Data Science using Python (WT)


Data Science using Python (WT)


Course fee including GST.

Please login to purchase the course.

The batch will start the next day of the registration date


You will get:

On successful completion of your training, you will get three things:

  • Certificate of Completion
  • Project Letter
  • Internship Letter


New batch starting from 25th January, 2022


07:00 to 08:00

Course Features
  • Duration: 4 Weeks of Training & 2 Weeks Project Work
  • 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 mathematical calculation skills and logical skills

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
  • Time-saving & Cost-effective
  • Get trained via industry experts (having 10+ years of experience in the same field, corporate trainers)
  • Full of hands-on practical exposure for better understanding
  • Adding super solid value in your professional career
  • Weekend Doubt clearing sessions.
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