What you’ll learn

  • Achieve the mastery in machine learning from simple linear regression to advanced reinforcement learning projects.
  • Get a deeper intuition about different Machine Learning nomenclatures.
  • Be able to manipulate different algorithms with the power of Mathematics.
  • Write different kinds of algorithms from scratch with Python.
  • Be able to preprocess any kind of Datasets.
  • Solve and Deal with different real-life and businesses problems from the outside world.
  • Deal with different machine learning and data science libraries like: Sikit-Learn, Pandas , NumPy & Matplotlib.
  • Explore the Data science world by handling, prepossessing and visualizing any kind of data set .
  • Make designs with advanced ML algorithms like the Reinforcement Leaning and handle different projects with the Gym library .

Introduction

Course Introduction
Course Guide
Machine Learning Analogy
Supervised Learning
Unsupervised, Semi-Supervised and Reinforcement Learning

 

……………… Supervised Learning ………………

Fasten your Belt and Enjoy the Ride!

 

—————– Regression —————–

Welcome to the Regression World!

 

Simple Linear Regression

The Essence of Simple Linear Regression (Housing Data Analysis)
Mathematics 1: The Hypothesis Function
Mathematics 2: The Cost Function
Mathematics 3: The Essence of The Gradient Descent
Mathematics 4: How GD Works?
Start where you’re. Use what you’ve. Do what you can!
Query 1: What about the Initialization?
Query 2: How to Adjust the Speed of Algorithm?
Query 3: What if it Was a Non-Convex Function?
Polymerization Between Gradient and Hypothesis
Don’t watch the clock. Do what it does. Keep going!
Let’s Start Coding!
Hello Anaconda!
Hello Jupyter Notebook!
Python 1: Required Libraries and Importing Data
What is The Unicode?
Python 2: Handling Data ( iloc Function )
Python 3: Handling Data ( Splitting Data into Train and Test Sets )
Python 4: Defining Main Function
Python 5: Defining The Gradient Descent Algorithm
Python 6: Debugging
Python 7: Scaling Data
Python 8: Defining Cost Function
Mathematics 5: SGD (Stochastic Gradient Descent)
Python 9: Stochastic Gradient Descent

 

Multiple Linear Regression

Welcome to Multiple Linear Regression
Basic Statistics and P-Value
R-Squared
The Essence of Multiple Linear Regression
Easy? No. Worth it? Absolutely.
Interpreting Coefficients in MLR
Preparation Steps 1: MLR Analysis (Business Problem Analysis)
Preparation Steps 2: Checking Linearity
Preparation Steps 3: Correlation Analysis
Success requires Effort.
Preparation Steps 4: Single Variable Regressions
Preparation Steps 5: Multiple Variable Regression
Choosing Best MLR Model
The Essence of Dummy Variables
Don’t stop when you’re tired. Stop when you’re done!
Applying Multiple Linear Regression Using Excel
Python 1: MLR (Stock Price Prediction)
Python 2: MLR (Stock Price Prediction)
Python 3: MLR Assignment (Human Life Expectancy)
Python 4: MLR Assignment (Human Life Expectancy)
Life Expectancy Assignment (Kaggle Problem)

 

Ridge & Lasso Regression

Python 1: Ridge Regression (Business Problem)
L1 & L2 Regularization Techniques
Python 2: Ridge Regression (Business Problem)
Python 3: Ridge Regression (Business Problem)
Python 4: Lasso Regression (Business Problem)

 

Polynomial Regression

The Essence of Residual Plots
Polynomial Regression VS Quadratic Regression
The Essence of Over-fitting
Python: Polynomial Regression

 

Decision Trees & Random Forests Regression

The Essence of Decision Trees Regressor
Python 1: Regression Trees (Petrol Consumption Prediction)
Python 2: Regression Trees (Business Problem)
The Essence of Random Forests Regression

 

—————– CLASSIFICATION —————–

Welcome to the Classification World!

 

Logistic Regression Classifier

The Essence of Logistic Regression Classifier
Mathematics 1: Logistic Regression ( The Hypothesis Function )
Mathematics 2: Logistic Regression ( Examples On The Hypothesis Function )
Mathematics 3: Logistic Regression ( The Cost Function )
Mathematics 4: Logistic Regression ( Estimating the parameters Thetas )
Python 1: Logistic Regression ( SKlearn generated Data_1 )
Python 2: Logistic Regression ( SKlearn generated Data_2 )
Python 3: Logistic Regression ( Spam Filter Problem Simulation )
Python 4: Logistic Regression (Buying Houses Business Problem )
Multi-Class Logistic Regression ( One Vs All Algorithm ) !
Logistic Regression Optimization ( Overfitting Problem )
Python 5: Multi-Class Logistic Regression ( Hotels Evaluation Business Problem )

 

