Unlock the power of data with our course, “Data Science and Machine Learning with Python.” This course is designed to provide a solid foundation in data analysis and machine learning techniques using Python, one of the most popular programming languages. You will learn to work with libraries like Pandas, NumPy, and Matplotlib for data manipulation and visualization. We’ll also guide you through machine learning concepts, including supervised and unsupervised learning, using libraries like Scikit-learn. By the end, you’ll be able to build, evaluate, and optimize machine learning models, preparing you for real-world data science challenges.
Who is this course for?
By taking this Data Science and Machine Learning with Python Course, you’re able to open yourself to new opportunities within the data science and AI career path. Such careers may include:
- Data Scientist (£35,000 to £65,000)
- Machine Learning Engineer (£45,000 to £80,000)
- AI Researcher (£50,000 to £100,000)
Course Curriculum
Data Science and Machine Learning with Python | |||
Course Overview & Table of Contents | 00:09:00 | ||
Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types | 00:05:00 | ||
Introduction to Machine Learning – Part 2 – Classifications and Applications | 00:06:00 | ||
System and Environment preparation – Part 1 | 00:08:00 | ||
System and Environment preparation – Part 2 | 00:06:00 | ||
Learn Basics of python – Assignment 1 | 00:10:00 | ||
Learn Basics of python – Assignment 2 | 00:09:00 | ||
Learn Basics of python – Functions | 00:04:00 | ||
Learn Basics of python – Data Structures | 00:12:00 | ||
Learn Basics of NumPy – NumPy Array | 00:06:00 | ||
Learn Basics of NumPy – NumPy Data | 00:08:00 | ||
Learn Basics of NumPy – NumPy Arithmetic | 00:04:00 | ||
Learn Basics of Matplotlib | 00:07:00 | ||
Learn Basics of Pandas – Part 1 | 00:06:00 | ||
Learn Basics of Pandas – Part 2 | 00:07:00 | ||
Understanding the CSV data file | 00:09:00 | ||
Load and Read CSV data file using Python Standard Library | 00:09:00 | ||
Load and Read CSV data file using NumPy | 00:04:00 | ||
Load and Read CSV data file using Pandas | 00:05:00 | ||
Dataset Summary – Peek, Dimensions and Data Types | 00:09:00 | ||
Dataset Summary – Class Distribution and Data Summary | 00:09:00 | ||
Dataset Summary – Explaining Correlation | 00:11:00 | ||
Dataset Summary – Explaining Skewness – Gaussian and Normal Curve | 00:07:00 | ||
Dataset Visualization – Using Histograms | 00:07:00 | ||
Dataset Visualization – Using Density Plots | 00:06:00 | ||
Dataset Visualization – Box and Whisker Plots | 00:05:00 | ||
Multivariate Dataset Visualization – Correlation Plots | 00:08:00 | ||
Multivariate Dataset Visualization – Scatter Plots | 00:05:00 | ||
Data Preparation (Pre-Processing) – Introduction | 00:09:00 | ||
Data Preparation – Re-scaling Data – Part 1 | 00:09:00 | ||
Data Preparation – Re-scaling Data – Part 2 | 00:09:00 | ||
Data Preparation – Standardizing Data – Part 1 | 00:07:00 | ||
Data Preparation – Standardizing Data – Part 2 | 00:04:00 | ||
Data Preparation – Normalizing Data | 00:08:00 | ||
Data Preparation – Binarizing Data | 00:06:00 | ||
Feature Selection – Introduction | 00:07:00 | ||
Feature Selection – Uni-variate Part 1 – Chi-Squared Test | 00:09:00 | ||
Feature Selection – Uni-variate Part 2 – Chi-Squared Test | 00:10:00 | ||
Feature Selection – Recursive Feature Elimination | 00:11:00 | ||
Feature Selection – Principal Component Analysis (PCA) | 00:09:00 | ||
