4_Data Science using Python (with – Internship | Project Letter)


4_Data Science using Python (with – Internship | Project Letter)


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The batch will start the next day of the registration date

Training & Duration

  • Live classes (Monday to Friday)
  • 4 Weeks of Training & 2 Weeks of Project Work
Target Audience

The course can be taken by:

Students: All students who are pursuing professional graduate/post-graduate courses related to computer science or Information Technology.

Teachers/Faculties: All computer science and engineering teachers/faculties.

Professionals: All IT professionals, who wish to acquire new skills or improve their existing skills.

Course Features
  • Online lectures: Online live lectures.
  • Updated Quality content: Content is the latest and gets updated regularly to meet the current industry demands.
Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.

2. At the end of the program, a final assessment will be conducted.


1. All successful participants will be provided with a certificate of completion.

2. Students who do not complete the course / leave it midway will not be awarded any certificate.

  • Basic knowledge of computers.
  • Knowledge of Python is essential.

If you are not familiar with Python suggested course is.

Topics to be covered
  • Intro ML, Applications of ML, types of ML
  • Introduction To Machine Learning
  • Supervised and unsupervised ML, steps in ML
  • History and Evolution
  • Artificial Intelligence Evolution
  • Find out where Machine Learning is applied in Technology and Science
  • Supervised Learning
  • Unsupervised Learning
  • Data Analysis Packages
  • Intro NumPy and some related NumPy functions
  • NumPy
  • Pandas with some operations
  • SciPy
  • Intro Matplotlib and some plotting functions
  • Matplotlib
  • Pandas
  • Sklearn
  • Overfitting, underfitting, generalization, building, and training
  • Regression
  • Regression model for boston dataset
  • Classification
  • Generalization, Overfitting, and Underfitting
  • Intro Linear regression, Simple linear regression, loss function
  • Understand how continuous supervised learning is different from discrete learning
  • R_squared, simple linear regression using scikit-learn
  • Code a Linear Regression in Python with scikit-learn
  • Feature scaling, one-hot encoding, label encoding, multiple
  • Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions
  • Linear regression example
  • k-Nearest Neighbor
  • Linear models
  • Naive Bayes Classifiers
  • Decision trees
  • Support Vector Machines
  • Challenges in unsupervised learning
  • Preprocessing and Scaling
  • Applying data transformations
  • Scaling training and test data the same way
  • Principal Component Analysis (PCA)
  • A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning
  • How Deep Learning Works?
  • Activation Functions
  • Training a Perceptron
  • TensorFlow code-basics
  • Tensorflow data types
  • Tensorflow methods
  • Introduction to Neural Networks
  • Neural Network Architecture
  • Linear Regression example revisited
  • The Neuron
  • Neural Network Layers
  • The MNIST Dataset
  • Coding MNIST NN
  • Understand the limitations of a Single Perceptron
  • Deepening the network
  • Convolutional Neural Networks
  • ConvNet Architecture
  • Overfitting and Regularization
  • Max Pooling and ReLU activations
  • Dropout
  • Strides and Zero Padding
  • Coding Deep ConvNets demo
  • Visualizing NN using Tensorflow
  • Tensorboard
  • Keras API
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers

For inquiry call:  8953463074

Online Live Training Program 2022

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