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Machine Learning in Python

Learn model building, evaluation, algorithms, machine learning concepts, data science PLUS Python coding and libraries
4.3
4.3/5
(45 reviews)
433 students
Created by

9.5

CourseMarks Score®

10.0

Freshness

8.2

Feedback

9.8

Content

Platform: Udemy
Video: 14h 52m
Language: English
Next start: On Demand

Top Machine Learning courses:

Detailed Analysis

CourseMarks Score®

9.5 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 5/2022.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

8.2 / 10
We analyzed factors such as the rating (4.3/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.8 / 10
Video Score: 9.9 / 10
The course includes 14h 52m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
Detail Score: 10.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
77 resources.
0 exercise.
0 test.

Table of contents

Description

Get instant access to a 234-page Machine Learning workbook containing all the reference material
Over 12 hours of clear and concise step by step instructions, practical lessons and engagement
35 quizzes and knowledge checks at various stages to test your learning and confirm your growth
Introduce yourself to our community of students in this course and tell us your goals
Encouragement & celebration of your progress: 25%, 50%, 75% and then 100% when you get your certificate
This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyze raw real-time data, identify trends, and make predictions. The participants will explore key techniques and tools to build Machine Learning solutions for businesses. You don’t need to have any technical knowledge to learn this skill.
What will you learn:
•Define what Machine Learning does and its importance
•Understand the Role of Machine Learning
•Explain what is Statistics
•Learn the different types of Descriptive Statistics
•Explain the meaning of Probability and its importance
•Define how Probability Process happens
•Discuss the definition of Objectives and Data Gathering Step
•Know the different concepts of Data Preparation and Data Exploratory Analysis Step
•Define what is Supervised Learning
•Differentiate Key Differences Between Supervised, Unsupervised, and Reinforced Learning
•Learn the difference between the Three Categories of Machine Learning
•Explore the usage of Two Categories of Supervised Learning
•Explain the importance of Linear Regression
•Learn the different types of Logistic Regression
•Learn what is an Integrated Development Environment and its importance
•Understand the factors why Developers use Integrated Development Environment
•Learn the most important factors on How to Perform Addition operations and close the Jupyter Notebook
•Apply and use Various Operations in Python
•Discuss Arithmetic Operation in Python
•Identify the different types of Built-in-Data Types in Python
•Learn the most important considerations of Dictionaries-Built-in Data types
•Explain the usage of Operations in Python and its importance
•Understand the importance of Logical Operators
•Define the different types of Controlled Statements
•Be able to create and write a program to find the maximum number
•…and more!
Contents and Overview
You’ll start with the History of Machine Learning; Difference Between Traditional Programming and Machine Learning; What does Machine Learning do; Definition of Machine Learning; Apply Apple Sorting Example Experiences; Role of Machine Learning; Machine Learning Key Terms; Basic Terminologies of Statistics; Descriptive Statistics-Types of Statistics; Types of Descriptive Statistics; What is Inferential Statistics; What is Analysis and its types; Probability and Real-life Examples; How Probability is a Process; Views of Probability; Base Theory of Probability.
Then you will learn about Defining Objectives and Data Gathering Step; Data Preparation and Data Exploratory Analysis Step; Building a Machine Learning Model and Model Evaluation; Prediction Step in the Machine Learning Process; How can a machine solve a problem-Lecture overview; What is Supervised Learning; What is Unsupervised Learning; What is Reinforced Learning; Key Differences Between Supervised,Unsupervised and Reinforced Learning; Three Categories of Machine Learning; What is Regression, Classification and Clustering; Two Categories of Supervised Learning; Category of Unsupervised Learning; Comparison of Regression , Classification and Clustering; What is Linear Regression; Advantages and Disadvantages of Linear Regression; Limitations of Linear Regression; What is Logistic Regression; Comparison of Linear Regression and Logistic Regression; Types of Logistic Regression; Advantages and Disadvantages of Logistic Regression; Limitations of Logistic Regression; What is Decision tree and its importance in Machine learning; Advantages and Disadvantages of Decision Tree.
We will also cover What is Integrated Development Environment; Parts of Integrated Development Environment; Why Developers Use Integrated Development Environment; Which IDE is used for Machine Learning; What are Open Source IDE; What is Python; Best IDE for Machine Learning along with Python; Anaconda Distribution Platform and Jupyter IDE; Three Important Tabs in Jupyter; Creating new Folder and Notebook in Jupyter; Creating Three Variables in Notebook; How to Check Available Variables in Notebook; How to Perform Addition operation and Close Jupyter Notebook; How to Avoid Errors in Jupyter Notebook; History of Python; Applications of Python; What is Variable-Fundamentals of Python; Rules for Naming Variables in Python; DataTypes in Python; Arithmetic Operation in Python; Various Operations in Python; Comparison Operation in Python; Logical Operations in Python; Identity Operation in Python; Membership Operation in Python; Bitwise Operation in Python; Data Types in Python; Operators in Python; Control Statements in Python; Libraries in Python; Libraries in Python; What is Scipy library; What is Pandas Library; What is Statsmodel and its features;
This course will also tackle Data Visualisation & Scikit Learn; What is Data Visualization; Matplotib Library; Seaborn Library; Scikit-learn Library; What is Dataset; Components of Dataset; Data Collection & Preparation; What is Meant by Data Collection; Understanding Data; Exploratory Data Analysis; Methods of Exploratory Data Analysis; Data Pre-Processing; Categorical Variables; Data Pre-processing Techniques.
This course will also discuss What is Linear Regression and its Use Case; Dataset For Linear Regression; Import library and Load Data set- steps of linear regression; Remove the Index Column-Steps of Linear Regression; Exploring Relationship between Predictors and Response; Pairplot method explanation; Corr and Heatmap method explanation; Creating Simple Linear Regression Model; Interpreting Model Coefficients; Making Predictions with our Model; Model Evaluation Metric; Implementation of Linear Regression-lecture overview; Uploading the Dataset in Jupyter Notebook; Importing Libraries and Load Dataset into Dataframe; Remove the Index Column; Exploratory Analysis -relation of predictor and response; Creation of Linear Regression Model; Model Coefficients; Making Predictions; Evaluation of Model Performance.
Next, you will learn about Model Evaluation Metrics and Logistic Regression – Diabetes Model.
Who are the Instructors?
Samidha Kurle from Digital Regenesys is your lead instructor – a professional making a living from her teaching skills with expertise in Machine Learning. She has joined with content creator Peter Alkema to bring you this amazing new course.
You’ll get premium support and feedback to help you become more confident with finance!
Our happiness guarantee…
We have a 30-day 100% money-back guarantee, so if you aren’t happy with your purchase, we will refund your course – no questions asked!
We can’t wait to see you on the course!
Enrol now, and master Machine Learning!
Peter and Samidha

