Data Science & Machine Learning with Python
Category: DevelopmentCategory: Personal Development
Course Info
Overview
Develop a Data Science & Machine Learning with Python arsenal of skills including critical strategic, managerial, and leadership abilities with our expertly developed Data Science & Machine Learning with Python.
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To enhance your learning outcomes, this career-focused Data Science & Machine Learning with Python curriculum employs a variety of interactive modules to provide you with abilities that are relevant to your career. This Data Science & Machine Learning with Python course was also created with professionals in mind and is tailored to fit with your busy schedule.
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Description
This Data Science & Machine Learning with Python course will teach you how to think critically and strategically about Data Science & Machine Learning with Python, as well as how to build and implement strategy. You’ll also discover fundamental Data Science & Machine Learning with Python ideas that will help you build the foundation you need to flourish in the workplace.This Learning Paths online Data Science & Machine Learning with Python course is for working people who want to improve their abilities and advance in their careers. The Data Science & Machine Learning with Python course is provided in an interactive virtual learning environment where you can study at your own comfort and convenience.
Who is this course for?
This certificate Data Science & Machine Learning with Python course is curated for individuals who want to improve their hard and soft skills. The interactive, guided approach to learning and the potential to build their worldwide network online will help you thrive. Moreover, working professionals in managerial and leadership roles in a variety of industries will benefit from the emphasis on the dynamics of leadership, influence, and strategy, as well as Data Science & Machine Learning with Python abilities. Those pursuing career advancement in the future will benefit from the skill to apply Data Science & Machine Learning with Python-derived abilities to current and future employment.
Requirements
This Learning Paths Data Science & Machine Learning with Python course will prepare you to make data-driven decisions that will give you a competitive edge. There are no formal requirements for this course. However smart gadgets and stable internet connection is required for a smooth learning journey.
Career Path
Earn a certificate of competence from the Learning Paths platform by learning all about Data Science & Machine Learning with Python. This Data Science & Machine Learning with Python course curriculum will benefit you at all stages of your career.
Certification
After successfully completing the Data Science & Machine Learning with Python course, you will get your PDF certificate for FREE! The hardcopy certificate will cost only £11.99 with free shipping inside the UK. For delivery outside the UK an additional shipping charge will be applied.
Course Curriculum
| Course Overview & Table of Contents | |||
| Course Overview & Table of Contents | 00:09:00 | ||
| Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types | |||
| Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types | 00:05:00 | ||
| Introduction to Machine Learning - Part 2 - Classifications and Applications | |||
| Introduction to Machine Learning – Part 2 – Classifications and Applications | 00:06:00 | ||
| System and Environment preparation - Part 1 | |||
| System and Environment preparation – Part 1 | 00:04:00 | ||
| System and Environment preparation - Part 2 | |||
| System and Environment preparation – Part 2 | 00:06:00 | ||
| Learn Basics of python - Assignment | |||
| Learn Basics of python – Assignment 1 | 00:10:00 | ||
| Learn Basics of python - Assignment | |||
| Learn Basics of python – Assignment 2 | 00:09:00 | ||
| Learn Basics of python - Functions | |||
| Learn Basics of python – Functions | 00:04:00 | ||
| Learn Basics of python - Data Structures | |||
| Learn Basics of python – Data Structures | 00:12:00 | ||
| Learn Basics of NumPy - NumPy Array | |||
| Learn Basics of NumPy – NumPy Array | 00:06:00 | ||
| Learn Basics of NumPy - NumPy Data | |||
| Learn Basics of NumPy – NumPy Data | 00:08:00 | ||
| Learn Basics of NumPy - NumPy Arithmetic | |||
| Learn Basics of NumPy – NumPy Arithmetic | 00:04:00 | ||
| Learn Basics of Matplotlib | |||
| Learn Basics of Matplotlib | 00:07:00 | ||
| Learn Basics of Pandas - Part 1 | |||
| Learn Basics of Pandas – Part 1 | 00:06:00 | ||
| Learn Basics of Pandas - Part 2 | |||
| Learn Basics of Pandas – Part 2 | 00:07:00 | ||
| Understanding the CSV data file | |||
| Understanding the CSV data file | 00:09:00 | ||
| Load and Read CSV data file using Python Standard Library | |||
| Load and Read CSV data file using Python Standard Library | 00:09:00 | ||
| Load and Read CSV data file using NumPy | |||
| Load and Read CSV data file using NumPy | 00:04:00 | ||
| Load and Read CSV data file using Pandas | |||
| Load and Read CSV data file using Pandas | 00:05:00 | ||
| Dataset Summary - Peek, Dimensions and Data Types | |||
| Dataset Summary – Peek, Dimensions and Data Types | 00:09:00 | ||
| Dataset Summary - Class Distribution and Data Summary | |||
| Dataset Summary – Class Distribution and Data Summary | 00:09:00 | ||
| Dataset Summary - Explaining Correlation | |||
| Dataset Summary – Explaining Correlation | 00:11:00 | ||
| Dataset Summary - Explaining Skewness - Gaussian and Normal Curve | |||
| Dataset Summary – Explaining Skewness – Gaussian and Normal Curve | 00:07:00 | ||
| Dataset Visualization - Using Histograms | |||
| Dataset Visualization – Using Histograms | 00:07:00 | ||
| Dataset Visualization - Using Density Plots | |||
| Dataset Visualization – Using Density Plots | 00:06:00 | ||
| Dataset Visualization - Box and Whisker Plots | |||
| Dataset Visualization – Box and Whisker Plots | 00:05:00 | ||
| Multivariate Dataset Visualization - Correlation Plots | |||
| Multivariate Dataset Visualization – Correlation Plots | 00:08:00 | ||
| Multivariate Dataset Visualization - Scatter Plots | |||
| Multivariate Dataset Visualization – Scatter Plots | 00:05:00 | ||
| Data Preparation (Pre-Processing) - Introduction | |||
| Data Preparation (Pre-Processing) – Introduction | 00:09:00 | ||
| Data Preparation - Re-scaling Data - Part 1 | |||
| Data Preparation – Re-scaling Data – Part 1 | 00:09:00 | ||
| Data Preparation - Re-scaling Data - Part 2 | |||
| Data Preparation – Re-scaling Data – Part 2 | 00:09:00 | ||
| Data Preparation - Standardizing Data - Part 1 | |||
| Data Preparation – Standardizing Data – Part 1 | 00:07:00 | ||
| Data Preparation - Standardizing Data - Part 2 | |||
| Data Preparation – Standardizing Data – Part 2 | 00:04:00 | ||
| Data Preparation - Normalizing Data | |||
| Data Preparation – Normalizing Data | 00:08:00 | ||
| Data Preparation - Binarizing Data | |||
| Data Preparation – Binarizing Data | 00:06:00 | ||
| Feature Selection - Introduction | |||
| Feature Selection – Introduction | 00:07:00 | ||
| Feature Selection - Uni-variate Part 1 - Chi-Squared Test | |||
| Feature Selection – Uni-variate Part 1 – Chi-Squared Test | 00:09:00 | ||
| Feature Selection - Uni-variate Part 2 - Chi-Squared Test | |||
| Feature Selection – Uni-variate Part 2 – Chi-Squared Test | 00:10:00 | ||
| Feature Selection - Recursive Feature Elimination | |||
| Feature Selection – Recursive Feature Elimination | 00:11:00 | ||
| Feature Selection - Principal Component Analysis (PCA) | |||
| Feature Selection – Principal Component Analysis (PCA) | 00:09:00 | ||
| Feature Selection - Feature Importance | |||
| Feature Selection – Feature Importance | 00:06:00 | ||
| Refresher Session - The Mechanism of Re-sampling, Training and Testing | |||
| Refresher Session – The Mechanism of Re-sampling, Training and Testing | 00:12:00 | ||
| Algorithm Evaluation Techniques - Introduction | |||
| Algorithm Evaluation Techniques – Introduction | 00:07:00 | ||
| Algorithm Evaluation Techniques - Train and Test Set | |||
| Algorithm Evaluation Techniques – Train and Test Set | 00:11:00 | ||
| Algorithm Evaluation Techniques - K-Fold Cross Validation | |||
| Algorithm Evaluation Techniques – K-Fold Cross Validation | 00:09:00 | ||
| Algorithm Evaluation Techniques - Leave One Out Cross Validation | |||
| Algorithm Evaluation Techniques – Leave One Out Cross Validation | 00:05:00 | ||
| Algorithm Evaluation Techniques - Repeated Random Test-Train Splits | |||
| Algorithm Evaluation Techniques – Repeated Random Test-Train Splits | 00:07:00 | ||
| Algorithm Evaluation Metrics - Introduction | |||
| Algorithm Evaluation Metrics – Introduction | 00:09:00 | ||
| Algorithm Evaluation Metrics - Classification Accuracy | |||
| Algorithm Evaluation Metrics – Classification Accuracy | 00:08:00 | ||
| Algorithm Evaluation Metrics - Log Loss | |||
| Algorithm Evaluation Metrics – Log Loss | 00:03:00 | ||
| Algorithm Evaluation Metrics - Area Under ROC Curve | |||
| Algorithm Evaluation Metrics – Area Under ROC Curve | 00:06:00 | ||
| Algorithm Evaluation Metrics - Confusion Matrix | |||
| Algorithm Evaluation Metrics – Confusion Matrix | 00:10:00 | ||
| Algorithm Evaluation Metrics - Classification Report | |||
| Algorithm Evaluation Metrics – Classification Report | 00:04:00 | ||
| Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction | |||
| Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction | 00:06:00 | ||
| Algorithm Evaluation Metrics - Mean Absolute Error | |||
