Pedram Jahangiry

Module 5- Python: Linear Regression Machine Learning approach (sklearn, PyCaret)

Module 5- Theory: Linear to Polynomial Mastering Regression Models in ML Theory Explained

Module 8- Python: Mastering KNN in Python- A Complete Guide with Scikit-learn and PyCaret

Module 10- Python 3: Mastering Machine Learning Boosting algorithms in (Scikit Learn and Pycaret)

Module 7- Python- Logistic Regression in Action Classifying Data with Scikit-Learn and Pycaret

Module 3- Part 2- ML boosting algorithms XGBoost, CatBoost and LightGBM

Module 4- Part 2- Deep Neural Networks Regularization techniques

VS code: 2-Python extensions

Part 35-Boosting algorithms in machine learning (AdaBoost, GBM, XGBoost)

VS code: 4-Terminal (Python cwd and environments)

Module 9- Python: Mastering Decision Trees: A Comprehensive Guide with Sklearn and PyCaret

Class 11 Machine Learning Logistic regression in Python

Module 3- Mastering Linear Regression in Python with Statsmodels: A Step-by-Step Guide

Module 6- Python 2- NLP - IMDB Sentiment Analysis - Bag of Words vs Sequence Models in TensorFlow!

Module 11- Python: Mastering PCA & Kernel PCA in Python using Sklearn and pca packages

Python Crash Course Part 5 Pandas

Module 4- Part 3- Deep Neural Networks with Python Tensorflow

Module 6- Python1- Master Multi-Feature Timeseries Forecasting with LSTM in TensorFlow

Module 3- Understanding Linear Regression Through an Econometrics Lens

Part 8-Machine learning solvers BEYOND Gradient Descent (SGD, Momentum, Adagrad, Adam)

Part 26-Support Vector Machines Regression

Module 12- Python part1: Mastering Clustering techniques using Sklearn (Kmeans, Hierarchical)

Part 29-Decision Tree Regression and classification models

VS code: 7-Github and sync setting (part 2)

3. Google Colab: Tricks!!!

Python Crash Course Part 4 Numpy

Machine Learning, Deep Learning and Deep forecasting course content (2023 road map)

Part 10-Feature scaling in machine learning

1-PyCaret introduction (low code machine learning Python package)

VS code: 6-Github and sync setting (part 1)

Module 5- Python 2- Convolutional Neural Networks (CNN) + data augmentation using Fashion MNIST

Module 3- boosting algorithms with PyCaret (XGBoost, Catboost and lightGBM)

Module 4- Part 1- The Anatomy of Machine Learning Models - what is Overfitting?

Module 2- Setting up Deep Learning Environment

Python Crash course Part 1 Installation

VS code: 5-Interactive Window

Part 24-SVM Classification (hard margin and soft margin)

Module 10- Theory 2: Machine Learning Boosting techniques: AdaBoost, GBM and XGboost

Part 19-Performance metrics for machine learning classification models

Module 3- part 2- ETS (Error, Trend, Seasonality) timeseries models

2-PyCaret installation on Windows and Google Colab

Part 13-Regularization and Penalized regression in machine learning

Class 17 Machine Learning SVM Regression in Python

Module 5- Python 1- basic CNN using MNIST data and Tensorflow

2. Google Colab: Basics! writing text, code and auto completion

VS code: 3-aesthetic extensions