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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
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