Machine Learning, Business analytics with R Programming & Py

Course Curriculum

Chapter 1: Complete machine learning & data science course Introduction

Lecture 1: Introduction

Lecture 2: How to get help for Machine learning & Data science

Lecture 3: Data science & machine learning as career option

Lecture 4: How to make right decisions for your career in data science & machine learning

Lecture 5: Various Job options for aspiring data scientists & machine learning engineers

Lecture 6: AI Vs ML Vs DL with Types of machine learning

Chapter 2: Job hunting strategy

Lecture 1: Strategy 1 with tips on resume/cv building

Lecture 2: Strategy 2 to target job avenues to get more calls & offers

Chapter 3: Hands-on R programming for machine learning & data science

Lecture 1: R Introduction with installation of rstudio

Lecture 2: Vectors, Matrix & Data frame

Lecture 3: Data types in R

Lecture 4: Variables & Objects in R

Lecture 5: Comments & Vectors in R

Lecture 6: Data wrangling with R-Part 1

Lecture 7: Data wrangling with R-Part 2

Lecture 8: Operators in R-Part 1

Lecture 9: Operators in R-Part 2

Lecture 10: Loops in R

Lecture 11: If Else conditional blocks in R

Lecture 12: Functions in R

Lecture 13: Assignment for R Programming fundamentals

Chapter 4: Machine learning fundamentals

Lecture 1: Reading various kind of files with R

Lecture 2: Data pre-processing introduction- selection & manipulation

Lecture 3: Data selection & manipulation-Rows & Columns

Lecture 4: Data selection & manipulation with Dplyr- Part 1

Lecture 5: Data selection & manipulation with Dplyr- Part 2

Lecture 6: Data selection & manipulation with Subset & Merge

Lecture 7: Data selection & manipulation-Handling missing data

Lecture 8: Data manipulation & selection assignment

Chapter 5: Data visualization with R

Lecture 1: Data visualization with R- introduction

Lecture 2: Histogram vs bar plot with plotting missing values

Lecture 3: Bar plots and Histograms with R

Lecture 4: Horizontal bar plots and Plot function

Lecture 5: More on Plot function with heat map

Lecture 6: Boxplot with Pair & Par commands

Lecture 7: Line graphs and Maps

Lecture 8: GGPlot 2 Introduction

Lecture 9: Data visualization with GGPlot2

Lecture 10: Lattice and Scatter3d plot libraries

Lecture 11: Assignment

Chapter 6: Applied Statistics for Machine learning

Lecture 1: Introduction to applied statistics with Variables and Sample Size

Lecture 2: Descriptive vs Inferential analysis

Lecture 3: Mean, Median, Mode and Range

Lecture 4: Variance and Standard deviation

Lecture 5: Standard Error- Skewness with Kurtosis

Lecture 6: P value with confidence interval

Lecture 7: T test and F ratio

Lecture 8: Hypothesis testing

Chapter 7: Introduction to Machine learning models

Lecture 1: Machine learning fundamentals

Lecture 2: Regression fundamentals

Lecture 3: Classification fundamentals

Lecture 4: Fundamentals of dimension reduction and data reduction models

Chapter 8: ANOVA with R

Lecture 1: ANOVA introduction & fundamentals

Lecture 2: ANOVA in R

Lecture 3: ANOVA Project

Chapter 9: Evaluation metrics or loss function for linear regression

Lecture 1: Evaluation metrics or loss function for linear regression

Chapter 10: Linear regression with R

Lecture 1: Fundamentals of Linear regression

Lecture 2: Implementation of linear regression in R

Lecture 3: Linear regression project

Chapter 11: Logistic Regression with R

Lecture 1: Fundamentals of Logistic Regression

Lecture 2: Logistic Regression with R- Part 1- Data Wrangling

Lecture 3: Logistic regression with R-Part 2 Data Wrangling and visualization

Lecture 4: Logistic regression with R-Part 3 Conclusion with Prediction

Lecture 5: Logistic Regression Project

Chapter 12: Dimension reduction technique with principal component analysis

Lecture 1: Fundamentals of Dimension reduction technique with principal component analysis

Lecture 2: PCA implementation in r with princomp

Lecture 3: PCA project

Chapter 13: Clustering with K-Means

Lecture 1: Fundamentals of clustering with K-Means

Lecture 2: K-Means implementation in r

Lecture 3: K-Means Project

Chapter 14: Tree based models- CART technique & Random Forest

Lecture 1: Fundamentals of Decision tree and CART technique

Lecture 2: CART Implementation in R

Lecture 3: Fundamentals of Ensemble techniques with Random forest machine learning model

Lecture 4: Random Forest with R

Lecture 5: Random Forest Project

Chapter 15: KNN- K Nearest Model

Lecture 1: Fundamentals of KNN

Lecture 2: Implementation of KNN in R

Lecture 3: KNN Project