Artificial Intelligence & Machine Learning

This course focuses on the practical aspects of Machine Learning, Deep Learning and Artificial Intelligence. The objective is to make use of TensorFlow for various types of neural networks. The participants will build and train deep learning models.

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ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

COURSE OUTLINE

ARTIFICIAL INTELLIGENCE & MACHINE LEARNING

• Artificial Intelligence (AI) Overview
• What is Machine Learning (ML)?
• AI vs ML vs Data Science
• Relationship between Deep Learning (DL) and Machine Learning
• Practical Use cases
• Concepts and Terms
• Tools/Platforms for ML, DL and AI
• Machine Learning Project End to End Pipeline
• Scalable ML/AI: Big Data and Cloud fits into the Ecosystem 

• GCP Introduction.
• Why Google Cloud Platform (GCP)?
• How Innovations at Google driving Data Engineering and Science globally?
• Key Google Products related to Data and Machine Learning
• Come on same page w.r.t. terms and concepts 

• Introduction to Serverless Architecture
• Current Challenges with On-Premise Architectures
• How Google enables higher productivity?
• How Key Google Products fit in Enterprise Architecture?
• How to design modern Data Analytics Pipeline on GCP? 

• Introduction to Machine Learning APIs
• Key ML Use Cases
• Vision API
• Natural Language API
• Translate API
• Speech API 

• Installing Anaconda
• Setting up Jupyter Notebook
• Experiencing Notebooks
• Key Python Syntax Recap
• Hands-on Exercises 

• Summary Statistics
• Exploratory Data Analysis
• Numerical Computation using Python
• Hands-on Exercises

• Overview
• Using MatPlot Lib
• Working with Seaborn
• Key types of plots
• Exploratory Analysis using Seaborn
• Hands-on Exercises 

• Content Acquisition Overview
• Working with Beautiful Soup
• Acquiring data using Rest Based APIs
• Data Cleaning & Wrangling using Pandas
• