Complete Python Machine Learning & Data Science for Dummies
Complete Python Machine Learning & Data Science for Dummies, available at $54.99, has an average rating of 4.15, with 90 lectures, based on 73 reviews, and has 2350 subscribers.
You will learn about Machine Learning and Data Science using Python for Beginners This course is ideal for individuals who are Beginners who are interested in Machine Learning using Python It is particularly useful for Beginners who are interested in Machine Learning using Python.
Enroll now: Complete Python Machine Learning & Data Science for Dummies
Summary
Title: Complete Python Machine Learning & Data Science for Dummies
Price: $54.99
Average Rating: 4.15
Number of Lectures: 90
Number of Published Lectures: 90
Number of Curriculum Items: 90
Number of Published Curriculum Objects: 90
Original Price: $139.99
Quality Status: approved
Status: Live
What You Will Learn
- Machine Learning and Data Science using Python for Beginners
Who Should Attend
- Beginners who are interested in Machine Learning using Python
Target Audiences
- Beginners who are interested in Machine Learning using Python
Hi.. Hello and welcome to my new course, Machine Learning with Python for Dummies. We will discuss about the overview of the course and the contents included in this course.
Artificial Intelligence, Machine Learning and Deep Learning Neural Networks are the most used terms now a days in the technology world. Its also the most mis-understood and confused terms too.
Artificial Intelligence is a broad spectrum of science which tries to make machines intelligent like humans. Machine Learning and Neural Networks are two subsets that comes under this vast machine learning platform
Lets check what’s machine learning now. Just like we human babies, we were actually in our learning phase then. We learned how to crawl, stand, walk, then speak words, then make simple sentences.. We learned from our experiences. We had many trials and errors before we learned how to walk and talk. The best trials for walking and talking which gave positive results were kept in our memory and made use later. This process is highly compared to a Machine Learning Mechanism
Then we grew young and started thinking logically about many things, had emotional feelings, etc. We kept on thinking and found solutions to problems in our daily life. That’s what the Deep Learning Neural Network Scientists are trying to achieve. A thinking machine.
But in this course we are focusing mainly in Machine Learning. Throughout this course, we are preparing our machine to make it ready for a prediction test. Its Just like how you prepare for your Mathematics Test in school or college. We learn and train ourselves by solving the most possible number of similar mathematical problems. Lets call these sample data of similar problems and their solutions as the ‘Training Input’ and ‘Training Output’ Respectively. And then the day comes when we have the actual test. We will be given new set of problems to solve, but very similar to the problems we learned, and based on the previous practice and learning experiences, we have to solve them. We can call those problems as ‘Testing Input’ and our answers as ‘Predicted Output’. Later, our professor will evaluate these answers and compare it with its actual answers, we call the actual answers as ‘Test Output’. Then a mark will be given on basis of the correct answers. We call this mark as our ‘Accuracy’. The life of a machine learning engineer and a data-scientist is dedicated to make this accuracy as good as possible through different techniques and evaluation measures.
Here are the major topics that are included in this course. We are using Python as our programming language. Python is a great tool for the development of programs which perform data analysis and prediction. It has tons of classes and features which perform the complex mathematical analysis and give solutions in simple one or two lines of code so that we don’t have to be a statistic genius or mathematical Nerd to learn data science and machine learning. Python really makes things easy.
These are the main topics that are included in our course
System and Environment preparation
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Installing Python and Required Libraries (Anaconda)
Basics of python and sci-py
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Python, Numpy , Matplotlib and Pandas Quick Courses
Load data set from csv / url
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Load CSV data with Python, NumPY and Pandas
Summarize data with description
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Peeking data, Data Dimensions, Data Types, Statistics, Class Distribution, Attribute Correlations, Univariate Skew
Summarize data with visualization
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Univariate, Multivariate Plots
Prepare data
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Data Transforms, Rescaling, Standardizing, Normalizing and Binarization
Feature selection – Automatic selection techniques
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Univariate Selection, Recursive Feature Elimination, Principle Component Analysis and Feature Importance
Machine Learning Algorithm Evaluation
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Train and Test Sets, K-fold Cross Validation, Leave One Out Cross Validation, Repeated Random Test-Train Splits.
Algorithm Evaluation Metrics
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Classification Metrics – Classification Accuracy, Logarithmic Loss, Area Under ROC Curve, Confusion Matrix, Classification Report.
