Goals

Through this program, you will:

  • Brush-up the prerequisites to learn AI. (Maths, Stats, Python, Optimization)
  • Understand all the concepts of Machine Learning, Deep Learning, Data Analysis.
  • Apply these concepts with the latest tools, platform to build end to end models and solutions
  • Through Design thinking apply your new skills to solve case studies across verticals.
  • Manage the change involved in shifting toward an AI-driven business.

Specials Features

Develop an actionable understanding of what artificial intelligence really is, how it works, what it can do and what it requires to perform.

Examine best practices of global leaders in the field, such as Microsoft, Amazon and Google.

Learn and understand all the latest concepts in AI


Program is build in conjunction to the industry needs and is designed by the leading AI experts across the globe from Carnegie Mellon University, University of Oxford and others.

Program covers all the latest libraries and Platforms and help you attain a competitive edge.

This is a highly flexible program and case studies and practicals can we adapted/ modified as per an organization’s need

Who Can Join?

  • Under Graduates - B.SC | B.Com | BA Statistics | BA Maths | B.Tech (including Non-IT and Non-CSE departments)
  • Professionals looking to launch their career in Data Science
  • Beginners or recent graduates in Bachelor's or Master's Degree
  • Professionals working in BPO's and Data Entry Jobs
  • IT Fresher's
Date Fundamentals of Artificial Intelligence Duration
Pre-Requisites Introduction to AI & Nature of Intelligence 02 Hrs
Mathematics of Machine Learning: Linear Algebra, Multivariable Calculus 10 Hours
Probability and Statistics 10 Hours
Python for Data Science 10 Hours
Algorithms and Data Structures 5 Hours
EDA & Optimization 10 Hours
Introduction to Machine Learning 15 Hours
Assessment Exam, Project Submission Theory + Lab
Tools & Platform Python, R Covered in the Labs
Assessment Exam, Project Submission

Curriculum

Pre-Requisites


1. Introduction to AI & Nature of Intelligencesec7accordion icon5 Hours

The course offers fundamentals that you cannot find in any other course or a book. These fundamentals will be invaluable for your future work on ML and AI.

  • This course explores the nature of intelligence, ranging from machines to the biological brain. Information provided in the course is useful when undergoing ambitious projects in machine learning and AI. It will help you avoid pitfalls in those projects.
  • What are the differences between the real brain and machine intelligence and how can you use this knowledge to prevent failures in your work? What are the limits of today's AI technology? How to assess early in your AI project whether it has chances of success?
  • What are the most fundamental mathematical theorems in machine learning and how they are relevant for your everyday work?
2. Mathematics of Machine Learningsec7accordion icon5 Hours

A. Linear Algebra:

  • Vectors, Matrices
  • Tensors
  • Matrix Operations
  • Projections
  • Eigenvalue decomposition of a matrix
  • LU Decomposition
  • QR Decomposition/Factorization
  • Symmetric Matrices
  • Orthogonalization & Orthonormalization
  • Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions, Limits, Cauchy Kernel, Fourier Transforms)
  • Information Theory (Entropy, Information Gain)
  • Function Spaces and Manifolds.
  • Function Spaces and Manifolds.

B. Multivariate Calculus

  • Differential and Integral Calculus
  • Partial Derivatives
  • Vector-Values Functions
  • Directional Gradient
  • Hessian
  • Jacobian
  • Laplacian and Lagrangian Distribution
3. Statistics and Probability for Data Scientists
  • Probability Theory and Statistics:
  • Combinatorics
  • Random Variables
  • Probability Rules & Axioms.
  • Bayes' Theorem
  • Variance and Expectation
  • Conditional and Joint Distributions
  • Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian)
  • Moment Generating Functions
  • Maximum Likelihood Estimation (MLE).
  • Prior and Posterior
  • Maximum a Posteriori Estimation (MAP) and Sampling Methods.
  • Descriptive Statistics.
  • Hypothesis Testing
  • Goodness of Fit
  • Analysis of Variance
  • Correlation
  • Chi2 test
  • Design of Experiments
4. Python Programming Language
  • Python language fundamentals
  • Data Structures
  • Beautiful Soup
  • Regular Expressions
  • JSON
  • Restful Web Services (Flask)
  • NumPy
  • Plots in matplotlib, Seaborn
  • Pandas
5. Algorithms and Data Structure

A. Graph Theory: Basic Concepts and Algorithms

B. Algorithmic Complexity

  • Algorithm Analysis
  • Greedy Algorithms
  • Divide and Conquer and Dynamic Programming.

