Python for Machine Learning: The Complete Beginner's Course

Learn to create machine learning algorithms in Python for students and professionals

Python for Machine Learning: The Complete Beginner's Course

Instructors:

Meta Brains

Description:

To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm! 

When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.

Like the Wall Street "quants" of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.

That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.

The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.

In this course, mathematical notations and jargon are minimized, each topic is explained in simple English, making it easier to understand. Once you've gotten your hands on the code, you'll be able to play with it and build on it. The emphasis of this course is on understanding and using these algorithms in the real world, not in a theoretical or academic context. 

You'll walk away from each video with a fresh idea that you can put to use right away!

All skill levels are welcome in this course, and even if you have no prior statistical experience, you will be able to succeed!


Course content:

  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here !1. Introduction to Machine Learning
    • 1. What is Machine Learning.mp4 (7.5 MB)
    • 1. What is Machine Learning.srt (2.1 KB)
    • 2. Applications of Machine Learning.mp4 (6.5 MB)
    • 2. Applications of Machine Learning.srt (1.9 KB)
    • 3. Machine learning Methods.mp4 (3.7 MB)
    • 3. Machine learning Methods.srt (0.4 KB)
    • 4. What is Supervised learning.mp4 (6.2 MB)
    • 4. What is Supervised learning.srt (1.3 KB)
    • 5. What is Unsupervised learning.mp4 (6.0 MB)
    • 5. What is Unsupervised learning.srt (1.0 KB)
    • 6. Supervised learning vs Unsupervised learning.mp4 (14.3 MB)
    • 6. Supervised learning vs Unsupervised learning.srt (4.4 KB)
    • 7. Course Materials.html (0.1 KB)
    • 7.1 50_Startups.csv (2.4 KB)
    • 7.10 Movie_Id_Titles.original (49.8 KB)
    • 7.11 MultipleLinearRegression.ipynb (8.5 KB)
    • 7.12 Recommender Systems with Python.ipynb (122.4 KB)
    • 7.13 salaries.csv (0.6 KB)
    • 7.14 u.data (2.0 MB)
    • 7.15 user data.csv (10.7 KB)
    • 7.2 Decision_tree.ipynb (14.3 KB)
    • 7.3 homeprices.csv (0.1 KB)
    • 7.4 K-means algorithm numpy&pandas clustering.ipynb (102.3 KB)
    • 7.5 KNN_Binary_Classification.ipynb (25.2 KB)
    • 7.6 linear_regression_houseprice.ipynb (16.3 KB)
    • 7.7 logistic_regression_Binary_Classification.ipynb (2.7 KB)
    • 7.8 mall customers data.csv (4.3 KB)
    • 7.9 mallCustomerData.txt (3.9 KB)
    2. Simple Linear Regression
    • 1. Introduction to regression.mp4 (9.0 MB)
    • 1. Introduction to regression.srt (1.9 KB)
    • 2. How Does Linear Regression Work.mp4 (7.7 MB)
    • 2. How Does Linear Regression Work.srt (1.9 KB)
    • 3. Line representation.mp4 (5.5 MB)
    • 3. Line representation.srt (0.8 KB)
    • 4. Implementation in python Importing libraries & datasets.mp4 (7.6 MB)
    • 4. Implementation in python Importing libraries & datasets.srt (1.4 KB)
    • 5. Implementation in python Distribution of the data.mp4 (9.5 MB)
    • 5. Implementation in python Distribution of the data.srt (2.2 KB)
    • 6. Implementation in python Creating a linear regression object.mp4 (13.2 MB)
    • 6. Implementation in python Creating a linear regression object.srt (2.8 KB)
    3. Multiple Linear Regression
    • 1. Understanding Multiple linear regression.mp4 (6.3 MB)
    • 1. Understanding Multiple linear regression.srt (1.4 KB)
    • 2. Implementation in python Exploring the dataset.mp4 (13.3 MB)
    • 2. Implementation in python Exploring the dataset.srt (3.5 KB)
    • 3. Implementation in python Encoding Categorical Data.mp4 (28.9 MB)
    • 3. Implementation in python Encoding Categorical Data.srt (5.6 KB)
    • 4. Implementation in python Splitting data into Train and Test Sets.mp4 (8.8 MB)
    • 4. Implementation in python Splitting data into Train and Test Sets.srt (1.5 KB)
    • 5. Implementation in python Training the model on the Training set.mp4 (8.6 MB)
    • 5. Implementation in python Training the model on the Training set.srt (1.0 KB)
    • 6. Implementation in python Predicting the Test Set results.mp4 (17.8 MB)
    • 6. Implementation in python Predicting the Test Set results.srt (2.8 KB)
    • 7. Evaluating the performance of the regression model.mp4 (6.0 MB)
    • 7. Evaluating the performance of the regression model.srt (1.3 KB)
    • 8. Root Mean Squared Error in Python.mp4 (11.8 MB)
    • 8. Root Mean Squared Error in Python.srt (2.2 KB)
    4. Classification Algorithms K-Nearest Neighbors
    • 1. Introduction to classification.mp4 (4.7 MB)
    • 1. Introduction to classification.srt (1.1 KB)
    • 10. Implementation in python Results prediction & Confusion matrix.mp4 (9.7 MB)
    • 10. Implementation in python Results prediction & Confusion matrix.srt (1.4 KB)
    • 2. K-Nearest Neighbors algorithm.mp4 (6.1 MB)
    • 2. K-Nearest Neighbors algorithm.srt (0.9 KB)
    • 3. Example of KNN.mp4 (3.5 MB)
    • 3. Example of KNN.srt (0.4 KB)
    • 4. K-Nearest Neighbours (KNN) using python.mp4 (6.1 MB)
    • 4. K-Nearest Neighbours (KNN) using python.srt (1.2 KB)
    • 5. Implementation in python Importing required libraries.mp4 (5.1 MB)
    • 5. Implementation in python Importing required libraries.srt (0.4 KB)
    • 6. Implementation in python Importing the dataset.mp4 (9.3 MB)
    • 6. Implementation in python Importing the dataset.srt (1.3 KB)
    • 7. Implementation in python Splitting data into Train and Test Sets.mp4 (19.7 MB)
    • 7. Implementation in python Splitting data into Train and Test Sets.srt (2.8 KB)
    • 8. Implementation in python Feature Scaling.mp4 (5.7 MB)
    • 8. Implementation in python Feature Scaling.srt (0.3 KB)
    • 9. Implementation in python Importing the KNN classifier.mp4 (12.5 MB)
    • 9. Implementation in python Importing the KNN classifier.srt (2.0 KB)
    5. Classification Algorithms Decision Tree
    • 1. Introduction to decision trees.mp4 (6.5 MB)
    • 1. Introduction to decision trees.srt (1.5 KB)
    • 2. What is Entropy.mp4 (5.2 MB)
    • 2. What is Entropy.srt (1.4 KB)
    • 3. Exploring the dataset.mp4 (6.0 MB)
    • 3. Exploring the dataset.srt (1.3 KB)
    • 4. Decision tree structure.mp4 (6.4 MB)
    • 4. Decision tree structure.srt (1.3 KB)
    • 5. Implementation in python Importing libraries & datasets.mp4 (4.6 MB)
    • 5. Implementation in python Importing libraries & datasets.srt (0.8 KB)
    • 6. Implementation in python Encoding Categorical Data.mp4 (17.0 MB)
    • 6. Implementation in python Encoding Categorical Data.srt (3.4 KB)
    • 7. Implementation in python Splitting data into Train and Test Sets.mp4 (4.9 MB)
    • 7. Implementation in python Splitting data into Train and Test Sets.srt (0.9 KB)
    • 8. Implementation in python Results prediction & Accuracy.mp4 (10.4 MB)
    • 8. Implementation in python Results prediction & Accuracy.srt (2.7 KB)
    6. Classification Algorithms Logistic regression
    • 1. Introduction.mp4 (6.6 MB)
    • 1. Introduction.s

