Python ML & Data Science Essentials

Course details

• Understand the fundamental concepts and types of machine learning, data science, and Python programming.
• Learn to prepare the system and environment for data analysis and machine learning tasks.
• Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization.
• Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing.
• Explore feature selection methods and evaluation metrics for classification and regression algorithms.
• Compare and select the best machine learning model using pipelines and ensembles.
• Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions.

• Course Overview & Table of Contents
• Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
• Introduction to Machine Learning - Part 2 - Classifications and Applications
• System and Environment preparation - Part 1
• System and Environment preparation - Part 2
• Learn Basics of python - Assignment
• Learn Basics of python - Assignment
• Learn Basics of python - Functions
• Learn Basics of python - Data Structures
• Learn Basics of NumPy - NumPy Array
• Learn Basics of NumPy - NumPy Data
• Learn Basics of NumPy - NumPy Arithmetic
• Learn Basics of Matplotlib
• Learn Basics of Pandas - Part 1
• Learn Basics of Pandas - Part 2
• Understanding the CSV data file
• Load and Read CSV data file using Python Standard Library
• Load and Read CSV data file using NumPy
• Load and Read CSV data file using Pandas
• Dataset Summary - Peek, Dimensions and Data Types
• Dataset Summary - Class Distribution and Data Summary
• Dataset Summary - Explaining Correlation
• Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
• Dataset Visualization - Using Histograms
• Dataset Visualization - Using Density Plots
• Dataset Visualization - Box and Whisker Plots
• Multivariate Dataset Visualization - Correlation Plots
• Multivariate Dataset Visualization - Scatter Plots
• Data Preparation (Pre-Processing) - Introduction
• Data Preparation - Re-scaling Data - Part 1
• Data Preparation - Re-scaling Data - Part 2
• Data Preparation - Standardizing Data - Part 1
• Data Preparation - Standardizing Data - Part 2
• Data Preparation - Normalizing Data
• Data Preparation - Binarizing Data
• Feature Selection - Introduction
• Feature Selection - Uni-variate Part 1 - Chi-Squared Test
• Feature Selection - Uni-variate Part 2 - Chi-Squared Test
• Feature Selection - Recursive Feature Elimination
• Feature Selection - Principal Component Analysis (PCA)
• Feature Selection - Feature Importance
• Refresher Session - The Mechanism of Re-sampling, Training and Testing
• Algorithm Evaluation Techniques - Introduction
• Algorithm Evaluation Techniques - Train and Test Set
• Algorithm Evaluation Techniques - K-Fold Cross Validation
• Algorithm Evaluation Techniques - Leave One Out Cross Validation
• Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
• Algorithm Evaluation Metrics - Introduction
• Algorithm Evaluation Metrics - Classification Accuracy
• Algorithm Evaluation Metrics - Log Loss
• Algorithm Evaluation Metrics - Area Under ROC Curve
• Algorithm Evaluation Metrics - Confusion Matrix
• Algorithm Evaluation Metrics - Classification Report
• Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
• Algorithm Evaluation Metrics - Mean Absolute Error
• Algorithm Evaluation Metrics - Mean Square Error
• Algorithm Evaluation Metrics - R Squared
• Classification Algorithm Spot Check - Logistic Regression
• Classification Algorithm Spot Check - Linear Discriminant Analysis
• Classification Algorithm Spot Check - K-Nearest Neighbors
• Classification Algorithm Spot Check - Naive Bayes
• Classification Algorithm Spot Check - CART
• Classification Algorithm Spot Check - Support Vector Machines
• Regression Algorithm Spot Check - Linear Regression
• Regression Algorithm Spot Check - Ridge Regression
• Regression Algorithm Spot Check - Lasso Linear Regression
• Regression Algorithm Spot Check - Elastic Net Regression
• Regression Algorithm Spot Check - K-Nearest Neighbors
• Regression Algorithm Spot Check - CART
• Regression Algorithm Spot Check - Support Vector Machines (SVM)
• Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
• Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
• Pipelines : Data Preparation and Data Modelling
• Pipelines : Feature Selection and Data Modelling
• Performance Improvement: Ensembles - Voting
• Performance Improvement: Ensembles - Bagging
• Performance Improvement: Ensembles - Boosting
• Performance Improvement: Parameter Tuning using Grid Search
• Performance Improvement: Parameter Tuning using Random Search
• Export, Save and Load Machine Learning Models : Pickle
• Export, Save and Load Machine Learning Models : Joblib
• Finalizing a Model - Introduction and Steps
• Finalizing a Classification Model - The Pima Indian Diabetes Dataset
• Quick Session: Imbalanced Data Set - Issue Overview and Steps
• Iris Dataset : Finalizing Multi-Class Dataset
• Finalizing a Regression Model - The Boston Housing Price Dataset
• Real-time Predictions: Using the Pima Indian Diabetes Classification Model
• Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
• Real-time Predictions: Using the Boston Housing Regression Model

• Unlimited access to the course for forever
• Digital Certificate, Transcript, student ID all included in the price
• Absolutely no hidden fees
• Directly receive CPD accredited qualifications after course completion
• Receive one to one assistance on every weekday from professionals
• Immediately receive the PDF certificate after passing
• Receive the original copies of your certificate and transcript on the next working day
• Easily learn the skills and knowledge from the comfort of your home