C. Data Structures

  • Array, List, Hashing, Binary Trees, Hashing, Heap, Stack etc
  • Dynamic Programming
  • Randomized & Sublinear Algorithm
  • Graphs

Main Program


1. Exploratory Data Analysis & Feature Engineering

A. Data Exploration And Preprocessing

  • Basic Plotting of Data
  • Outlier Detection
  • Dimensionality Reduction: Principal Component Analysis, Multidimensional Scaling
  • Data Transformation
  • Dealing with Missing Values

B. Feature Engineering

  • Feature extraction and feature engineering,
  • Feature transformation
  • Feature selection
  • Grid search
  • Automatically create features
  • Aggregations and transformations
  • Introduction about Feature tools
  • Introduction about Entities & Entity Sets, table Relationships, Feature Primitives, Deep Feature synthesis
2. Introduction to Machine Learning

A. Learning from Data:

  • The appeal of learning from examples, Motivational Case Studies, A formal definition of learning, Key Components of Learning, Population vs. Sample, Decision Boundary, Types of data, Typical Issues with Data, Types of Learning.
  • Learning as search: Instance and Hypothesis Space, Introductions to Search Algorithms, Cost Functions.
  • Version Spaces/Perceptron/Linear Regression /Nearest Neighbour
  • Overfitting/Regularization, Worst case performance: VC dimension, Bias-Variance Tradeoff, Non-Linear Embedding, Outlier Detection, Minimum description length.
  • Estimating Accuracy: Train/test split, Cross Validation, Bootstrap Hypothesis testing, Confusion Matrix, Sensitivity and Specificity, Precision and Recall, ROC curves and AUC, MAPE, Kappa Statistic, AIC, BIC
  • Data Science Process

B. Introduction to Bayesian Learning:

  • Probability review
  • Bayes rule
  • Conjugate Priors
  • Bayesian Inference
  • Bayesian linear regression
  • Bayes classifiers
  • Statistical Estimation. Maximum Likelihood Estimation c. Bayes error rate e. Curse of dimensionality
  • Laplace approximation, Naïve Bayes, Introduction to Bayesian Belief Networks, Introduction to Sampling, Gibbs sampling, Logistic regression
3. Supervised Machine Learning
  • Classification Algorithms: Decision Trees, Rule Induction, SVM
  • Dealing with Skewed Class Distribution and Cost-based Classification: Resampling
  • Regression: Lasso and Ridge Regression, MARS, OLS, PLS, GLM
  • Survival Analysis: Cox’s Regression, Weibull Distribution, Parametric Survival Models
  • Ensemble Models: Bagging, Boosting, Stacking, Random Forests, XGBoost, GBDT
  • Neural Networks and Introduction to Deep Learning: Multi-Layered Perceptron, Backpropagation, Convolutional Neural Networks, Encode-Decoders, Recurrent Neural Networks, Long Short-Term Memory (LSTM)
  • Time Series Forecasting: Time Series Decomposition, Holt-Winters, ARIMA, Intermittent Models, Time-frequency domain, Fourier transforms, wavelet transforms, Dynamic Regression Models, Neural Networks, Demand Forecasting
  • Case Study: Network Intrusion detection
  • Case Study: Weather Forecasting
  • Case Study: Image Classification
  • Case Study: Predictive Text Generation
  • Case Study: Customer Lifetime Modelling
  • Case Study: Churn Prediction
  • Case Study: Speech synthesis
  • Case Study: Named Entity Extraction
  • Case Study: Car navigation
  • Case Study: Diagnosis

Fees

Total Program Fee

Rs 10,000/- + GST

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Frequently Asked Questions

What is this preparatory Data Science program intended to provide?
Data science is mainly about figuring out trends and patterns based on statistics and jumbled data. You will be introduced to the basics of statistics, probability, from Python to Machine Learning & more. Expand your career horizon by adopting data science practices in various organizations.
When does the program begin?
AI preparatory Bootcamp will commence on 15th April 2022
How is the program delivered?
This is a self-paced preparatory Bootcamp with live interaction with qualified Teaching Assistants
Is there a refund policy for the program?
There will be no refund
What is the minimum educational qualification required for the program?
raduate in any discipline (esp. non-IT and non-CSE background)
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About APG Learning

APG LEARNING– A Division of Sakal Media Group APG Learning is the Skilling and Education vertical of the AP Globale Group that has its presence across different businesses including Media, Advisory, and Community Development.

APG Learning began operations a few years ago with a mission to educate students and ensure sustainable socio-economic development. We run some of the most successful employability-related courses in the fields of Media & communication, Business, Entrepreneurship, Finance,Technology& Lifestyle.

Our programs are designed to build entrepreneurs and leaders, who will go on to have a substantial social impact on economic development. With a faculty drawn from the industry as well as academia, learning at APG Learning melds together real-time experiences with academic knowledge. Also, APG Learning has kept up with the pace of Teaching in the Digital Age with our segment of online training as the approach towards teaching and learning has drastically changed with the advent of technology.

Contact Info

For any queries regarding admissions for the PGD Data Science program, or to establish industry or academia relationships, or additional assistance