لغة الآلة | codebasics
codebasics
I am Dhaval Patel, Founder of the online education platform codebasics.io and a co-founder of a software & data company called AtliQ Technologies (Ex. Bloomberg, NVIDIA). Teaching is my passion. The goal of this channel is to make the BEST quality DATA education available to all for free. I have more than 12+ years of DATA INDUSTRY experience (mainly in the USA) and the co-founder of this channel Hemanand Vadivel has 6+ years of DATA INDUSTRY experience in Europe. We teach based on our real-life
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- 1 | Machine Learning Tutorial Python -1: What is Machine Learning? 00:06:51
- 2 | Machine Learning Tutorial Python - 2: Linear Regression Single Variable 00:15:14
- 3 | Machine Learning Tutorial Python - 3: Linear Regression Multiple Variables 00:14:08
- 4 | Machine Learning Tutorial Python - 4: Gradient Descent and Cost Function 00:28:26
- 5 | Machine Learning Tutorial Python - 5: Save Model Using Joblib And Pickle 00:08:21
- 6 | Machine Learning Tutorial Python - 6: Dummy Variables & One Hot Encoding 00:21:35
- 7 | Machine Learning Tutorial Python - 7: Training and Testing Data 00:06:34
- 8 | Machine Learning Tutorial Python - 8: Logistic Regression (Binary Classification) 00:19:19
- 9 | Machine Learning Tutorial Python - 8 Logistic Regression (Multiclass Classification) 00:15:43
- 10 | Machine Learning Tutorial Python - 9 Decision Tree 00:14:46
- 11 | Machine Learning Tutorial Python - 10 Support Vector Machine (SVM) 00:23:22
- 12 | Machine Learning Tutorial Python - 11 Random Forest 00:12:48
- 13 | Machine Learning Tutorial Python 12 - K Fold Cross Validation 00:25:20
- 14 | Machine Learning Tutorial Python - 13: K Means Clustering Algorithm 00:25:15
- 15 | Machine Learning Tutorial Python - 14: Naive Bayes Classifier Algorithm Part 1 00:13:38
- 16 | Machine Learning Tutorial Python - 15: Naive Bayes Classifier Algorithm Part 2 00:11:28
- 17 | Machine Learning Tutorial Python - 16: Hyper parameter Tuning (GridSearchCV) 00:16:30
- 18 | Machine Learning Tutorial Python - 17: L1 and L2 Regularization Lasso, Ridge Regression 00:19:21
- 19 | Machine Learning Tutorial Python - 18: K nearest neighbors classification with python code 00:15:42
- 20 | Machine Learning Tutorial Python - 19: Principal Component Analysis (PCA) with Python Code 00:24:09
- 21 | Machine Learning Tutorial Python - 20: Bias vs Variance In Machine Learning 00:10:50
- 22 | Machine Learning Tutorial Python - 21: Ensemble Learning - Bagging 00:23:37
- 23 | Machine Learning & Data Science Project - 1 : Introduction (Real Estate Price Prediction Project) 00:02:12
- 24 | Machine Learning & Data Science Project - 2 : Data Cleaning (Real Estate Price Prediction Project) 00:15:21
- 25 | Machine Learning & Data Science Project - 3 : Feature Engineering (Real Estate Price Prediction) 00:08:26
- 26 | Machine Learning & Data Science Project - 4 : Outlier Removal (Real Estate Price Prediction Project) 00:19:31
- 27 | Machine Learning & Data Science Project - 5 : Model Building (Real Estate Price Prediction Project) 00:19:12
- 28 | Machine Learning & Data Science Project - 6 : Python Flask Server (Real Estate Price Prediction) 00:21:13
- 29 | Machine Learning & Data Science Project - 7 : Website or UI (Real Estate Price Prediction Project) 00:13:21
- 30 | Deploy machine learning model to production AWS (Amazon EC2 instance) 00:28:38
- 31 | Data Science & Machine Learning Project - Part 1 Introduction Image Classification 00:04:42
- 32 | Data Science & Machine Learning Project - Part 2 Data Collection Image Classification 00:05:37
- 33 | Data Science & Machine Learning Project - Part 3 Data Cleaning Image Classification 00:40:45
- 34 | Data Science & Machine Learning Project - Part 4 Feature Engineering Image Classification 00:18:23
- 35 | Data Science & Machine Learning Project - Part 5 Training a Model Image Classification 00:20:01
- 36 | Data Science & Machine Learning Project - Part 6 Flask Server Image Classification 00:36:16
- 37 | Data Science & Machine Learning Project - Part 7 Build Website Image Classification 00:38:14
- 38 | Data Science & Machine Learning Project - Part 8 Deployment & Exercise Image Classification 00:05:17
- 39 | What is feature engineering Feature Engineering Tutorial Python # 1 00:05:12
- 40 | Outlier detection and removal using percentile Feature engineering tutorial python # 2 00:17:18
- 41 | Outlier detection and removal: z score, standard deviation Feature engineering tutorial python # 3 00:20:05
- 42 | Outlier detection and removal using IQR Feature engineering tutorial python # 4 00:08:02
تقييمات الطلاب
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١٢ تقييمMohamed Emad
04-07-2023
Azzani Abdullah
19-12-2022
Mohamed Soliman
29-08-2022
Yousef Mohamed
18-05-2022
Ayman Muhammed
18-10-2021
Mona Ali
26-05-2021
youtube
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Mohamed Kaamal
10-12-2021
Khaled Mousa
08-11-2022
Machine Learning Tutorial Python Machine Learning For Beginners
In this video series, we are going to learn about machine learning with Python. There are activities where computers are better than humans, and some activities where humans are better than computers. In simple words, machine learning is the technique that makes computers better at things than humans. It’s about making machines learn the things humans do.
Machine learning is a bigger area, of which deep learning and mathematical models are important parts. One might think about why it is important to learn machine learning? It is because it has a big implication in real life too. Spam filtering in emails, voice assistance devices, online video or music streaming recommendations, are a few of the real-life applications of machine learning which are already available to us. However, the best use of this feature can be expected in the near-decade in the form of driverless cars. Go through this machine learning playlist to understand these concepts more easily.
This machine learning tutorials playlist includes linear regression, gradient descent, logistic regression, decision tree, support vector, K-fold cross-validation, KNN classification, etc. The playlist also includes several practical implication projects which will help you get a more clear understanding of the concept. Another topic included in this machine learning course is Feature Engineering, which is an important part of data analytics and machine learning.