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Course Highlights
Python has steadily ascended the ranks in the tech world, becoming the go-to language for Data Science and Machine Learning pursuits. Its versatility and ease of use make it an optimal choice for those aspiring to delve into the intricate realms of data analysis and algorithm development. By integrating Python into the curriculum, this course ensures that learners are not only familiar with the language but also proficient in its application to Data Science and Machine Learning tasks.
Data Science, on the other hand, stands as the backbone of today’s analytical advancements. With the ever-growing quantum of data, there’s a dire need for professionals who can sift through this data deluge and extract meaningful insights. This course simplifies complex topics, making it easier for learners to grasp the intricate mechanics of data manipulation, processing, and analysis. By emphasizing Python’s role in Data Science, the course underscores its significance in modern-day data operations.
Lastly, Machine Learning represents the pinnacle of automated data analysis. With the power of Python and the methodologies of Data Science, Machine Learning enables systems to learn from data and make decisions. This course elucidates these concepts, teaching learners to design, implement, and test Machine Learning algorithms. The synergy of Python, Data Science, and Machine Learning in this curriculum ensures that, by the end, learners are well-equipped to tackle any data challenge that comes their way.
Note: Skill-up is a Janets-approved resale partner for Quality Licence Scheme Endorsed courses.
Learning outcome
- Acquire in-depth knowledge of Data Science techniques using Python.
- Understand and implement Machine Learning algorithms.
- Analyze and visualize data with Python libraries like Pandas and Matplotlib.
- Evaluate and compare Machine Learning algorithms for optimum results.
- Develop real-time prediction models using trained datasets.
Certificate of Achievement
Quality Licence Scheme Endorsed Certificate
Upon completing the final assessment, you can apply for the Quality Licence Scheme Endorsed Certificate of Achievement. Endorsed certificates can be ordered and delivered to your home by post for only £129.
Order Your QLS Certificate
An extra £10 postage charge will be required for students leaving overseas.
Skill Up Recognised Certificate
Upon successful completion of the Data Science & Machine Learning with Python course, you have the opportunity to request a Skill Up Recognised Certificate. This certificate holds significant value, and its validation will endure throughout your lifetime.
- 1. PDF Certificate + PDF Transcript: £14.99
- 2. Hardcopy Certificate + Hardcopy Transcript: £19.99
- 3. Delivery Charge: £10.00 (Applicable for International Students)
CPD Quality Standards Accredited Certificate
After successfully completing the Data Science & Machine Learning with Python course, you can apply for the CPD Quality Standards Accredited Certificate of Achievement.
1. PDF Certificate: £25.00
2. Hardcopy Certificate: £35.00
3. Delivery Charge: £10.00 (Applicable for International Students)
Course media
Why should I take this course?
- Stay ahead in the rapidly growing field of Data Science and Machine Learning.
- Enhance career opportunities in the tech and data sectors.
- Learn to use Python, a leading programming language in Data Science.
- Address real-world business challenges using data analysis.
- Achieve proficiency in handling and visualizing large datasets.
Career Path
- Data Scientist
- Machine Learning Engineer
- Python Data Analyst
- Predictive Modeler
- Data Visualization Expert
Requirements
- Basic understanding of programming concepts.
- Eagerness to learn Data Science and Machine Learning.
- A computer with internet access.
Course Curriculum
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Course Overview & Table of Contents
00:09:00
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Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types
00:05:00
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Introduction to Machine Learning – Part 2 – Classifications and Applications
00:06:00
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System and Environment preparation – Part 1
00:08:00
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System and Environment preparation – Part 2
00:06:00
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Learn Basics of python – Assignment 2
00:09:00
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Learn Basics of python – Functions
00:04:00
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Learn Basics of python – Data Structures
00:12:00
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Learn Basics of NumPy – NumPy Array
00:06:00
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Learn Basics of NumPy – NumPy Data
00:08:00
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Learn Basics of NumPy – NumPy Arithmetic
00:04:00
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Learn Basics of Matplotlib
00:07:00
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Learn Basics of Pandas – Part 1
00:06:00
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Learn Basics of Pandas – Part 2
00:07:00
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Understanding the CSV data file
00:09:00
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Load and Read CSV data file using Python Standard Library
00:09:00
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Load and Read CSV data file using NumPy
00:04:00
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Load and Read CSV data file using Pandas
00:05:00
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Dataset Summary – Peek, Dimensions and Data Types
00:09:00
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Dataset Summary – Class Distribution and Data Summary
00:09:00
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Dataset Summary – Explaining Correlation
00:11:00
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Dataset Summary – Explaining Skewness – Gaussian and Normal Curve
00:07:00
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Dataset Visualization – Using Histograms
00:07:00
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Dataset Visualization – Using Density Plots
00:06:00
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Dataset Visualization – Box and Whisker Plots
00:05:00
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Multivariate Dataset Visualization – Correlation Plots
00:08:00
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Multivariate Dataset Visualization – Scatter Plots
00:05:00
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Data Preparation (Pre-Processing) – Introduction
00:09:00
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Data Preparation – Re-scaling Data – Part 1
00:09:00
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Data Preparation – Re-scaling Data – Part 2
00:09:00