| Algorithm Evaluation Metrics – Mean Absolute Error | 00:07:00 | ||
| Algorithm Evaluation Metrics - Mean Square Error | |||
| Algorithm Evaluation Metrics – Mean Square Error | 00:03:00 | ||
| Algorithm Evaluation Metrics - R Squared | |||
| Algorithm Evaluation Metrics – R Squared | 00:04:00 | ||
| Classification Algorithm Spot Check - Logistic Regression | |||
| Classification Algorithm Spot Check – Logistic Regression | 00:12:00 | ||
| Classification Algorithm Spot Check - Linear Discriminant Analysis | |||
| Classification Algorithm Spot Check – Linear Discriminant Analysis | 00:04:00 | ||
| Classification Algorithm Spot Check - K-Nearest Neighbors | |||
| Classification Algorithm Spot Check – K-Nearest Neighbors | 00:05:00 | ||
| Classification Algorithm Spot Check - Naive Bayes | |||
| Classification Algorithm Spot Check – Naive Bayes | 00:04:00 | ||
| Classification Algorithm Spot Check - CART | |||
| Classification Algorithm Spot Check – CART | 00:04:00 | ||
| Classification Algorithm Spot Check - Support Vector Machines | |||
| Classification Algorithm Spot Check – Support Vector Machines | 00:05:00 | ||
| Regression Algorithm Spot Check - Linear Regression | |||
| Regression Algorithm Spot Check – Linear Regression | 00:08:00 | ||
| Regression Algorithm Spot Check - Ridge Regression | |||
| Regression Algorithm Spot Check – Ridge Regression | 00:03:00 | ||
| Regression Algorithm Spot Check - Lasso Linear Regression | |||
| Regression Algorithm Spot Check – Lasso Linear Regression | 00:03:00 | ||
| Regression Algorithm Spot Check - Elastic Net Regression | |||
| Regression Algorithm Spot Check – Elastic Net Regression | 00:02:00 | ||
| Regression Algorithm Spot Check - K-Nearest Neighbors | |||
| Regression Algorithm Spot Check – K-Nearest Neighbors | 00:06:00 | ||
| Regression Algorithm Spot Check - CART | |||
| Regression Algorithm Spot Check – CART | 00:04:00 | ||
| Regression Algorithm Spot Check - Support Vector Machines (SVM) | |||
| Regression Algorithm Spot Check – Support Vector Machines (SVM) | 00:04:00 | ||
| Compare Algorithms - Part 1 : Choosing the best Machine Learning Model | |||
| Compare Algorithms – Part 1 : Choosing the best Machine Learning Model | 00:09:00 | ||
| Compare Algorithms - Part 2 : Choosing the best Machine Learning Model | |||
| Compare Algorithms – Part 2 : Choosing the best Machine Learning Model | 00:05:00 | ||
| Pipelines : Data Preparation and Data Modelling | |||
| Pipelines : Data Preparation and Data Modelling | 00:11:00 | ||
| Pipelines : Feature Selection and Data Modelling | |||
| Pipelines : Feature Selection and Data Modelling | 00:10:00 | ||
| Performance Improvement: Ensembles - Voting | |||
| Performance Improvement: Ensembles – Voting | 00:07:00 | ||
| Performance Improvement: Ensembles - Bagging | |||
| Performance Improvement: Ensembles – Bagging | 00:08:00 | ||
| Performance Improvement: Ensembles - Boosting | |||
| Performance Improvement: Ensembles – Boosting | 00:05:00 | ||
| Performance Improvement: Parameter Tuning using Grid Search | |||
| Performance Improvement: Parameter Tuning using Grid Search | 00:08:00 | ||
| Performance Improvement: Parameter Tuning using Random Search | |||
| Performance Improvement: Parameter Tuning using Random Search | 00:06:00 | ||
| Export, Save and Load Machine Learning Models : Pickle | |||
| Export, Save and Load Machine Learning Models : Pickle | 00:10:00 | ||
| Export, Save and Load Machine Learning Models : Joblib | |||
| Export, Save and Load Machine Learning Models : Joblib | 00:06:00 | ||
| Finalizing a Model - Introduction and Steps | |||
| Finalizing a Model – Introduction and Steps | 00:07:00 | ||
| Finalizing a Classification Model - The Pima Indian Diabetes Dataset | |||
| Finalizing a Classification Model – The Pima Indian Diabetes Dataset | 00:07:00 | ||
| Quick Session: Imbalanced Data Set - Issue Overview and Steps | |||
| Quick Session: Imbalanced Data Set – Issue Overview and Steps | 00:09:00 | ||
| Iris Dataset : Finalizing Multi-Class Dataset | |||
| Iris Dataset : Finalizing Multi-Class Dataset | 00:09:00 | ||
| Finalizing a Regression Model - The Boston Housing Price Dataset | |||
| Finalizing a Regression Model – The Boston Housing Price Dataset | 00:08:00 | ||
| Real-time Predictions: Using the Pima Indian Diabetes Classification Model | |||
| Real-time Predictions: Using the Pima Indian Diabetes Classification Model | 00:07:00 | ||
| Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset | |||
| Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset | 00:03:00 | ||
| Real-time Predictions: Using the Boston Housing Regression Model | |||
| Real-time Predictions: Using the Boston Housing Regression Model | 00:08:00 | ||
| Resources | |||
| Resources – Data Science & Machine Learning with Python | 00:00:00 | ||