Data Science and Machine Learning using Python

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Section 1: Course introduction, tips, environment setup -- All you need to start!
- A message from the instructor!
- PreFace - Course overview
  FREE PREVIEW
- Tips to successful in the filed of Data Science
- Course Introduction
- Setup jupyter notebook and environment - part 1
- Setup jupyter notebook and environment - part 2
- Environment setup - Other Options/suggestions
- Please download the course material
- Please read.
- Possible update in the course.
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Section 2: Python Essential for Data Science & Machine Learning
- Python datatypes - Part 1
- Python datatypes - Part 2
- Comparisons Operators, if, else, elif statement
- Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)
- Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)
- Python Essentials Exercises Overview
- Python Essentials Exercises Solutions
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Section 3: NumPy Essentials
- What is Numpy? A brief introduction.
- NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.
  FREE PREVIEW
- NumPy Essentials - Indexing, slicing, broadcasting & boolean masking
- NumPy Essentials - Arithmetic Operations & Universal Functions
- NumPy Essentials Exercises Overview
- NumPy Essentials Exercises Solutions
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Section 4: Pandas Essentials
- What is pandas? A brief introduction and installation instructions.
- Pandas Introduction - little more!
- TIP: Good to know - How to avoid unnecessary future warning to keep your notebook clean!
- Pandas Essentials - Pandas Data Structures - Series
- Pandas Essentials - Pandas Data Structures - DataFrame
- Pandas Essentials - Hierarchical Indexing
- Pandas Essentials - Handling Missing Data
- Pandas Essentials - Data Wrangling - Combining, merging, joining
- Pandas Essentials - Groupby
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- Pandas Essentials - Useful Methods and Operations
- Pandas Essentials - Project 1 (Overview) Customer Purchases Data
- Pandas Essentials - Project 1 (Solutions) Customer Purchases Data
- Pandas Essentials - Project 2 (Overview) Chicago Payroll Data
- Pandas Essentials - Project 2 (Solutions Part 2) Chicago Payroll Data
- Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data
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Section 5: Python for Data Visualization using matplotlib
- Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach
- Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach
- Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach
- Matplotlib Essentials - Exercises Overview
- Matplotlib Essentials - Exercises Solutions
- Matplotlib Essentials (Optional) - Advance
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Section 6: Seaborn
- Seaborn - Introduction (& Installation - if needed)
- Seaborn - Distribution Plots
- Seaborn - Categorical Plots (Part 1)
- Seaborn - Categorical Plots (Part 2)
- Seaborn - Axis Grids
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- Seaborn - Matrix Plots
- Seaborn - Regression Plots
- Seaborn - Controlling Figure Aesthetics
- Seaborn - Exercises Overview
- Seaborn - Exercise Solutions
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Section 7: Python for Data Visualization using pandas
- Python for Data Visualization using pandas
- Pandas Data Visualization Exercises Overview
- Panda Data Visualization Exercises Solutions
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Section 8: Python for interactive & geographical plotting using Plotly and Cufflinks
- Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1)
- Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2)
- Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview)
- Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions)
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Section 9: Capstone Project - Python for Data Analysis & Visualization
- Project 1 - Oil vs Banks Stock Price during recession (Overview)
- Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1)
- Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2)
- Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3)
- Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview)
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Section 10: Python for Machine Learning (ML) - scikit-learn - Linear Regression Model
- Introduction to ML - What, Why and Types.....
- Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff
- TIP: How to avoid Future warning in your jupyter notebook!
- scikit-learn - Linear Regression Model - Hands-on (Part 1)
- scikit-learn - Linear Regression Model Hands-on (Part 2)
- Good to know: Pickle it! -- How to save and load your trained Machine Learning model
- scikit-learn - Linear Regression Model (Insurance Data Project Overview)
