECISION TREES | |||
| Decision Trees Section Overview | 00:05:00 | ||
| 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 | 00:05:00 | ||
| ID3 – Putting Everything Together | 00:22:00 | ||
| Evaluating our ID3 implementation | 00:17:00 | ||
| Compare with sklearn implementation | 00:04:00 | ||
| Visualizing the tree | 00:11:00 | ||
| Plot the Important Features | 00:06:00 | ||
| Decision Trees Hyper-parameters | 00:12:00 | ||
| Pruning | 00:18:00 | ||
| [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 AND RANDOM FORESTS | |||
| Ensemble Learning Section Overview | 00:04:00 | ||
| What is Ensemble Learning? | 00:14:00 | ||
| What is Bootstrap Sampling? | 00:09:00 | ||
| What is Bagging? | 00:06:00 | ||
| 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 | 00:07:00 | ||
| Compare with sklearn implementation | 00:04:00 | ||
| Random Forests Hyper-Parameters | 00:05:00 | ||
| Random Forests Pros and Cons | 00:06:00 | ||
| What is Boosting? | 00:05:00 | ||
| AdaBoost Part 1 | 00:05:00 | ||
| AdaBoost Part 2 | 00:15:00 | ||
| SUPPORT VECTOR MACHINES | |||
| SVM Outline | 00:06:00 | ||
| SVM intuition | 00:12:00 | ||
| Hard vs Soft Margins | 00:14:00 | ||
| C hyper-parameter | 00:05:00 | ||
| Kernel Trick | 00:13:00 | ||
| Kernel Types | 00:19:00 | ||
| SVM with Linear Dataset (Iris) | 00:14:00 | ||
| SVM with Non-linear Dataset | 00:13:00 | ||
| SVM with Regression | 00:06:00 | ||
| [Project] Voice Gender Recognition using SVM | 00:05:00 | ||
| K-MEANS | |||
| Unsupervised Machine Learning Intro | 00:21:00 | ||
| Unsupervised Machine Learning Continued | 00:21:00 | ||
| Data Standardization | 00:20:00 | ||
| PCA | |||
| PCA Section Overview | 00:06:00 | ||
| What is PCA? | 00:10:00 | ||
| PCA Drawbacks | 00:04:00 | ||
| PCA Algorithm Steps (Mathematics) | 00:14:00 | ||
| Covariance Matrix vs SVD | 00:05:00 | ||
| PCA – Main Applications | 00:03:00 | ||
| PCA – Image Compression | 00:28:00 | ||
| PCA – Image Compression | 00:28:00 | ||
| PCA – Image Compression | 00:28:00 | ||
| PCA – Feature Scaling and Screen Plot | 00:10:00 | ||
| PCA – Supervised vs Unsupervised | 00:05:00 | ||
| PCA – Visualization | 00:08:00 | ||
| DATA SCIENCE CAREER | |||
| Creating A Data Science Resume | 00:07:00 | ||
| Data Science Cover Letter | 00:04:00 | ||
| How to Contact Recruiters | 00:05:00 | ||
| Getting Started with Freelancing | 00:05:00 | ||
| Top Freelance Websites | 00:06:00 | ||
| Personal Branding | 00:05:00 | ||
| Networking | 00:04:00 | ||
| Importance of a Website | 00:03:00 | ||
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