You will learn

✓ Define what Machine Learning does and its importance
✓ Learn the different types of Descriptive Statistics
✓ Apply and use Various Operations in Python
✓ Explore the usage of Two Categories of Supervised Learning
✓ Learn the difference of the Three Categories of Machine Learning
✓ Understand the Role of Machine Learning
✓ Explain the meaning of Probability and its importance
✓ Define how Probability Process happen
✓ Discuss the definition of Objectives and Data Gathering Step
✓ Know the different concepts of Data Preparation and Data Exploratory Analysis Step
✓ Define what is Supervised Learning
✓ Differentiate Key Differences Between Supervised,Unsupervised,and Reinforced Learning
✓ Explain the importance of Linear Regression
✓ Learn the different types of Logistic Regression
✓ Learn what is an Integrated Development Environment and its importance
✓ Understand the factors why Developers use Integrated Development Environment
✓ Learn the most important factors on How to Perform Addition operation and close Jupyter Notebook
✓ Discuss Arithmetic Operation in Python
✓ Identify the different Types of Built-in-Data Types in Python
✓ Learn the most important considerations of Dictionaries-Built-in Data types
✓ Explain the usage of Operations in Python and its importance
✓ Understand the importance of Logical Operators
✓ Define the different types of Controlled Statements
✓ Be able to create and write a program to find maximum number
✓ Differentiate the different types of range functions in Python
✓ Explain what is Statistics, Probability and key concepts

Requirements

• No technical knowledge or experience is required to get going in this course
• A basic understanding of the importance of data science will be useful

This course is for

• Anyone interested in the field of Machine Learning and key concepts
• People who want to understand ML and build models in Python

How much does the Machine Learning in Python course cost? Is it worth it?

The course costs $14.99. And currently there is a 82% discount on the original price of the course, which was $84.99. So you save $70 if you enroll the course now.

Does the Machine Learning in Python course have a money back guarantee or refund policy?

YES, Machine Learning in Python has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Machine Learning in Python course, but there is a $70 discount from the original price ($84.99). So the current price is just $14.99.

Who is the instructor? Is Peter Alkema a SCAM or a TRUSTED instructor?

Peter Alkema has created 74 courses that got 13,665 reviews which are generally positive. Peter Alkema has taught 85,316 students and received a 4.4 average review out of 13,665 reviews. Depending on the information available, Peter Alkema is a TRUSTED instructor.
Business | Technology | Self Development
In my courses you learn practical skills: “I feel like I am in a real classroom.” – Kira Minehart
“What an amazing course! After finishing this course, I have confidence. Thank so much Dr Peter Alkema”. Or Tulongeni Shilunga: “This is exactly the jump-start I needed. Very clear and concise”
I also help lead digital transformation at FirstRand, the biggest financial services group in Africa. I’ve been featured on CNBC Africa and won the Gartner CIO Of The Year in 2016. I founded and led the largest banking hackathon in South Africa which was published in 2019 as a case study by Harvard Business School.
I’ve taught over 17,000 students about business, academics and self-development. In 2020 I completed my PhD at Wits University In Johannesburg. The study introduced a ground-breaking theory of Agile software development teams. My woodworking book was published in 2014 and has sold over 10,000 copies.
Olugbenga Gbadegesin: “Excellent delivery” / Lebogang Tswelapele: “This is what I have been longing for” / Paskalia Ndapandula: “Peter speaks with so much clarity” / Amantle Mangwedi: “It was straight to the point and the sections are cut into nice short segments which made it easier to go through” Kathy Bermudez: “Excellent material. Well organized…”
Werner van Wyk: “Thank you Peter, once again your lesson and course have given me so much knowledge and understanding” / Yvonne Rudolph “I really look forward to take everything i learned in action” / Josephine Mahlangu: “exactly what I needed to know, absolutely valuable and helpful for my personal growth”

9.5

CourseMarks Score®

10.0

Freshness

8.2

Feedback

9.8

Content

Platform: Udemy
Video: 14h 52m
Language: English
Next start: On Demand

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