- scikit-learn - Linear Regression Model (Insurance Data Project Solutions)
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Section 11: Python for Machine Learning - scikit-learn - Logistic Regression Model
- Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity...etc.
- Output of classification report in scikit-learn — A small change
- scikit-learn - Logistic Regression Model - Hands-on (Part 1)
- scikit-learn - Logistic Regression Model - Hands-on (Part 2)
- scikit-learn - Logistic Regression Model - Hands-on (Part 3)
- scikit-learn - Logistic Regression Model - Hands-on (Project Overview)
- scikit-learn - Logistic Regression Model - Hands-on (Project Solutions)
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Section 12: Python for Machine Learning - scikit-learn - K Nearest Neighbors
- Theory: K Nearest Neighbors, Curse of dimensionality ....
- scikit-learn - K Nearest Neighbors - Hands-on
- scikt-learn - K Nearest Neighbors (Project Overview)
- scikit-learn - K Nearest Neighbors (Project Solutions)
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Section 13: Python for Machine Learning - scikit-learn - Decision Tree and Random Forests
- Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging....
- scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1)
  FREE PREVIEW
- scikit-learn - Decision Tree and Random Forests (Project Overview)
- scikit-learn - Decision Tree and Random Forests (Project Solutions)
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Section 14: Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)
- Support Vector Machines (SVMs) - (Theory Lecture)
- scikit-learn - Support Vector Machines - Hands-on (SVMs)
- scikit-learn - Support Vector Machines (Project 1 Overview)
- scikit-learn - Support Vector Machines (Project 1 Solutions)
- scikit-learn - Support Vector Machines (Optional Project 2 - Overview)
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Section 15: Python for Machine Learning - scikit-learn - K Means Clustering
- Theory: K Means Clustering, Elbow method .....
  FREE PREVIEW
- scikit-learn - K Means Clustering - Hands-on
- scikit-learn - K Means Clustering (Project Overview)
- scikit-learn - K Means Clustering (Project Solutions)
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Section 16: Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)
- Theory: Principal Component Analysis (PCA)
- scikit-learn - Principal Component Analysis (PCA) - Hands-on
- scikit-learn - Principal Component Analysis (PCA) - (Project Overview)
- scikit-learn - Principal Component Analysis (PCA) - (Project Solutions)
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Section 17: Recommender Systems with Python - (Additional Topic)
- Theory: Recommender Systems their Types and Importance
- Python for Recommender Systems - Hands-on (Part 1)
- Python for Recommender Systems - - Hands-on (Part 2)
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Section 18: Python for Natural Language Processing (NLP) - NLTK - (Additional Topic)
- Natural Language Processing (NLP) - (Theory Lecture)
  FREE PREVIEW
- NLTK - NLP-Challenges, Data Sources, Data Processing .....
- NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing
- NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW....
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- NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes ...
- NLTK - NLP - Pipeline feature to assemble several steps for cross-validation...
- Please read, it's important!
- Congratulations and thanks you for doing the course!

Data Science & Machine Learning using Python - A Complete Course

Course curriculum

Section 1: Course introduction, tips, environment setup -- All you need to start!

Section 2: Python Essential for Data Science & Machine Learning

Section 3: NumPy Essentials

Section 4: Pandas Essentials

Section 5: Python for Data Visualization using matplotlib

Section 6: Seaborn

Section 7: Python for Data Visualization using pandas

Section 8: Python for interactive & geographical plotting using Plotly and Cufflinks

Section 9: Capstone Project - Python for Data Analysis & Visualization

Section 10: Python for Machine Learning (ML) - scikit-learn - Linear Regression Model

Section 11: Python for Machine Learning - scikit-learn - Logistic Regression Model

Section 12: Python for Machine Learning - scikit-learn - K Nearest Neighbors

Section 13: Python for Machine Learning - scikit-learn - Decision Tree and Random Forests

Section 14: Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)

Section 15: Python for Machine Learning - scikit-learn - K Means Clustering

Section 16: Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)

Section 17: Recommender Systems with Python - (Additional Topic)

Section 18: Python for Natural Language Processing (NLP) - NLTK - (Additional Topic)