Machine Learning with Python

About Machine Learning

Machine Learning is the art of training machines for automated Data Processing, Analysis and Prediction. It deals with the following concepts:
 Fundamentals of using data to train machines
 Representation of an artificial neural network
 Linear regression with multiple variables
 Logistic regression for classifying data
 Support Vector Machines algorithms
 Designing of a machine learning system
 Principle Component Analysis for data modeling.

Course Details

 Course Name:

Machine Learning With Python

 Course Duration:

2.5 Month(s)

 Course Fee(in Rupees):

Rs.20000 18000 + GST

 Training Mode:


 Project Work:

One Machine Learning Project

 Lecture Days:

All Days of Week

 Payment Options:

Cash, PayTM, Netbanking

Contact Branch For Free Demo Class


 Those who have interests in learning Python


 IT Professionals

 If you want to learn Machine Learning

 If you want to become a Data Scientist


 Experienced and qualified trainers Consultants form product companies, IT companies and telecom companies.

 Vast experience in software development, telecom, IoT product design, development boards, etc.

 All our courses are tailored for building professionals with employable skills.

 Our IoT Trainings are structured to induct you deep into building an industry use case solutions.

 The trainers are handpicked and have immense level of experience and willing to share their experience and knowledge.

 We have a well-equipped lab that will help you accelerate your learning.

 We are experience in some of the most advanced and best courses in the cutting-edge technologies and domains both online and class room based training.


  •  Introduction To Working Environments/Linux
  •  Introduction To Python
  •  Hands-on using Jupyter/Anaconda
  •  Introduction To NumPy & Pandas
  •  Projects
  •  Statistical Inference
  •  Types of Variables
  •  Probability Distribution
  •  Measures of Central Tendencies
  •  Normal Distribution
  •  Matrices and Vectors
  •  Addition and Scalar Multiplication
  •  Matrix Vector Multiplication
  •  Matrix Multiplication
  •  Matrix Multiplication Properties
  •  Inverse and Transpose
  •  Linear Regression
  •  Gradient Descent
  •  Gradient Descent For Linear Regression
  •  Polynomial Regression
  •  Learning Curves
  •  Regularized Linear Models
  •  Logistic Regression
  •  Large Margin Intuition
  •  Mathematics Behind Large Margin Classification
  •  Linear SVM Classification
  •  Non-Linear SVM Classification
  •  SVM Regression
  •  Training and Visualizing a Decision Tree
  •  Making Predictions
  •  Estimating Class Probabilities
  •  The CART Training Algorithm
  •  Gini Impurity or Entropy
  •  Regularization Hyper Parameters
  •  Regression
  •  Instability
  •  Introduction To Machine Learning
  •  Machine Learning Application
  •  Introduction To AI
  •  Different Types of Machine Learning
  •  Machine Learning Projects Checklist
  •  Frame the problem and look at the big picture.
  •  Get the data.
  •  Explore the data to gain insights.
  •  Prepare the data for Machine Learning algorithms.
  •  Explore many different models and short-list the best ones.
  •  Fine-tune model
  •  Present the solution.
  •  Launch, Monitor and Maintain the system.
  •  Voting Classifiers
  •  Bagging and Pasting
  •  Random Patches and Random Subspaces
  •  Random Forests
  •  Boosting
  •  Stacking
  •  Unsupervised Learning: Introduction
  •  K-Means Algorithm
  •  Optimization Objective
  •  Random Initialization
  •  Choosing the Number of Clusters
  •  Neurons and the Brain
  •  Model Representation
  •  Multiclass Classification
  •  Back Propagation Algorithm
  •  Back Propagation Intuition
  •  Unrolling Parameters
  •  Gradient Checking
  •  Random Initialization
  •  Training a Binary Classification
  •  Performance Measures
  •  Confusion Matrix
  •  Precision and Recall
  •  Precision/Recall Tradeoff
  •  The ROC Curve
  •  Multi-class Classification
  •  Multi-label Classification
  •  Multi-output Classification
  •  Deep Learning
  •  NLP
  •  Tensor Flow
  •  Problem Motivation
  •  Gaussian Distribution
  •  Algorithm
  •  Developing and Evaluating an Anomaly Detection System
  •  Anomaly Detection vs. Supervised Learning
  •  Choosing What Feature to Use
  •  Multivariate Gaussian Distribution
  •  Anomaly Detection using the Multivariate Gaussian Distribution