Course Description
The Data Science and Machine Learning with Python teaches the most effective technique to understand the subject thoroughly. This course is taught by top industry experts and academic professionals. Enrol our top online courses now for a super discounted price.
Data Science and Machine Learning with Python is meticulously researched and prepared with the utmost care. All of the topics and subtopics are scientifically organised, taking into account the learner’s psychology and overall experience. All of the modules are bite-sized, audiovisual, simple to comprehend and interactive.
With a rich learning experience and pleasant customer service, Academy of Skills ensures quality and care. When you buy an online course from Academy of Skills, you get full access for 365 days and full tutor support and 24×7 customer service.
Most of our skilled instructors guarantee that they will answer all of your questions and make your learning experience pleasant. Furthermore, you will receive an electronic certificate immediately after finishing the Data Science and Machine Learning with Python , which will assist you in obtaining work in the appropriate sector by enhancing your expertise and CV.
You should select this Data Science and Machine Learning with Python from Academy of Skills if you want to learn about this topic while focusing on high-quality learning from professionals and experts. There are no hidden costs or examination fees. We are entirely transparent and honest about all of the course costs.
Why Study This Online Course?
This course will broaden your knowledge and add valuable skills to your resume. There are no set deadlines or instructional schedules so you can go at your own pace. You are free to study at your own speed, but to obtain the certificate; you must pass the assessment test or project. (Note: Some of our online courses do not have any MCQ examinations or assessments. We can add an assessment on request.) Our online learning platform can be accessed from any internet-connected device, such as a smartphone, tablet, or computer, which will be used to deliver the course. The best part is that because this course is video-based, you may pause it whenever you want to take your notes.
Why Study with the Academy of Skills?
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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 desire to learn
- A PC or mobile device
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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