PGP in AI and Machine Learning

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Course Description:

The PG Program in AI and Machine Learning aims to make extremely well-formed AI experts with great knowledge of mathematics, skilled in appropriate languages, and knowledge of advanced algorithms and applications.

This PGP in AI and Machine Learning Program is planned for those who wish to shift or progress their career in this stirring and well-paying field. We welcome professionals as well as fresher who are viewing great career in the industry.

Because of the rising need for bright and accurate decision making, there is a rapid growth in the adoption of AI and Machine Learning technologies. Therefore, these are balanced to stay the significant tools shortly.

Accelerate your future growth with this PGP in AI and Machine Learning with Careerera.

Why AI and Machine Learning

The hope of technology is AI and Machine Learning. As of automating functions to reform work methods, AI and Machine Learning are ever more used in every industry. The rising acceptance of this technology across verticals has raised the range and call for talented experts with a robust understanding of artificial intelligence and Machine Learning.

The three chief stages of Artificial Intelligence are:

Machine Learning- This stage of AI engages intelligent systems with algorithms to learn.
Machine Intelligence- This stage engages the use of enhanced perceptive abilities.
Machine Consciousness- This stage revolves around self-learning.


When you study AI and Machine Learning through the industry specialists, you get an advantage in your chase of making a career in this industry.

Careerera provides progressed AI and Machine Learning PGP Program planned and carry forward by specialists with a great experience. Learn from the supreme when you register in our training programs.

Audience Profile:

The intended audience for the PG Program in AI and Machine Learning are:

  • IT Professionals
  • Data Professionals
  • Individuals with basic programming skills interested in the AI & ML
  • Data Scientists viewing to get a big boost in their careers
  • Professionals looking for a career shift into AI & ML


Course Learning:

The course delivers the understanding of

  • NLP
  • Deep Learning
  • Reinforcement Learning
  • Graphical Models together with Predictive Analytics and Statistics


Course Essentials:

The program is focused at contenders who:

  • Acquires a Bachelor’s degree with a least of 50% total marks or equivalent
  • Are contented using a programming language and are known to academic level math and statistics.


Course Highlights:

  • Case Studies and Assignments
  • Practical applied Workshops
  • Interpersonal communication with Industry Mentors
  • Timely Doubt Resolution
  • Careerera Alumni Status
  • Placement Support
  • 24*7 Customer Support
  • Lifetime Access to learning materials
This course is planned for working experts viewing to select skills in advanced basics like Reinforcement Learning, Graphical Models, NLP, and Deep Learning together with a great foundation of Statistics. This program requires reliable work and time assurance over the total duration of 11 months.
The content will be a combination of lectures from industry experts with outstanding faculty. Moreover, the course includes some live lectures or sessions keen on solving your queries and support learning.
Upon completion of the PG Program in AI-ML, you will be eligible for respective job opportunities: Business Analyst Product Analyst Machine Learning Engineer
All needed content is integrated with PG Program in AI-ML fee and accessible lifetime once you are registered with Careerera course. You are welcome to buy additional material for your reference from the expert suggestion.
Yes, once enrolled, you will get a programming refresher. We strongly suggest that you finish these preliminary so that you are prepared from the first day of the course.
  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores
  • Experiential learning project
  • Python Overview
  • Python Basics
  • Python functions, packages and routines
  • Pandas, NumPy, Matplotlib introduction
  • Pandas for Pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Seaborn for Data Visualization
  • Sci-kit Library
  • Case Studies and careers
  • Experiential Learning project
  • Introduction to Anaconda/Jupyter for coding/data visualisation
  • Introduction to Data Science, ML, AI
  • Supervised Learning
    • Introduction to Machine Learning
    • Supervised Learning concepts
    • Linear Regression (both Univariate and Multivariate)
    • Polynomial Regression (both Univariate and Multivariate)
    • Logistic Regression (Binary Class)
    • Logistic Regression (Multi Class)
    • K-NN Classification
    • Naive Bayesian classifiers
    • SVM - Support Vector Machines
    • Experiential Learning project
  • Unsupervised Learning
    • Unsupervised Learning concepts
    • Clustering approaches
    • K Means clustering
    • Hierarchical clustering
    • High-dimensional clustering
    • Expectation Maximization
  • Decision Trees
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Stacking
  • Experiential Learning project
  • PCA (Principal Component Analysis) and Its Applications
  • Confusion Matrix
  • Value based methods Q-learning
  • Policy based methods
  • User & item based recommendation systems
  • Collaborative filtering
  • Content based filtering
  • Hybrid recommendation systems
  • Performance measurement
  • Experiential Learning project
  • Text Analytics
  • Feature extraction
  • Model Defects & Evaluation Metrics
  • Model selection and tuning
  • Comparison of Machine Learning models
  • Experiential Learning project
  • Python (Pandas, Numpy, Scipy,
  • Matplotlib, Seaborn and Scikit-Learn)
  • Mini Projects
  • Machine Learning Lab session
  • Deep Learning
    • Neural Network Basics
    • Artificial Neural Network (ANN)
    • Forward Propagation
    • Backward Propagation
    • Deep Neural Networks
    • Recurrent Neural Networks (RNN)
    • Deep Learning applied to images using CNN
    • Tensor Flow for Neural Networks & Deep Learning
  • Convolutional Neural Networks
  • Keras library for deep learning in Python
  • Pre-processing image Data
  • Object & face recognition
  • Visualizing features & kernels
  • TensorBoard – Visualizing Learning, Graph Visualization
  • Synthesis and style transfer
  • Case Study: Visualizing a convoluted neural network
  • NLP library NLTK
  • Statistical NLP and text similarity
  • Syntax and parsing techniques
  • Text summarization techniques
  • Semantics and Generation
  • Uninformed and heuristic-based search techniques
  • Adversarial search and its uses
  • Planning and constraint satisfaction techniques
  • Python
  • Data libraries like Pandas, Numpy, Scipy
  • Python ML library scikit-learn
  • Python visualization library Matplotlib
  • NLP library NLTK
  • Tensor Flow
  • Keras
  • Group Presentation


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