# Courses and Training

## IBM AI Engineering Professional Certificate

MAR 2021IBMDescribe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction

Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn.

Deploy machine learning algorithms and pipelines on Apache Spark

- Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow

## The Complete 2021 Web Development Bootcamp

MAR 2021 - OngoingAngela YuContent :

Front-End Web Development

HTML 5

CSS 3

Bootstrap 4

Javascript ES6

DOM Manipulation

jQuery

Bash Command Line

Git, GitHub and Version Control

Backend Web Development

Node.js

NPM

Express.js

EJS

REST

APIs

Databases

SQL

MongoDB

Mongoose

Authentication

Firebase

React.js

React Hooks

Web Design

Deployment with GitHub Pages, Heroku and MongoDB Atlas

## Building Deep Learning models with TensorFlow

APR 2020IBMexplain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.

describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.

understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

## Introduction to Deep Learning and Neural Networks with Keras

FEB 2020IBMContents :

Describe what a neural network is, what a deep learning model is, and the difference between them.

Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.

Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.

Build deep learning models and networks using the Keras library.

## Machine Learning with Python

JAN 2020IBMContents :

Regression, classification, clustering, sci-kit learn and SciPy

Projects including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.

## Scalable Machine Learning on Big Data using Apache Spark

JAN 2020IBMgain a practical understanding of Apache Spark, and apply it to solve machine learning problems involving both small and big data.

Understand how parallel code is written, capable of running on thousands of CPUs.

make use of large scale compute clusters to apply machine learning algorithms on Petabytes of data using Apache SparkML Pipelines.

eliminate out-of-memory errors generated by traditional machine learning frameworks when data doesn’t fit in a computer's main memory

test thousands of different ML models in parallel to find the best performing one – a technique used by many successful Kagglers

Run SQL statements on very large data sets using Apache SparkSQL and the Apache Spark DataFrame API.

## Machine Learning

JAN 2020Stanford University Contents:

Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).

Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).

Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).