Machine Learning with python

Teacher

iBytes Academy

Course Features

Lectures 101
Quizzes 0
Students 41
Assessments Yes

Getting Started 1
- Lecture1.1
  
  Getting Started 04 min
Python - 1 11
- Lecture2.1
  
  Python Introduction 05 min
- Lecture2.2
  
  Data types 12 min
- Lecture2.3
  
  User Inputs and string concatenation 05 min
- Lecture2.4
  
  Operators 06 min
- Lecture2.5
  
  Conditional Statements 05 min
- Lecture2.6
  
  Tuples 06 min
- Lecture2.7
  
  Loops 09 min
- Lecture2.8
  
  Functions 07 min
- Lecture2.9
  
  Problem 1: Reverse every word in a String 08 min
- Lecture2.10
  
  Problem 2: Prime numbers till N 10 min
- Lecture2.11
  
  Python Quiz – 1
Python - 2 5
- Lecture3.1
  
  Lists 04 min
- Lecture3.2
  
  Add/Remove elements from List 06 min
- Lecture3.3
  
  User Inputs in Lists and 2-D Lists 10 min
- Lecture3.4
  
  Dictionaries 07 min
- Lecture3.5
  
  Python Quiz – 2
Numpy 3
- Lecture4.1
  
  Introduction to Numpy 10 min
- Lecture4.2
  
  Operations and functions in Numpy 05 min
- Lecture4.3
  
  Numpy Quiz
MatplotLib 2
- Lecture5.1
  
  Introduction to MatplotLib 07 min
- Lecture5.2
  
  Common properties in MatplotLib 09 min
Pandas 6
- Lecture6.1
  
  Introduction to Pandas 03 min
- Lecture6.2
  
  Pandas – 2 12 min
- Lecture6.3
  
  Pandas – 3 08 min
- Lecture6.4
  
  Pandas – 4 03 min
- Lecture6.5
  
  Pandas – Code/Dataset
- Lecture6.6
  
  Pandas Quiz
Introduction to Machine Learning 2
- Lecture7.1
  
  Machine Learning Introduction – 1 11 min
- Lecture7.2
  
  Machine Learning introduction – 2 10 min
Linear Regression 13
- Lecture8.1
  
  Linear Regression Introduction 07 min
- Lecture8.2
  
  Linear Regression Intro – 2 12 min
- Lecture8.3
  
  Linear Regression Score 08 min
- Lecture8.4
  
  Gradient Descent Intro 11 min
- Lecture8.5
  
  Coding Linear Regression from scratch 05 min
- Lecture8.6
  
  Coding Linear Regression from scratch – 2 08 min
- Lecture8.7
  
  Using Linear Regression from sklearn 08 min
- Lecture8.8
  
  Data with multiple features 13 min
- Lecture8.9
  
  Polynomial Regression 09 min
- Lecture8.10
  
  Overfitting and Regularizations 05 min
- Lecture8.11
  
  Linear Regression – Code/Dataset
- Lecture8.12
  
  Gradient Descent from Scratch 12 min
- Lecture8.13
  
  Linear Regression Quiz
Logistic Regression 4
- Lecture9.1
  
  Logistic Regression Introduction 12 min
- Lecture9.2
  
  Logistic Regression on Cancer Dataset 12 min
- Lecture9.3
  
  Multi-class Classification 08 min
- Lecture9.4
  
  Logistic Regression Quiz
Classification Metrics 4
- Lecture10.1
  
  Classification Metrics Introduction 12 min
- Lecture10.2
  
  Classification Metrics for cancer dataset 08 min
- Lecture10.3
  
  Classification Metrics for Iris dataset 04 min
- Lecture10.4
  
  Classification Metrics Quiz
Feature Scaling 3
- Lecture11.1
  
  Feature Scaling Introduction 16 min
- Lecture11.2
  
  Feature Scaling for Wine dataset 10 min
- Lecture11.3
  
  Feature Scaling Quiz
Decision Trees 10
- Lecture12.1
  
  Decision Trees Introduction – 1 10 min
- Lecture12.2
  
  Decision Trees Introduction – 2 11 min
- Lecture12.3
  
  Accuracy and info gain 08 min
- Lecture12.4
  
  Gain Ratio and Gini Index 09 min
- Lecture12.5
  
  Overfitting in Decision Trees 07 min