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Data Preparation – Standardizing Data – Part 1
00:07:00
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Data Preparation – Standardizing Data – Part 2
00:04:00
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Data Preparation – Normalizing Data
00:08:00
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Data Preparation – Binarizing Data
00:06:00
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Feature Selection – Introduction
00:07:00
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Feature Selection – Uni-variate Part 1 – Chi-Squared Test
00:09:00
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Feature Selection – Uni-variate Part 2 – Chi-Squared Test
00:10:00
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Feature Selection – Recursive Feature Elimination
00:11:00
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Feature Selection – Principal Component Analysis (PCA)
00:09:00
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Feature Selection – Feature Importance
00:07:00
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Refresher Session – The Mechanism of Re-sampling, Training and Testing
00:12:00
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Algorithm Evaluation Techniques – Introduction
00:07:00
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Algorithm Evaluation Techniques – Train and Test Set
00:11:00
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Algorithm Evaluation Techniques – K-Fold Cross Validation
00:09:00
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Algorithm Evaluation Techniques – Leave One Out Cross Validation
00:05:00
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Algorithm Evaluation Techniques – Repeated Random Test-Train Splits
00:07:00
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Algorithm Evaluation Metrics – Introduction
00:09:00
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Algorithm Evaluation Metrics – Classification Accuracy
00:08:00
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Algorithm Evaluation Metrics – Log Loss
00:03:00
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Algorithm Evaluation Metrics – Area Under ROC Curve
00:06:00
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Algorithm Evaluation Metrics – Classification Report
00:04:00
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Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction
00:06:00
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Algorithm Evaluation Metrics – Mean Absolute Error
00:07:00
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Algorithm Evaluation Metrics – Mean Square Error
00:03:00
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Algorithm Evaluation Metrics – R Squared
00:04:00
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Classification Algorithm Spot Check – Logistic Regression
00:12:00
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Classification Algorithm Spot Check – Linear Discriminant Analysis
00:04:00
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Classification Algorithm Spot Check – K-Nearest Neighbors
00:05:00
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Classification Algorithm Spot Check – Naive Bayes
00:04:00
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Classification Algorithm Spot Check – CART
00:04:00
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Classification Algorithm Spot Check – Support Vector Machines
00:05:00
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Regression Algorithm Spot Check – Linear Regression
00:08:00
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Regression Algorithm Spot Check – Ridge Regression
00:03:00
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Regression Algorithm Spot Check – Lasso Linear Regression
00:03:00
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Regression Algorithm Spot Check – Elastic Net Regression
00:02:00
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Regression Algorithm Spot Check – K-Nearest Neighbors
00:06:00
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Regression Algorithm Spot Check – CART
00:04:00
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Regression Algorithm Spot Check – Support Vector Machines (SVM)
00:04:00
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Compare Algorithms – Part 1 : Choosing the best Machine Learning Model
00:09:00
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Compare Algorithms – Part 2 : Choosing the best Machine Learning Model
00:05:00
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Pipelines : Data Preparation and Data Modelling
00:11:00
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Pipelines : Feature Selection and Data Modelling
00:10:00
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Performance Improvement: Ensembles – Voting
00:07:00
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Performance Improvement: Ensembles – Bagging
00:08:00
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Performance Improvement: Ensembles – Boosting
00:05:00
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Performance Improvement: Parameter Tuning using Grid Search
00:08:00
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Performance Improvement: Parameter Tuning using Random Search
00:06:00
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Export, Save and Load Machine Learning Models : Pickle
00:10:00
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Export, Save and Load Machine Learning Models : Joblib
00:06:00
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Finalizing a Model – Introduction and Steps
00:07:00
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Finalizing a Classification Model – The Pima Indian Diabetes Dataset
00:07:00
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Quick Session: Imbalanced Data Set – Issue Overview and Steps
00:09:00
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Iris Dataset : Finalizing Multi-Class Dataset
00:09:00
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Finalizing a Regression Model – The Boston Housing Price Dataset
00:08:00
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Real-time Predictions: Using the Pima Indian Diabetes Classification Model
00:07:00
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Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
00:03:00
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Real-time Predictions: Using the Boston Housing Regression Model
00:08:00
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Resources – Data Science & Machine Learning with Python
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Submit Your Assignment & Order QLS Certificate
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Skill Up Recognised Certificate
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Order CPDQS Certificate
Offer Ends in
All Courses for £49-
Duration:10 hours, 24 minutes
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Access:1 Year
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Units:93