Regression Metrics – Mean Absolute Error, Mean Squared Error, R 2.
Spot-Checking Classification Algorithms
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Linear Algorithms – Logistic Regression, Linear Discriminant Analysis.
Non-Linear Algorithms – k-Nearest Neighbours, Naive Bayes, Classification and Regression Trees, Support Vector Machines.
Spot-Checking Regression Algorithms
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Linear Algorithms – Linear Regression, Ridge Regression, LASSO Linear Regression and Elastic Net Regression.
Non-Linear Algorithms – k-Nearest Neighbours, Classification and Regression Trees, Support Vector Machines.
Choose The Best Machine Learning Model
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Compare Logistic Regression, Linear Discriminant Analysis, k-Nearest Neighbours, Classification and Regression Trees, Naive Bayes, Support Vector Machines.
Automate and Combine Workflows with Pipeline
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Data Preparation and Modelling Pipeline
Feature Extraction and Modelling Pipeline
Performance Improvement with Ensembles
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Voting Ensemble
Bagging: Bagged Decision Trees, Random Forest, Extra Trees
Boosting: AdaBoost, Gradient Boosting
Performance Improvement with Algorithm Parameter Tuning
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Grid Search Parameter
Random Search Parameter Tuning
Save and Load (serialize and deserialize) Machine Learning Models
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Using pickle
Using Joblib
finalize a machine learning project
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steps For Finalizing classification models – pima indian dataset
Dealing with imbalanced class problem
steps For Finalizing multi class models – iris flower dataset
steps For Finalizing regression models – boston housing dataset
Predictions and Case Studies
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Case study 1: predictions using the Pima Indian Diabetes Dataset
Case study: Iris Flower Multi Class Dataset
Case study 2: the Boston Housing cost Dataset
Machine Learning and Data Science is the most lucrative job in the technology arena now a days. Learning this course will make you equipped to compete in this area.
Best wishes with your learning. Se you soon in the class room.
Course Curriculum
Chapter 1: Course Overview & Table of Contents
Lecture 1: Course Overview & Table of Contents
Chapter 2: Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types
Lecture 1: Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types
Chapter 3: Introduction to Machine Learning – Part 2 – Classifications and Applications
Lecture 1: Introduction to Machine Learning – Part 2 – Classifications and Applications
Chapter 4: System and Environment preparation – Part 1
Lecture 1: System and Environment preparation – Part 1
Chapter 5: System and Environment preparation – Part 2
Lecture 1: System and Environment preparation – Part 2
Chapter 6: Learn Basics of python – Assignment
Lecture 1: Learn Basics of python – Assignment
Chapter 7: Learn Basics of python – Flow Control
Lecture 1: Learn Basics of python – Assignment
Chapter 8: Learn Basics of python – Functions
Lecture 1: Learn Basics of python – Functions
Chapter 9: Learn Basics of python – Data Structures
Lecture 1: Learn Basics of python – Data Structures
Chapter 10: Learn Basics of NumPy – NumPy Array
Lecture 1: Learn Basics of NumPy – NumPy Array
Chapter 11: Learn Basics of NumPy – NumPy Data
Lecture 1: Learn Basics of NumPy – NumPy Data
Chapter 12: Learn Basics of NumPy – NumPy Arithmetic
Lecture 1: Learn Basics of NumPy – NumPy Arithmetic
Chapter 13: Learn Basics of Matplotlib
Lecture 1: Learn Basics of Matplotlib
Chapter 14: Learn Basics of Pandas – Part 1
Lecture 1: Learn Basics of Pandas – Part 1
Chapter 15: Learn Basics of Pandas – Part 2
Lecture 1: Learn Basics of Pandas – Part 2
Chapter 16: Understanding the CSV data file
Lecture 1: Understanding the CSV data file
Chapter 17: Load and Read CSV data file using Python Standard Library
Lecture 1: Load and Read CSV data file using Python Standard Library
Chapter 18: Load and Read CSV data file using NumPy
