Machine Learning

A subset of artificial intelligence (AI), machine learning (ML) is the area of computational science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside of human interaction. Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. If any corrections are identified, the algorithm can incorporate that information to improve its future decision making. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.

Data is the lifeblood of all business. Data-driven decisions increasingly make the difference between keeping up with competition or falling further behind. Machine learning can be the key to unlocking the value of corporate and customer data and enacting decisions that keep a company ahead of the competition.

Main Features

  • Master Python and Scikit-Learn for Data Science and Machine Learning
  • Build an e-mail spam classifier using Naive Bayes classification Technique
  • Use train/test and K-Fold cross validation to choose and tune your models
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
  • Get a deeper intuition about different Machine Learning nomenclatures.
  • Solve and Deal with different real-life and businesses problems
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem

R for Machine Learning

1
What is R
2
Introduction to R
3
R installation
4
Using R as calculator
5
Strings and constants in R
6
Data Structures in R

Data Type and data structures in R

1
Repeat Function
2
Sequence Function
3
Sub-setting observations from a vector
4
Sub-setting observations from a dataframe

Basic Operations in R

1
Missing values in R
2
Subsets of Factor
3
The Recycling rule
4
Type Coercion
5
Numeric, Automatic, Factor
6
Changing factor levels

Additional Topics on Data Structures

1
Attributes
2
Classes
3
Date function
4
Date Coercion
5
Cross tabulation
6
Exploring Objects
7
Generic Functions
8
Importing different files
9
Text files
10
SAS Files
11
SPSS Files
12
Excel CSV files

Introduction to library concept in R

1
Condition statements in R: if, ifelse
2
Loops in R
3
The repeat statements
4
Lapply, Sapply, apply
5
Creating User defined functions

Charting on R

1
Global and Local Variable
2
Function within function
3
Charting and plotting with R
4
Line diagram
5
Histogram
6
Bar chart
7
Box plot
8
Pie Chart
9
Creating Google charts in R
10
Plotting Google Maps

Python for Machine Learning

1
Introduction to Python
2
Python Installation
3
Variables and inputs
4
Basic command line functions and GUI functions on Basic command line functions and GUI functions on Simple Calculations, Numeric functions and String Functions

Introduction to Anaconda

1
Why Anaconda
2
IDE Spyder
3
Getting started with Spyder Basics
4
Variables
5
Numeric Operations Types of data Isinstance
6
Logical Operators
7
Data Structures and Conditional Executions
8
Dictionaries in Python
9
Key Value Pairs
10
Different types of operators
11
Mutability
12
Different mutability for different sequence structures
13
Indexing and slicing
14
Control Flow tools
15
Conditional Statements
16
While Loop

Working with Different Functions

1
Built in functions
2
Different functions and their uses
3
Using different functions
4
Built in modules
5
Executing functions using libraries
6
User defined functions
7
Lambda Keyword
8
Map() function
9
Filtering
10
Reduce()
11
Default Parameters
12
Multiple Parameters
13
Local Variables
14
Global Variables
15
List Comprehension

Expressions and Exceptions

1
Range
2
File input output
3
Types of errors
4
Exception Handling

Data Science and Predictive Analytics

1
Statistical Tests: One Sample T Test
2
Two independent sample T test
3
Paired Sample T test
4
One-Way Anova
5
Two-Way Anova
6
Chi Square test of Independence
7
Correlation Analysis
8
Partial Correlation
9
Factor Analysis
10
Cluster Analysis
11
Linear Regression
12
Logistic Regression
13
Time Series: ARIMA modelling
14
Autocorrelations
15
Forecasting

Data Science on Python

1
Introduction to Data Science
2
Graphs and Plotting
3
Working on Pandas
4
Exploratory Analysis
5
Why Python for Data Science
6
Different Editors for Python
7
Jupyter Notebook and Spyder
8
Working with Libraries
9
Libraries required for Data analysis
10
Numpy, Scipy, Matplolib and Pandas
11
Bar charts, Line Charts, Scatter Plots
12
2D and 3D Plotting using Matplotlib
13
Importing csv files
14
Building Predictive Models
15
Linear Regression
16
Logistic Regression
17
Time Series Analysis
18
Data Visualisation with plotly

Machine Learning

1
Introduction to ML
2
Basic idea behind ML
3
Impact of ML in today’s world
4
Core concepts

Steps in ML

1
Types of ML algorithms
2
Supervised Learning
3
Unsupervised Learning
4
Reinforcement Learning

Applications of ML

1
Implementing the following ML algorithms on R and Python
2
Decision Tree
3
Support Vector machine(SVM)
4
Naïve Bayes
5
KNN (K-Nearest Neighbours)
6
K-Means
7
Random Forest
8
Gradient Boosting

Mathematical Concepts

1
Calculus Concepts: differential and integral
2
Real Analysis: Sets, functions and Hyperplane
3
Theory of Probability
4
Conditional probability
5
Bayes Theorem
6
Concept of array and matrices
7
Concept of Distance Measures/metrics: Euclidean Minkowski Hamming
8
Polynomial Functions
9
Radial Basis functions
Faq Content 1
Faq Content 2

Productivity Hacks to Get More Done in 2018

— 28 February 2017

  1. Facebook News Feed Eradicator (free chrome extension) Stay focused by removing your Facebook newsfeed and replacing it with an inspirational quote. Disable the tool anytime you want to see what friends are up to!
  2. Hide My Inbox (free chrome extension for Gmail) Stay focused by hiding your inbox. Click "show your inbox" at a scheduled time and batch processs everything one go.
  3. Habitica (free mobile + web app) Gamify your to do list. Treat your life like a game and earn gold goins for getting stuff done!


4
4 out of 5
6 Ratings

Detailed Rating

Stars 5
3
Stars 4
0
Stars 3
3
Stars 2
0
Stars 1
0

{{ review.user }}

{{ review.time }}
 

Show more
Please, login to leave a review
Add to Wishlist
Enrolled: 34 students
Duration: 260 hours
Lectures: 140
Video: 9 hours
Level: Intermediate

Archive

Working hours

Monday 9:30 am - 7.00 pm
Tuesday 9:30 am - 7.00 pm
Wednesday 9:30 am - 7.00 pm
Thursday Closed
Friday 9:30 am - 7.00 pm
Saturday 9:30 am - 7.00 pm
Sunday 9:30 am - 7.00 pm
WhatsApp chat