Feature Selection – Feature Importance | 00:07:00 | ||
Refresher Session – The Mechanism of Re-sampling, Training and Testing | 00:12:00 | ||
Algorithm Evaluation Techniques – Introduction | 00:07:00 | ||
Algorithm Evaluation Techniques – Train and Test Set | 00:11:00 | ||
Algorithm Evaluation Techniques – K-Fold Cross Validation | 00:09:00 | ||
Algorithm Evaluation Techniques – Leave One Out Cross Validation | 00:05:00 | ||
Algorithm Evaluation Techniques – Repeated Random Test-Train Splits | 00:07:00 | ||
Algorithm Evaluation Metrics – Introduction | 00:09:00 | ||
Algorithm Evaluation Metrics – Classification Report | 00:04:00 | ||
Algorithm Evaluation Metrics – Log Loss | 00:03:00 | ||
Algorithm Evaluation Metrics – Area Under ROC Curve | 00:06:00 | ||
Algorithm Evaluation Metrics – Confusion Matrix | 00:10:00 | ||
Algorithm Evaluation Metrics – Classification Report | 00:04:00 | ||
Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction | 00:06:00 | ||
Algorithm Evaluation Metrics – Mean Absolute Error | 00:07:00 | ||
Algorithm Evaluation Metrics – Mean Square Error | 00:03:00 | ||
Algorithm Evaluation Metrics – R Squared | 00:04:00 | ||
Classification Algorithm Spot Check – Logistic Regression | 00:12:00 | ||
Classification Algorithm Spot Check – Linear Discriminant Analysis | 00:04:00 | ||
Classification Algorithm Spot Check – K-Nearest Neighbors | 00:05:00 | ||
Classification Algorithm Spot Check – Naive Bayes | 00:04:00 | ||
Classification Algorithm Spot Check – CART | 00:04:00 | ||
Classification Algorithm Spot Check – Support Vector Machines | 00:05:00 | ||
Regression Algorithm Spot Check – Linear Regression | 00:08:00 | ||
Regression Algorithm Spot Check – Ridge Regression | 00:03:00 | ||
Regression Algorithm Spot Check – Lasso Linear Regression | 00:03:00 | ||
Regression Algorithm Spot Check – Elastic Net Regression | 00:02:00 | ||
Regression Algorithm Spot Check – K-Nearest Neighbors | 00:06:00 | ||
Regression Algorithm Spot Check – CART | 00:04:00 | ||
Regression Algorithm Spot Check – Support Vector Machines (SVM) | 00:04:00 | ||
Compare Algorithms – Part 1 : Choosing the best Machine Learning Model | 00:09:00 | ||
Compare Algorithms – Part 2 : Choosing the best Machine Learning Model | 00:05:00 | ||
Pipelines : Data Preparation and Data Modelling | 00:11:00 | ||
Pipelines : Feature Selection and Data Modelling | 00:10:00 | ||
Performance Improvement: Ensembles – Voting | 00:07:00 | ||
Performance Improvement: Ensembles – Bagging | 00:08:00 | ||
Performance Improvement: Ensembles – Boosting | 00:05:00 | ||
Performance Improvement: Parameter Tuning using Grid Search | 00:08:00 | ||
Performance Improvement: Parameter Tuning using Random Search | 00:06:00 | ||
Export, Save and Load Machine Learning Models : Pickle | 00:10:00 | ||
Export, Save and Load Machine Learning Models : Joblib | 00:06:00 | ||
Finalizing a Model – Introduction and Steps | 00:07:00 | ||
Finalizing a Classification Model – The Pima Indian Diabetes Dataset | 00:07:00 | ||
Quick Session: Imbalanced Data Set – Issue Overview and Steps | 00:09:00 | ||
Iris Dataset : Finalizing Multi-Class Dataset | 00:09:00 | ||
Finalizing a Regression Model – The Boston Housing Price Dataset | 00:08:00 | ||
Real-time Predictions: Using the Pima Indian Diabetes Classification Model | 00:07:00 | ||
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset | 00:03:00 | ||
Real-time Predictions: Using the Boston Housing Regression Model | 00:08:00 |