AI Engineering cohort with live weekend classes: https://codebasics.io/bootcamps/ai-engineering-bootcamp-software-engineers-3

Recorded bootcamp for AI engineering and data science: https://codebasics.io/bootcamps/gen-ai-data-science-bootcamp-with-virtual-internship

You are an outstanding teacher who can enhance the knowledge of the people. We are glad to witness the high quality content creators like you on YouTube over the past few years.

Topic Index
0:54 Why statistical machine learning still matters
2:17 Course overview and prerequisites
3:44 What is machine learning?
6:57 Labeled data, training and inference
10:15 Machine learning vs traditional programming
12:09 AI, machine learning and deep learning
13:33 Python, Jupyter and environment setup
16:22 Linear Regression
20:56 Mean Squared Error (MSE)
22:38 Gradient Descent
24:03 y = mx + b, slope and intercept
26:53 Independent vs dependent variables
27:38 Linear regression with scikit-learn
36:40 Multiple Linear Regression
42:50 Train/Test Split and model evaluation
49:49 R² score
51:50 random_state
54:13 Regression vs Classification
57:53 Binary vs Multiclass Classification
59:15 Logistic Regression
1:04:57 Sigmoid function
1:07:15 Logistic regression coding
1:20:18 Precision and Recall
1:25:11 F1 Score
1:25:44 Confusion Matrix
1:29:03 Multiclass Classification
1:34:28 Iris dataset exploration
1:37:05 Multiclass logistic regression
1:56:47 Data cleaning and preprocessing
2:04:03 One-Hot Encoding
2:07:21 Feature Scaling / StandardScaler
2:10:31 Support Vector Machine (SVM)
2:13:22 Support Vectors and margins
2:15:01 Hyperplanes and kernels
2:17:55 Overfitting and generalization
2:21:59 UCI ML Repository and datasets
2:26:11 Heart disease dataset preprocessing
2:39:21 SVM parameters: C and Gamma
2:41:23 Overfitting vs Underfitting
2:50:16 Model and parameter selection
2:54:36 Bias and Variance
3:01:11 Decision Trees
3:05:29 Choosing decision-tree splits
3:08:18 Gini Impurity
3:08:39 Decision Tree coding
3:18:32 Random Forest
3:23:00 Random Forest intuition and feature sampling
3:23:54 Titanic dataset example
3:26:42 Random Forest coding
3:30:37 Why one train/test split can be misleading
3:32:28 K-Fold Cross Validation
3:39:15 Train vs Validation vs Test data
3:45:55 Stratified K-Fold
3:52:22 Hyperparameter Tuning
3:52:28 GridSearchCV
3:58:38 RandomizedSearchCV
4:02:11 Ensemble Learning
4:03:56 Bagging
4:06:29 Boosting
4:07:39 XGBoost
4:09:11 Supervised vs Unsupervised Learning
4:11:48 Unsupervised learning and clustering
4:13:09 Clustering for outlier detection / DBSCAN
4:14:57 K-Means Clustering
4:15:19 Choosing K and centroids
4:20:07 Elbow Method
4:21:14 K-Means coding
4:23:34 Scaling data before clustering
4:25:51 Elbow plot in code
4:28:09 Capstone: Product Return Prediction
4:35:16 End-to-end ML workflow
4:37:00 Capstone implementation begins
4:41:34 Dataset exploration
4:52:07 Exploratory Data Analysis (EDA)
4:52:15 Data Cleaning
4:55:06 Feature Engineering
4:59:30 Log transformations and cleaned dataset
5:00:34 Train/Test Split
5:03:26 Outlier removal
5:04:24 Encoding categorical features
5:05:12 Handling missing values
5:06:28 Scaling and preprocessing
5:07:26 Comparing Logistic Regression, Decision Tree, Random Forest and XGBoost
5:08:31 Model evaluation metrics
5:09:17 Selecting the best model
5:10:55 Final confusion matrix
5:11:22 Feature coefficients / importance
5:14:06 Saving the trained model
5:14:31 Saving with Joblib
5:17:07 Building the backend and frontend
5:17:35 FastAPI backend
5:18:45 Node.js frontend
5:19:23 Running the application
5:21:47 End-to-end project recap
5:22:02 Final exercises

Thank you so much for this crash course at right time ❤ . Please do on Deep learning as well.

The timing of this video dropped is crazy in era of gen ai. 🎉🎉🎉🎉

Looking forward to the next one.

Thank you so much 🙏 i wanted to switch roles and this came right on time 🙏

thank you soo much sir for making this video your explanination is too good
a time given by you for making this video is totally worth it (honestly sir you teach a dumb student very well)  thank you from the bottom my heart🙏

Certified masterpiece.

Right video at the right time. I am searching for Deep learning roles, and all I find is Machine learning and NLP along with Airflow.

1:40:00 exercise

❤ thank u sir

thanks man

I have completed the Machine Learning course and it was very helpful. Pleasee do upload the Deep Learning course soon, as it would really help me with my project. Thank you!

2:33:35 It should have been model.fit(X_train, y_train). I think you trained the model on test data and later tested the model with test data as well...

This course is fantastic—your teaching is practical and easy to understand. I have one question: you mentioned that data scientists select models and deploy them to production. Isn’t  selecting models & deployment usually an ML engineer’s responsibility? Also, are these machine learning tutorials intended for ML/AI engineering roles or mainly data science roles?

I love your teaching from Afghanistan.
You are create good courses.
I am ML engineer based on Afghanistan.

Let’s get it

Sir  how can we select the right model for a dataset? can we take any number for the random_state?

TOP CLASS __ BUT IF YOU EXPLAINED IT A BIT DEEPLY IT WOULD BE APPRECIABLE __OTHERWISE VIDEO IS OUTSTANDING