Download this course:

file type : Torrent
Files :
  • Torrent 1685.3 MB
*select one of the torrent file above to download the course
source: https://www.udemy.com/course/python-for-machine-learning-beginners/

Top reviews:

AW
Armel Wonga

Der Kurs ist sehr gut strukturiert und kurz und bündig verständlich!

ST
Sakshi Todkar

great content of python for machine learning

AU
Anushka Udara

Very good for beginners in machine learning

KM
Kevin Murgana

Excellent, direct and concise. A very practical and well detailed course. Highly recommended!

GG
Gemechu Gadisa

Its very interesting and make me improve my knowledge on machine learning using python.

SFGL
Sergio Flores González

Dinámico, divertido, entretenido y muy visual.

NYA
Nayibe Yesenia arias

Es el mejor curso que he visto hasta el momento, he entendido cosas que no había tenido oportunidad de entender jamás. Lo recomiendo totalmente.

KBR
Kalyan Balaji Rajgopal

Can teach in a very better way, that even a complete beginner can understand.

HZ
Hayley Zhang

??????????????????????????

BO
Brian Obure

It is interesting and I am gaining

GNVRJ
Gudupu Naga Venkata Rama Jayadev

THIS TUTORIAL TEACHES ME BASIC KNOWLEDGE ABOUT MACHINE LEARNING.

AL
Addakula Lavanya

nice course its usefule for me thank you


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