About This Course

In this course, we will focus mainly on Machine Learning. Throughout this course, we will prepare our machine to make it ready for a prediction test, just like how you prepare for your Mathematics Test in school or college. We learn and train ourselves by solving the most possible number of similar mathematical problems. Let’s call these sample data of similar problems and their solutions as training input and training output, respectively. And then, the day comes when we have the actual test. 

We will be given a new set of problems to solve, but very similar to the problems we learned, and based on the previous practice and learning experiences, we have to solve them. We can call those problems as testing input and our answers as predicted output. Later, our professor will evaluate these answers and compare it with its actual answers, which we can call as test output. Then, a mark will be given on the basis of the correct answers. We call this mark as our accuracy. The life of a machine learning engineer and a data-scientist is dedicated to make this accuracy as good as possible through different techniques and evaluation measures.

This course will cover:

  • Introduction to Machine Learning
  • Basics of Python
  • NumPy
  • Pandas
  • Matplotlib
  • Data visualization and preparation
  • Classification and Regression models
  • And much more

Machine Learning and Data Science are the most lucrative jobs in the technology arena nowadays. Learning this course will make you equipped to compete in this area.

Our Promise to You

By the end of this course, you will have learned how Machine Learning works.

30 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about Machine Learning.

Course Curriculum

Section 1 - Introduction
Course Overview And Table Of Contents 00:00:00
Introduction To Machine Learning Part 1 – Concepts , Definitions And Types 00:00:00
Introduction To Machine Learning Part 2 – Classifications And Applications 00:00:00
System And Environment Preparation Part 1 00:00:00
Important: TensorFlow Incompatibility 00:00:00
System And Environment Preparation Part 2 00:00:00
Course Resources 00:00:00
Section 2 - Basics Of Python
Assignment 00:00:00
Flow Control 00:00:00
Functions 00:00:00
Data Structures 00:00:00
Section 3 - Basics Of NumPy And Matplotlib
NumPy Array 00:00:00
NumPy Data 00:00:00
NumPy Arithmetic 00:00:00
Matplotlib 00:00:00
Section 4 - Basics Of Pandas
Basics Of Pandas Part 1 00:00:00
Basics Of Pandas Part 2 00:00:00
Section 5 - CSV Data File
Understanding The CSV Data File 00:00:00
Load And Read CSV Data File Using Python Standard Library 00:00:00
Load And Read CSV Data File Using NumPy 00:00:00
Load And Read CSV Data File Using Pandas 00:00:00
Section 6 - Dataset Summary
Peek, Dimensions And Data Types 00:00:00
Class Distribution And Data Summary 00:00:00
Explaining Correlation 00:00:00
Explaining Skewness – Gaussian And Normal Curve 00:00:00
Section 7 - Dataset Visualization
Using Histograms 00:00:00
Using Density Plots 00:00:00
Box And Whisker Plots 00:00:00
Multivariate Dataset Visualization – Correlation Plots 00:00:00
Multivariate Dataset Visualization – Scatter Plots 00:00:00
Section 8 - Data Preparation
Data Preparation (Pre-Processing) – Introduction 00:00:00
Re-Scaling Data Part 1 00:00:00
Re-Scaling Data Part 2 00:00:00
Standardizing Data Part 1 00:00:00
Standardizing Data Part 2 00:00:00
Normalizing Data 00:00:00
Binarizing Data 00:00:00
Section 9 - Feature Selection
Introduction 00:00:00
Uni-Variate Part 1 – Chi-Squared Test 00:00:00
Uni-Variate Part 2 – Chi-Squared Test 00:00:00
Recursive Feature Elimination 00:00:00
Principal Component Analysis (PCA) 00:00:00
Feature Importance 00:00:00
Section 10 - Algorithm Evaluation Techniques
Refresher Session – The Mechanism Of Re-Sampling, Training And Testing 00:00:00
Introduction 00:00:00
Train And Test Set 00:00:00
K-Fold Cross Validation 00:00:00
Leave One Out Cross Validation 00:00:00
Repeated Random Test-Train Splits 00:00:00
Section 11 - Algorithm Evaluation Metrics
Introduction 00:00:00
Classification Accuracy 00:00:00
Log Loss 00:00:00
Area Under ROC Curve 00:00:00
Confusion Matrix 00:00:00
Classification Report 00:00:00
Mean Absolute Error – Dataset Introduction 00:00:00
Mean Absolute Error 00:00:00
Mean Square Error 00:00:00
R Squared 00:00:00
Section 12 - Classification Algorithm Spot Check
Logistic Regression 00:00:00
Linear Discriminant Analysis 00:00:00
K-Nearest Neighbors 00:00:00
Naive Bayes 00:00:00
Cart 00:00:00
Support Vector Machines 00:00:00
Section 13 - Regression Algorithm Spot Check
Linear Regression 00:00:00
Ridge Regression 00:00:00
Lasso Linear Regression 00:00:00
Elastic Net Regression 00:00:00
K-Nearest Neighbors 00:00:00
Cart 00:00:00
Support Vector Machines 00:00:00
Section 14 - Compare Algorithms
Choosing The Best Machine Learning Model Part 1 00:00:00
Choosing The Best Machine Learning Model Part 2 00:00:00
Section 15 - Pipelines
Data Preparation And Data Modelling 00:00:00
Feature Selection And Data Modelling 00:00:00
Section 16 - Performance Improvement
Ensembles – Voting 00:00:00
Ensembles – Bagging 00:00:00
Ensembles – Boosting 00:00:00
Parameter Tuning Using Grid Search 00:00:00
Parameter Tuning Using Random Search 00:00:00
Section 17 - Export, Save And Load Machine Learning Models
Pickle 00:00:00
Joblib 00:00:00
Section 18 - Finalizing A Model
Finalizing A Model – Introduction And Steps 00:00:00
Finalizing A Classification Model – The Pima Indian Diabetes Dataset 00:00:00
Quick Session: Imbalanced Data Set – Issue Overview And Steps 00:00:00
Iris Dataset: Finalizing Multi-Class Dataset 00:00:00
Finalizing A Regression Model – The Boston Housing Price Dataset 00:00:00
Section 19 - Real-Time Predictions
Using The Pima Indian Diabetes Classification Model 00:00:00
Using Iris Flowers Multi-Class Classification Dataset 00:00:00
Using The Boston Housing Regression Model 00:00:00
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