- Lecture12.6
  
  Pruning 09 min
- Lecture12.7
  
  Decision Trees on Iris Dataset 09 min
- Lecture12.8
  
  Decision Trees on Heart Dataset 03 min
- Lecture12.9
  
  Decision Trees – Code/Dataset
- Lecture12.10
  
  Decision Trees Quiz
Random Forest 4
- Lecture13.1
  
  Random Forest Introduction 05 min
- Lecture13.2
  
  Data Bagging 05 min
- Lecture13.3
  
  Random Forest on Iris dataset 07 min
- Lecture13.4
  
  Random Forest Quiz
Naive Bayes 6
- Lecture14.1
  
  Bayes Theorem 07 min
- Lecture14.2
  
  Naive Bayes Introduction 12 min
- Lecture14.3
  
  Laplace Correction 11 min
- Lecture14.4
  
  Classification of different datasets with Naive Bayes 05 min
- Lecture14.5
  
  Naive Bayes -Code/Dataset
- Lecture14.6
  
  Naive Bayes Quiz
KNN 6
- Lecture15.1
  
  KNN Introduction – 1 06 min
- Lecture15.2
  
  KNN Introduction – 2 05 min
- Lecture15.3
  
  Pros and Cons of KNN 07 min
- Lecture15.4
  
  Choosing value of K 08 min
- Lecture15.5
  
  Classification of Cancer Dataset with KNN 11 min
- Lecture15.6
  
  KNN Quiz
SVM 7
- Lecture16.1
  
  SVM Introduction 15 min
- Lecture16.2
  
  Kernels 17 min
- Lecture16.3
  
  Effect of C on Decision boundary 08 min
- Lecture16.4
  
  Guassian Curve 15 min
- Lecture16.5
  
  Regression using SVR 03 min
- Lecture16.6
  
  SVM -Code/Dataset
- Lecture16.7
  
  SVM Quiz
PCA 7
- Lecture17.1
  
  PCA-Introduction 09 min
- Lecture17.2
  
  PCA on 2D dataset 05 min
- Lecture17.3
  
  PCA on Breast cancer dataset 15 min
- Lecture17.4
  
  PCA on images 16 min
- Lecture17.5
  
  Eighen Faces 09 min
- Lecture17.6
  
  PCA on LFW dataset 05 min
- Lecture17.7
  
  PCA Quiz
NLP 7
- Lecture18.1
  
  NLP Intro – 1 07 min
- Lecture18.2
  
  NLP Intro – 2 13 min
- Lecture18.3
  
  POS Tag and Lemmatization 07 min
- Lecture18.4
  
  Project – Twitter Sentiments – 1 13 min
- Lecture18.5
  
  Project – Twitter Sentiments – 2 12 min
- Lecture18.6
  
  Project – Twitter Sentiments – 3 12 min
- Lecture18.7
  
  Twitter Sentiments – Code/Dataset

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Data structures and algorithms

Machine Learning with python

Aptitude

Data structures and algorithms

Machine Learning with python

Aptitude

Machine Learning

Machine Learning with python

Course Features

Getting Started 1

Python - 1 11

Python - 2 5

Numpy 3

MatplotLib 2

Pandas 6

Introduction to Machine Learning 2

Linear Regression 13

Logistic Regression 4

Classification Metrics 4

Feature Scaling 3

Decision Trees 10

Random Forest 4

Naive Bayes 6

KNN 6

SVM 7

PCA 7

NLP 7

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Ready to Learn ?

Data structures and algorithms

Machine Learning with python

Aptitude

Data structures and algorithms

Machine Learning with python

Aptitude

Machine Learning

Machine Learning with python

Course Features

Getting Started 1

Python - 1 11

Python - 2 5

Numpy 3

MatplotLib 2

Pandas 6

Introduction to Machine Learning 2

Linear Regression 13

Logistic Regression 4

Classification Metrics 4

Feature Scaling 3

Decision Trees 10

Random Forest 4

Naive Bayes 6

KNN 6

SVM 7

PCA 7

NLP 7

Reviews

Leave A Reply Cancel reply

Contact

Useful Links

Ready to Learn ?

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