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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?

  • 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
    Best Prices With Regular Savings
  • Learn Anytime
    Learn new skills 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 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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