Machine Learning With Python
Vitti offers certified project based training program in Machine Learning

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Machine Learning is field of computer science that uses statistical techniques to give computer systems the ability to learn with Data without explicitly being programmed. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills and practical exposure on skillsets like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
Especially developed in collaboration with academia & AI Industry experts to reskill and retool working professionals towards Artificial Intelligence space, this program offers the benefits of Experienced Faculty Led Sessions: Live-Interactive Online classes and practical exposure. Program content & structure designed in collaboration with faculty and Industry experts.

Duration: 50 Hours Session: 2 hours/day - 2 days a week (weekend) Batch Size: 50 Students

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This course is focused on building industry-ready professionals who can work on Machine Learning & it will provide in-depth understanding of Artificial Neural Networks, Data Analytics for Machine Learning and its mechanism. Furthermore, you will be provided with project based Learning which is an important to gain expertise in Artificial Intelligence. With this program, one will be able to automate real life scenarios using Machine Learning Algorithms. As part of the course, practical use cases of Machine Learning with Python programming language will be discussed for better & improved learning experience.

Syllabus Structure

What you will learn from this course


Module 1:

Module 1 : Introduction to Machine Learning
  • Machine Learning Categories
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Frameworks for Building
  • Machine Learning Systems
  • Machine Learning Python Packages

Module 2:

Module 2 : Data Analysis Packages
  • NumPy
  • Pandas
  • Matplotlib
  • Machine Learning Core Libraries

Module 3:

Module 3 : Machine Learning Perspective of Data
  • Dealing with Missing Data
  • Handling Categorical Data
  • Normalizing Data
  • Feature Construction or Generation
  • Exploratory Data Analysis
  • Univariate Analysis
  • Multivariate Analysis

Module 4:

Module 4 : Supervised Machine Learning
  • Classification
  • Collecting, preparing and exploring the data
  • Regression
  • Multivariate regression
  • KNN algorithm
  • Classification using Decision Trees and rules
  • The naïve Bayes classification
  • Preparing and exploring data
  • Training a model on the data
  • Evaluating the model performance
  • Logistic Regression
  • Support Vector Machine
  • Artificial Neural Network

Module 5:

Module 5 : Unsupervised Machine Learning
  • Clustering as a machine learning task
  • Hierarchical Clustering
  • K-means
  • Finding Value of k
  • Principal Component Analysis (PCA)
  • Hidden Markov Mode

Module 6:

Module 6 : Convolutional Neural Network
  • Architecture of CNN
  • Types of layers in CNN
  • Building an image classifier using CNN
  • Deep Learning with CNN

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