Lecture 1: Load and Read CSV data file using NumPy
Chapter 19: Load and Read CSV data file using Pandas
Lecture 1: Load and Read CSV data file using Pandas
Chapter 20: Dataset Summary – Peek, Dimensions and Data Types
Lecture 1: Dataset Summary – Peek, Dimensions and Data Types
Chapter 21: Dataset Summary – Class Distribution and Data Summary
Lecture 1: Dataset Summary – Class Distribution and Data Summary
Chapter 22: Dataset Summary – Explaining Correlation
Lecture 1: Dataset Summary – Explaining Correlation
Chapter 23: Dataset Summary – Explaining Skewness – Gaussian and Normal Curve
Lecture 1: Dataset Summary – Explaining Skewness – Gaussian and Normal Curve
Chapter 24: Dataset Visualization – Using Histograms
Lecture 1: Dataset Visualization – Using Histograms
Chapter 25: Dataset Visualization – Using Density Plots
Lecture 1: Dataset Visualization – Using Density Plots
Chapter 26: Dataset Visualization – Box and Whisker Plots
Lecture 1: Dataset Visualization – Box and Whisker Plots
Chapter 27: Multivariate Dataset Visualization – Correlation Plots
Lecture 1: Multivariate Dataset Visualization – Correlation Plots
Chapter 28: Multivariate Dataset Visualization – Scatter Plots
Lecture 1: Multivariate Dataset Visualization – Scatter Plots
Chapter 29: Data Preparation (Pre-Processing) – Introduction
Lecture 1: Data Preparation (Pre-Processing) – Introduction
Chapter 30: Data Preparation – Re-scaling Data – Part 1
Lecture 1: Data Preparation – Re-scaling Data – Part 1
Chapter 31: Data Preparation – Re-scaling Data – Part 2
Lecture 1: Data Preparation – Re-scaling Data – Part 2
Chapter 32: Data Preparation – Standardizing Data – Part 1
Lecture 1: Data Preparation – Standardizing Data – Part 1
Chapter 33: Data Preparation – Standardizing Data – Part 2
Lecture 1: Data Preparation – Standardizing Data – Part 2
Chapter 34: Data Preparation – Normalizing Data
Lecture 1: Data Preparation – Normalizing Data
Chapter 35: Data Preparation – Binarizing Data
Lecture 1: Data Preparation – Binarizing Data
Chapter 36: Feature Selection – Introduction
Lecture 1: Feature Selection – Introduction
Chapter 37: Feature Selection – Uni-variate Part 1 – Chi-Squared Test
Lecture 1: Feature Selection – Uni-variate Part 1 – Chi-Squared Test
Chapter 38: Feature Selection – Uni-variate Part 2 – Chi-Squared Test
Lecture 1: Feature Selection – Uni-variate Part 2 – Chi-Squared Test
Chapter 39: Feature Selection – Recursive Feature Elimination
Lecture 1: Feature Selection – Recursive Feature Elimination
Chapter 40: Feature Selection – Principal Component Analysis (PCA)
Lecture 1: Feature Selection – Principal Component Analysis (PCA)
Chapter 41: Feature Selection – Feature Importance
Lecture 1: Feature Selection – Feature Importance
Chapter 42: Refresher Session – The Mechanism of Re-sampling, Training and Testing
Lecture 1: Refresher Session – The Mechanism of Re-sampling, Training and Testing
Chapter 43: Algorithm Evaluation Techniques – Introduction
Lecture 1: Algorithm Evaluation Techniques – Introduction
Chapter 44: Algorithm Evaluation Techniques – Train and Test Set
Lecture 1: Algorithm Evaluation Techniques – Train and Test Set
Chapter 45: Algorithm Evaluation Techniques – K-Fold Cross Validation
Lecture 1: Algorithm Evaluation Techniques – K-Fold Cross Validation
Chapter 46: Algorithm Evaluation Techniques – Leave One Out Cross Validation
Lecture 1: Algorithm Evaluation Techniques – Leave One Out Cross Validation
Chapter 47: Algorithm Evaluation Techniques – Repeated Random Test-Train Splits
Lecture 1: Algorithm Evaluation Techniques – Repeated Random Test-Train Splits
Chapter 48: Algorithm Evaluation Metrics – Introduction
Lecture 1: Algorithm Evaluation Metrics – Introduction
Chapter 49: Algorithm Evaluation Metrics – Classification Accuracy
Lecture 1: Algorithm Evaluation Metrics – Classification Accuracy
Chapter 50: Algorithm Evaluation Metrics – Log Loss
Lecture 1: Algorithm Evaluation Metrics – Log Loss
Instructors
-
Abhilash Nelson
Computer Engineering Master & Senior Programmer at Dubai
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 4 votes
- 3 stars: 8 votes
- 4 stars: 26 votes
- 5 stars: 33 votes
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