Overview

• Why learn Machine learning? 
• What are the course objectives?
• What skills will you learn with our Machine Learning Course?
• Who should take this Machine Learning Training Course?
• Why companies need Machine learning professionals with highest package?
• Future of Machine learning professionals in India and across the globe.

Concept

• Introduction to Machine Learning. Difference between Machine Learning, Deep Learning and Artificial Intelligence.
• Understanding the concepts, methods and models used in Machine Learning.
• Understanding the principles, design, implementation and validation of learning systems.

Understanding different Machine Learning technics

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning
• Understanding the basic Machine Learning Model.
• Why should I choose Python for Machine Learning?

Overview of the Python language

• Using Python console
• Why Jupyter and Spyder? 

Generating Python code

• Basic programming concepts/Scripts
• Text editors and Graphical User Interfaces (GUIs) for Python
• Packages (Numpy, Pandas, Matplotlib and Scikitlearn) – Very important

Introduction

• Basic Syntax 
• Variable and Data Types 
• Operator

Conditional Statements

• If 
• If- else 
• Nested if-else
• Looping
• For 
• While 
• Nested loops

Control Statements

• Break 
• Continue 
• Pass

String Manipulation

• Accessing Strings 
• Basic Operations 
• String slices 
• Function and Methods

Lists

• Introduction 
• Accessing list 
• Operations 
• Working with lists 
• Function and Methods

Tuple

• Introduction 
• Accessing tuples 
• Operations 
• Working 
• Functions and Methods

Dictionaries

• Introduction 
• Accessing values in dictionaries 
• Working with dictionaries 
• Properties 
• Functions

Functions

• Defining a function 
• Calling a function 
• Types of functions 
• Function Arguments 
• Anonymous functions 
• Global and local variables

Modules

• Importing module 
• Math module 
• Random module 
• Packages and Composition

Input-Output

• Printing on screen 
• Reading data from the keyboard 
• Opening and closing file 
• Reading and writing files 
• Functions

Data Preprocessing 

• Importing the dataset
• Importing the Libraries
• Missing Data
• Categorical Data
• Splitting the Dataset into the Training set and Test set
• Feature Scaling

Regression (Widely Used Supervised ML)

• In-depth understanding of Regression (Mathematics and Statistics)
• What is the difference between Regression and Correlation?
• Dataset + Business Problem Description
• Simple Linear Regression in Python / Excel / Excel Add-Ins 

Multivariate Linear Regression with k fold cross-validation

• Dataset + Business Problem Description
• Multiple Linear Regression Intuition 
• Prerequisites: What is the P-Value?
• Multiple Linear Regression in Python 
• Multiple Linear Regression in Python - Backward Elimination
• Multiple Linear Regression in Python - Automatic Backward Elimination

Polynomial Regression

• Polynomial Regression Intuition
• How to get the dataset
• Polynomial Regression in Python 

Support Vector Regression (SVR)

• How to get the dataset
• SVR in Python

Decision Tree Regression

• Decision Tree Regression Intuition
• How to get the dataset
• Decision Tree Regression in Python

Random Forest Regression

• Random Forest Regression Intuition
• How to get the dataset
• Random Forest Regression in Python

Evaluating Regression Models Performance

• R-Squared Intuition
• Adjusted R-Squared Intuition
• Interpreting Linear Regression Coefficients
• Regression Model Practice in Python

Classification (Widely Used Supervised ML)

Logistic Regression

• Logistic Regression Intuition
• Logistic Regression in Python

K-Nearest Neighbors (K-NN)

• K-Nearest Neighbor Intuition
• K-NN in Python

Support Vector Regression (SVR)

• How to get the dataset
• SVR Intuition
• SVR in Python

Kernel SVM

• Kernel SVM Intuition
• Mapping to a higher dimension
• The Kernel Trick
• Types of Kernel Functions
• How to get the dataset
• Kernel SVM in Python

Naive Bayes

• Bayes Theorem
• Naive Bayes Intuition
• How to get the dataset
• Naive Bayes in Python

Decision Tree Classification

• How to get the dataset
• Decision Tree Classification in Python

Random Forest Classification

• Random Forest Classification Intuition
• Random Forest Classification in Python

Voting Classification

• Best Algorithm Intuition in terms of accuracy
• Model evaluation
• Prediction with a real dataset

Classification using the unstructured dataset 

• Opinion mining project

Evaluating Classification Models Performance

• False Positives & False Negatives
• Confusion Matrix
• Accuracy Paradox
• CAP Curve
• CAP Curve Analysis

Clustering (Unsupervised ML Technics)

• K-Means Clustering
• K-Means  - Selecting the Number Of Clusters
• K-Means Clustering in Python

Hierarchical Clustering

• Hierarchical Clustering Using Dendrograms
• How to get the dataset
• HC in Python

Natural Language Processing

• Welcome to Natural Language Processing
• Natural Language Processing Intuition
• How to get the dataset
• Natural Language Processing in Python
• Sentiment Analysis 
• Word Cloud Analysis

Deep Learning

Artificial Neural Networks

• The Neuron and Activation Function
• How do Neural Networks work/learn?
• Gradient Descent
• Stochastic Gradient Descent
• Backpropagation
• How to get the dataset
• Business Problem Description
• ANN in Python 

Convolutional Neural Networks

• Plan of attack
• What are convolutional neural networks?
• Convolution Operation,  ReLU Layer,  Pooling, Flattening, Full Connection and Summary
• Softmax & Cross-Entropy
• CNN in Python

Dimensionality Reduction

Principal Component Analysis (PCA)

• Principal Component Analysis (PCA) Intuition
• PCA in Python 

Principal Component Analysis (PCA)

• Principal Component Analysis (PCA) Intuition
• PCA in Python 

Kernel PCA

• Kernel PCA in Python

Time Series Analysis 

• ARIMA 
• Facebook’s Prophet