- Classes start on 13
^{th}June 2020 - 40 hours of learning Over 10 weeks
- Online format with Self-paced classes
- Course fee
~~₹ 40,000~~₹ 16,000/-

Artificial Intelligence and Machine Learning are the buzzwords across industries at the moment. We use them everyday, be it through search engines, online shopping, or the apps on our phone. But what exactly are their mechanics? And how can you be the one engineering them?

This course answers these questions, and more. It offers an in-depth understanding of ML libraries, optimization through ML, and the functional mathematics of AI. Taught by Kevin Vivian, an AI Engineer at Applied Materials in Santa Clara, California, this course will equip you with industry-ready AI and ML skills and guide you to building your own portfolio of projects.

By the time you complete **Applied AI**, you will -

- Learn the mathematics of AI
- Master ML for efficient result-optimization
- Gain unprecedented insights into ML libraries
- Create your own portfolio of projects

- Laptop with a browser for Jupyter Notebook

definition, scalars, addition, scalar multiplication, inner product (dot product), vector projection, cosine similarity, orthogonal vectors, normal and orthonormal vectors, vector norm, vector space, linear combination, linear span, linear independence, basis vectors

definition, addition, transpose, scalar multiplication, matrix multiplication, matrix multiplication properties, hadamard product, functions, linear transformation, determinant, identity matrix, invertible matrix and inverse, rank, trace, popular type of matrices- symmetric, diagonal, orthogonal, orthonormal, positive definite matrix

- Eigenvalues & eigenvectors
- concept, intuition, significance, how to find
- Principle component analysis
- concept, properties, applications
- Singular value decomposition
- concept, properties, applications

- Scalar derivative: definition, intuition, common rules of differentiation, chain rule, partial derivatives
- Gradient: concept, intuition, properties, directional derivative

how to find derivative of {scalar-valued, vector-valued} function wrt a {scalar, vector} -> four combinations- Jacobian

local/global maxima and minima, saddle point, convex functions, gradient descent algorithms- batch, mini-batch, stochastic, their performance comparison

- Basic rules and axioms: events, sample space, frequentist approach, dependent and independent events, conditional probability
- Random variables- continuous and discrete, expectation, variance, distributions- joint and conditional

- Bayes’ Theorem, MAP, MLE
- Popular distributions- binomial, bernoulli, poisson, exponential, gaussian
- Conjugate priors

- Variance, Mean, Standard Deviation
- Different forms of averages (e.g. weighted averages)

- Data Visualization
- Histograms, box plots, scatter plots, QQ Plots etc.
- Sampling distributions

- Information theory- entropy, cross-entropy, KL divergence, mutual information, Markov Chain- definition, transition matrix, stationarity

- Information theory- entropy, cross-entropy, KL divergence, mutual information
- Markov Chain- definition, transition matrix, stationarity

A new age data scientist highly skilled in programming languages like R and Python as well as libraries like Pandas, Numpy, and Scikit-Learn among others, Kevin Vivian brings a wealth of knowledge and rich real-time industry experience. Currently working with ICURO in Santa Clara, California, he is also an accomplished mathematician and is extremely passionate about teaching.

Read moreConnect with a diverse peer group from a multitude of industries like IT, ITES, Telecom, E-commerce, FMCG and the Automotive sectors and engage in conversations with leading industry experts.

Our course -- Applied AI -- enrols up to 70 students. Since they bring a variety of experiences and skills from their respective fields, you will have the chance to participate in intensive cross learning and form a rich network for life.

- Exclusive networking
- Real-time interactions
- Immersive learning