Decision Tree VS Random Forest Classifiers

The Essence of Decision Trees classifier
Decision Trees Optimization (Overfitting Problem)
Mathematics: Decision Trees (The Entropy Algorithm)
Installing GV
Python 1: Decision Trees (Website Campaign Business Problem)
Python/GV 2: Optimizing DT Algorithm Results (Website Campaign Business Problem)
The Essence of Random Forest Classifier
Python 3: Random Forest (Website Campaign Business Problem)

 

Naive Bayes Classifier

Mathematics 1: Probability Basics
Mathematics 2: Bayes’ Theorem
The Essence of Naive Bayes Classifier
Solving an Example Manually (Gaussian Naive Bayes)
Manually Solved 1: Email Classification Example (Multinomial Naive Bayes)
Manually Solved 2: Email Classification Example (Multinomial Naive Bayes)
Python 1: Gaussian Naive Bayes (Hiring New Applicants Business Problem)
Multinomial Naive Bayes (Email Classification Problem)
Python 2: Multinomial Naive Bayes (Email Classification Problem)

 

Support Vector Machine Classifier

The Essence of Support Vector Machines
Mathematics 1: Support Vector Machines (The Hypothesis Function)
Mathematics 2: Support Vector Machines (Cost Function Regularization)
Python 1: Support Vector Machines (Bank Credit Cards Business Problem)
Python 2: Support Vector Machines (SKlearn Generated Data)
The Essence of Handwritten Digits Recognition
Python 3: Support Vector Machines (Handwritten Digits Recognition)

 

Kernel Support Vector Machine

Why Kernel SVM?
The Essence of Kernel SVM (Kernel Trick | The similarity Function)
Mathematics 1: Kernel SVM (Example On The Kernel Trick)
Mathematics 2: Kernel SVM (Different Types of Kernel Functions)
Python 1: Gaussian Kernel SVM (Solving Bank Credit Cards Business Problem)
Python 2: Gaussian Kernel SVM (Optimizing The Model Results)
Python 3: Kernel SVM (SKlearn Breast Cancer Dataset)
Python 4: Kernel SVM (Gaussian – Sigmoid – Polynomial) Kernels

 

K-Nearest Neighbor Classifier

The Essence of K-Nearest Neighbor
Mathematics: K-Nearest Neighbor (Solving An Example Manually)
Python 1: K-nearest Neighbor (Buying Houses Business Problem)
Python 2: K-nearest Neighbor (SKLearn Iris Data set)

 

Evaluation of Classification Models

Confusion Matrix
Evaluation Parameters

 

……………… Unsupervised Learning ………………

Keep Calm and Enjoy Unsupervised Learning.

 

—————– Clustering —————–

Welcome to the Clustering World!
The Essence of Clustering Techniques (Digital Marketing and Finances)
Categories of Clustering
I want to see what happens if I don’t give up.
Mathematics 1: Hierarchical Clustering
Mathematics 2: Single Linkage
Mathematics 3: Average and Centroid Linkage
It always seems impossible until it’s done.
Python 1: Agglomerative Hierarchical Clustering
Python 2: Agglomerative Hierarchical Clustering
Python 3: Agglomerative Hierarchical Clustering
The Essence of K-Means Clustering
Mathematics 4: The Elbow Method
Mathematics 5: K-Means++
Python 4: K-Means Clustering

 

—————– Dimensionality Reduction —————–

Welcome to the Dimensionality Reduction World!
The Curse of Dimensionality
The Essence of Dimensionality Reduction
Mathematics 1: Dimensionality Reduction
Mathematics 2: Principal Component Analysis
Mathematics 3: Principal Component Analysis
The Essence of Eigenvectors and Eigenvalues
Mathematics 4: How to Find Eigenvectors and Eigenvalues?
Mathematics 3: Applying PCA on an Example
Python: Applying PCA (Breast Cancer Dataset)

 

—————– Data Analysis —————–

Welcome to the World of Data Analysis!

 

Pandas (Python Library for Handling Data)

Creating Series Object
Information about series
Peeking at data
Accessors(loc_iloc_ix)
Arithmetic Operations
Reindexing Series
Slicing Series
Creating DataFrame
Operations on the DataFrame Columns
Selecting rows of DataFrame
Modifying DataFrame
Modifying DataFrame ll
Arithmtic Operations
Hierarchical index and reindexing
Importing Data
Tidying Up Data
Dealing with missing data
Dealing with missing data ll
Dealing with missing data lll
Duplicated data
How to tidy data up
Concatenation
Merging Data
Merging Data ll
Intro to SAC
Grouping Data
Grouping Data ll
Applying
Applying 2
Applying3
Intro to time series object
times series object 1
times series object 2
times series object 3
times series object 4

 

Matplotlib (Python Library for Visualizing Data)

Introduction to Matplotlib
Matplotlib_1
Matplotlib_2

Leave a Reply

Your email address will not be published. Required fields are marked *