Information & Digital Technologies with Python Data Science

Struggling to find the right course? We got you covered with our Information & Digital Technologies with Python Data Science bundle!

Enrol in our most sought-after Information & Digital Technologies with Python Data Science bundle which will set you apart from the rest .

If you're an enthusiastic learner looking to enhance your understanding in Information & Digital Technologies with Python Data Science then don't rush; instead, develop the relevant knowledge and skills with our bundle to make yourself stand out as a strong candidate in the job market.

Courses included in this Information & Digital Technologies with Python Data Science bundle are:

• Course 01: Data Science & Machine Learning With Python
• Course 02: Python 3 For Beginners
• Course 03: Spatial Data Visualization And Machine Learning In Python

Standout features of studying Information & Digital Technologies with Python Data Science with Janets:

• The ability to complete the Information & Digital Technologies with Python Data Science at your own convenient time
• Online free tests and assessments to evaluate the progress
• Facility to study Information & Digital Technologies with Python Data Science from anywhere in the world by enroling
• Get all the required materials and documentation after getting enrolled in Life Coaching
• Get a free E-certificate, Transcript, and Student ID with Life Coaching
• Expert-designed Information & Digital Technologies with Python Data Science with video lectures and 24/7 tutor support

This career-focused Information & Digital Technologies with Python Data Science bundle is formulated to make you a good fit for the employment market. Not only that, the proficiency that can be obtained after completing this Information & Digital Technologies with Python Data Science training will add value to your resume and catch the attention of the employers.So what are you waiting for enrol Information & Digital Technologies with Python Data Science bundle now and launch your career with a bang.

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

The Information & Digital Technologies with Python Data Science course is ideal for those who are interested or already working in this sector.

No prior qualifications are needed for Learners to enrol on this Information & Digital Technologies with Python Data Science

After completing this Information & Digital Technologies with Python Data Science course, you will have the knowledge and skills to explore trendy and in-demand Information & Digital Technologies with Python Data Science jobs.

Currently there are no Q&As for this course. Be the first to ask a question.

Certificate of completion

Digital certificate - Included