**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