AI Training

Brahma Capital Training Courses:

At Brahma, we are recruiting teams and individuals with excellent technical abilities and creative potential. We have invested in dozens of courses to help bolster your technical knowledge as programmer. Each course was selected to fit a specific area where we see a future business opportunity. Courses are self-guided, but our senior programmers are available to help explain individual concepts and applications.


Big Data and Analytics

Machine learning and AI, the study of pattern recognition and prediction within the field of computer science, depends on large data sets being available to train the machines. Digital assets and cloud aggregated business processes creates large amount of raw data. Big data analytics learning modules will teach you to uncover hidden patterns, correlations, trends, and more. We are offering this to show you how to master data science techniques, and in future join one of our teams.

  • Certificates of completion in SAS, R, Oracle & database courses
  • SAS courses, including SAS Dataset, SAS Format & SAS Functions training
  • R programing, including R Business Analytics, R-Studio and Anova training
  • Pinnacle data & analytics courses, including Hadoop, Cloud, Tableau, MongoDB & Informatica training
  • Oracle & other database courses, including Oracle SOA Suite, Oracle SQL, RMAN, Oracle Database and Toad training

Start Studying Machine Learning Techniques & Fundamentals of Python

Do you want to work on self-driving cars, speech recognition, or image recognition? These technologies depend on machine learning. This series of video lectures, taught by Stanford-educated, Silicon Valley experts that have decades of direct experience under their belts, will teach you the basics of machine learning and Python.

Python is one of Google's preferred languages, and an extremely in-demand skill sought by other companies working on AI. These modules start from square one of downloading and installing Python on your machine then progress into more advanced concepts that will leave you prepared to get coding completely on your own.

Python has a simple, highly-readable syntax which makes it perfect for beginners. From simple user scripts to web servers to complex APIs, Python is an incredibly powerful language. Whether you're a beginner or looking to refresh your Python skills, this course breaks basic concepts down into beginner-friendly, comprehendible lessons. You'll finish the training with three hands-on coding exercises so that you'll feel confident in your skills by completion.

  • Understand how to install Python on Windows, Mac & Linux
  • Understand the basic Python keywords, operators, statements & expressions
  • Get an in-depth understanding of object oriented programming & the Python API
  • Develop powerful Python applications w/ IntelliJ IDEA (a powerful environment)
  • Learn when to use Python 2 & when to use Python.
  • Get introduced to suggested text editors & integrated development environments
  • Work w/ data types including strings, lists, tuples, dictionaries, booleans & more
  • Study what variables are & when to use them
  • Perform mathematical operations
  • Capture input from a user & control the flow of your programs
  • Know the importance of white space
  • Study what modules are, when you should use them & how to create your own
  • Define & use functions (including important built-in functions)
  • Learn to install the Python 3 interpreter & run the interpreter in the command line
  • Execute Python source files
  • Use Python to do simple arithmetic
  • Learn to work w/ words & characters using a data type known as the "string"
  • Study the Boolean type, a representation of True & False
  • Start organizing data into a list
  • Create lists & access or edit elements inside them
  • Look at the dictionary type to create mappings
  • Study the control flow: if statement, looping, function

Deep Learning Prerequisites: Linear Regression in Python

Use Probability Theory to Make More Accurate Predictions

Deep Learning is a set of powerful algorithms that are the force behind self-driving cars, image searching, voice recognition, and many more applications. One of the central foundations of deep learning is linear regression; using probability theory to gain deeper insight into the "line of best fit." This is the first step to creating neural networks which can learn on their own. In this course, you'll start with the basics of building a linear regression module in Python, and progress into practical machine learning issues that will provide the foundations for an exploration of Deep Learning.

  • Use a 1-D linear regression to prove Moore's Law
  • Learn how to create a machine learning model that can learn from multiple inputs
  • Apply multi-dimensional linear regression to predict a patient's systolic blood pressure given their age & weight
  • Discuss generalization, overfitting, train-test splits, & other issues that may arise while performing data analysis

Deep Learning Prerequisites: Logistic Regression in Python

Introduce Yourself to the Building Blocks of Neural Networks

Logistic regression is one of the most fundamental techniques used in machine learning, data science, and statistics, as it may be used to create a classification or labeling algorithm that quite resembles a biological neuron. Logistic regression units, by extension, are the basic bricks in the neural network, the central architecture in deep learning. In this course, you'll come to terms with logistic regression using practical, real-world examples to fully appreciate the vast applications of Deep Learning.

  • Code your own logistic regression module in Python
  • Use Deep Learning for facial expression recognition
  • Understand how to make data-driven decisions

Data Science: Deep Learning in Python

Learn to Build the Kinds of Artificial Neural Networks That Make Google Seem to Know Everything

Artificial neural networks are the architecture that make Apple's Siri recognize your voice, Tesla's self-driving cars know where to turn, Google Translate learn new languages, and many more technological features. The data science that unites all of them is Deep Learning. In this course, you'll build your very first neural network, going beyond basic models to build networks that automatically learn features.

  • Extend the binary classification model to multiple classes using the softmax function
  • Code the important training method, backpropagation, in Numpy
  • Implement a neural network using Google's TensorFlow library
  • Predict user actions on a website given user data using a neural network
  • Use Deep Learning for facial expression recognition
  • Learn some of the newest development in neural networks

Data Science: Practical Deep Learning in Theano & TensorFlow

Build & Understand Neural Networks Using Two of the Most Popular Deep Learning Techniques

In this course, you will start with the basics of TensorFlow and Theano, understanding how to build neural networks with these popular tools. Using these tools, you'll learn how to build and understand a neural network, and how to visualize what is happening within a model as it learns. With this foundation you will then delve into advanced concepts of Deep Learning.

  • Discover batch & stochastic gradient descent, two techniques that allow you to train on a small sample of data at each iteration, greatly speeding up training time
  • Discuss how momentum can carry you through local minima
  • Learn adaptive learning rate techniques like AdaGrad & RMSprop
  • Explore dropout regularization & other modern neural network techniques
  • Understand the variables & expressions of TensorFlow & Theano
  • Set up a GPU-instance on AWS & compare the speed of CPU vs GPU for training a deep neural network
  • Look at the MNIST dataset & compare against known benchmarks