Complete Machine Learning Course With Python

Learn to create Machine Learning Algorithms in Python using Different Datasets

What you'll learn

Around 15+ Machine learning algorithms explanation with different datasets and 15+ assignment for practice

Supervised and Unsupervised learning models,PRINCIPLE COMPONENT ANALYSIS(PCA)

Solve any problem in your business, job or personal life with powerful Machine Learning models

Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more

Requirements

Basic Python programming knowledge is necessary

Good understanding of linear algebra,Stastics

Description

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins);Gain complete machine learning tool sets to tackle most real world problemsUnderstand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix,etc. and when to use them.Combine multiple models with by bagging, boosting or stackingMake use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your dataDevelop in Spyder and various IDECommunicate visually and effectively with Matplotlib and SeabornEngineer new features to improve algorithm predictionsMake use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen dataUse SVM for handwriting recognition, and classification problems in generalUse decision trees to predict staff attritionAnd much much more!No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area.If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you!Take this course and become a machine learning engineer!

Overview

Section 1: Introduction

Lecture 1 What Is Machine learning

Lecture 2 Key Skills needed to learn Machine learning

Lecture 3 Supervised learning vs Unsupervised Learning

Lecture 4 Dependent Variable vs Independent Variable

Lecture 5 What Does This Course Cover

Lecture 6 Basic Python Concepts

Section 2: Introduction to Machine Learning and Anaconda Installation

Lecture 7 Introduction to Machine Learning

Lecture 8 Anconda Installation

Section 3: Exploratory Data Analysis

Lecture 9 What is Exploratory Data Analysis(EDA)

Lecture 10 knowing initial details of dataset

Lecture 11 Modifying or removing unwanted data

Lecture 12 Retrieving Data

Lecture 13 Statistical Information

Lecture 14 Drawing Graphs

Lecture 15 EDA Assignment

Section 4: Outliers

Lecture 16 What is Outliers

Lecture 17 Finding the Outliers

Lecture 18 IQR and handling the outliers

Section 5: Simple Linear Regression

Lecture 19 What is Regression

Lecture 20 What is simple liner regression model

Lecture 21 What is r-squared Value

Lecture 22 Simple linear regression Program-1

Lecture 23 Simple linear regression Program-2(train and test data)

Section 6: Multiple Linear Regression

Lecture 24 What is Multiple Linear Regression

Lecture 25 Multiple Linear Regression -program 1

Section 7: One Hot Encoding

Lecture 26 What Is One Hot Encoding

Lecture 27 One Hot Encoding-First way

Lecture 28 One Hot Encoding-Second way

Lecture 29 One Hot Encoding-Program 1

Lecture 30 One Hot Encoding-Program 2(Third way)

Section 8: Polynomial Linear Regression

Lecture 31 What is Polynomial Linear Regression

Lecture 32 Polynomial Linear Regression Program-1

Section 9: Ridge Regression

Lecture 33 What is Bias and Variance

Lecture 34 What is Regularization

Lecture 35 Ridge Regression-Program 1

Lecture 36 Ridge Regression-Assignment

Section 10: Lasso Regression

Lecture 37 What is Lasso regression and practice program-1

Section 11: ElasticNet Regression

Lecture 38 what is ElasticNet Regression and practice program-1

Section 12: Logistic Regression

Lecture 39 What is Logistic Regression and program-1

Section 13: Support Vector Machine(SVM)

Lecture 40 What is Support Vector Machine

Section 14: Naive Bayes Classification

Lecture 41 What is Naive Bayes Classification

Lecture 42 Naive Bayes Classification Program-1

Lecture 43 Naive Bayes Classification Program-2

Section 15: KNN Classifier

Lecture 44 KNN Classifer defination and its practice program-1

Section 16: Decision Trees

Lecture 45 Decision Trees Defination and its program-1

Section 17: Random Forest

Lecture 46 Random Forest Defination and its practice program-1

Section 18: K-Means Clustering(unsupervised model)

Lecture 47 What is K-Means Clustering

Lecture 48 K-Means Clustering Program-1

Section 19: Apriori Algorithm

Lecture 49 What is Apriori Algorithm

Section 20: Principle Component Analysis(PCA)

Lecture 50 what is Principle Component Analysis(PCA)

Lecture 51 Principle Component Analysis Program-1

Lecture 52 Principle Component Analysis Program-2

Lecture 53 Principle Component Analysis-Assignment

Section 21: K-Fold Cross Validation

Lecture 54 What is K-Fold Cross Validation

Lecture 55 K-Fold Cross Validation Program-1

Section 22: Model Selection

Lecture 56 What is Model Selection

Lecture 57 Model Selection Program-1

Section 23: Assignment Solutions

Lecture 58 Assignment Solutions

Anyone willing and interested to learn machine learning algorithm with Python,Anyone who want to choose carrer in Datascience,AI,Machine learning,Data analytics,Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms