Overview
Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career.
This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more.
After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today!
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.
Who is This course for?
This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science.
Requirements
The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush.
Career Path
This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as
- Data Analyst
- Data Scientist
- Data Manager
- Business Analyst
And much more!
Course Curriculum
- 88 sections
- 90 lectures
- 10 hours, 19 minutes total length
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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 00:04:00
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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 00:06:00
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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 – 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 – Confusion Matrix 00:10:00
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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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This course includes:
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Duration:10 hours, 19 minutes
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Access:1 year access
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Units:90
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Level:
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CPD Points:10
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Certificate:Yes
