About Course:

Python is also known to be an important programming language for web development, artificial intelligence, scientific computing and data analysis. Many organizations use python to build productivity tools, desktop applications and much more. It is highly popular and dependable as many organizations uses python for their business purposes and are constantly looking for people with Python Programming skills. Therefore, it is necessary that you begin with Python as it offers great scope and job opportunity. Sage Academy is a leading Python training provider in New Delhi.

Who can learn this course:

Sage Academy is a rapidly developing corporate practical training and development company based in Delhi that helps people to learn new skills and become job-worthy. Enrolling our Python Machine Learning program that is helpful to career enhancement of experienced professionals or Freshers. People who have taken a sabbatical from their careers like Graduates can also learn new skills and become employable with the help of our practical training and certifications courses. We promise to meet all your training and development needs and make you an expert in a few weeks. Trust us once and you’ll be happy you did.

Python Machine Learning Training Course

Module 1 - Basic Python

Using Arithmetic Operators in Python

The Double Equality Sign

How to Reassign Values

Understanding Line Continuation

Indexing Elements

Structuring with Indentation

Operators

Create Functions with Parameters

Module 2 – Data Handling and Manipulation

Introduction to NumPy and SciPy

Introduction To Type of Data Variables

Data Summarization Techniques

Building A Data Dictionary

Outlier Treatment

Missing Value Treatment.

Data manipulation using Pandas

Import and export

Database access with SQL.

Module 3 - Analytics and Visualization

Introduction to Seaborn and Matplotlib

Plotting with Matplotlib

Types of Charts & Graphs (Line, Bar, Histogram, Pie Chart, Scatter Plot)

Module 4 - Linear Regression, Polynomial Regression and Logistic Regression:

Introduction to linear regression technique & it uses

Details of ordinary least squares estimation technique

Modeling steps

Validation of linear regression assumptions

Metrics to measure model performance.

Data Preparation Model Building

Introduction to logistic regression technique & it uses

Maximum likelihood estimation technique

Dependent variable definition, handling

Weight of Evidence & Information Value

Variable reduction

Model statistics interpretation

Module 5 - Classification using Naive Bayes (Supervised Learning)

Understanding Naïve Bayes – basic concepts & algorithm.

Understanding decision trees

Understanding classification rules.

Applying Naïve Bayes Classifier

K Nearest Neighbors

Applying K Nearest Neighbors

Decision Tree, Apply Decision tree

Random Forest, Apply Random Forest

Vector Machine, Apply Support Vector Machine

Module 6 – Classification using Nearest Neighbors (Unsupervised Learning):

Hierarchical Clustering, Apply Hierarchical Clustering

K Means Clustering, Apply K Means Clustering

Understanding classification using nearest neighbors

The KNN Nearest Neighbors

Preparing data for use with KNN

Outlier Detection, Principal Component Analysis

Singular Value Decomposition

Apply Singular value decomposition

Module 7 - Neural Networks and Support Vector Machines:

Understanding neural networks. Activation functions. Network topology.

Building Neural Network with TensorFlow.

Convolutional Neural Networks (CNN)

Understanding SVM. Classification with hyper planes. Finding the maximum margin.

Using kernels for nonlinear spaces.

Module 8 – Natural Language Processing:

 Text Classification, Dataset for Text Classification

Text Classification using RNN

Text classification using CNN

Information Extraction

Named Entity Recognition (NER) using Spacy

Sentiment Analysis, Sentiment analysis using Vader

Text Generation, Text Generation using FNET.

Text Generation using Recurrent Long Short-Term Memory Network

 Module 9 - Text mining And Over fitting And Initialization:

Main concepts and components of text mining

Text mining tasks and approaches

An understanding of the art of the possible in Text Analytics – the applicability

components and benefits

Under fitting and Over fitting for Classification

Training, Validation, and Test Datasets

N-Fold Cross Validation

Early Stopping or When to Stop Training

 Initializations

Module 10- Report Development