Python for Data Science and Machine Learning: Beginner to Pro

Python is one of the most powerful and widely used programming languages in data science and machine learning today. This course provides a solid foundation in Python coding while introducing key concepts in AI, machine learning, and data analysis.
You’ll work through real-world projects, learn to handle data, build predictive models, and develop the coding skills needed to solve complex problems. No prior experience is necessary — we start from the basics and progress to practical applications.
By the end of the course, you will be ready to tackle data science projects confidently and explore advanced machine learning topics with a strong coding background.

Key Features of The Course

• A CPD certificate that is recognised worldwide.

• A great online learning experience.

• Interesting and unique online materials and activities.

• Expert guidance and support from the field leaders.

• Access to the study resources anytime you want.

• Friendly and helpful customer service and admin support by email, phone, and chat from Monday through Friday.

• Get a year-long access to the course.
  

Requirements

• No prerequisites; suitable for individuals from any academic background.

• Accessible course materials from any internet-enabled device.
  

CPD Certificate from Course Gate

You can obtain your CPD certificate from us upon completing the course. You can order the PDF certificate for £9 and the hard copy for £15. Also, you can order both PDF and hardcopy certificates for £22.

Course Curriculum

• 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
• Resources

Disclaimer:

This is an online course with pre-recorded lessons.. Your account details will be sent to your email within 24 to 48 hours (but don’t worry, we’re usually much quicker).