Data Science using Python (WT)

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Data Science using Python (WT)

Rs.1,525.42

Course fee including GST.

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The batch will start the next day of the registration date

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You will get:

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

  • Certificate of Completion
  • Project Letter
  • Internship Letter

Batch:

New batch starting from 25th January, 2022

Timing:

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.

Certification

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
Benefits:
  • 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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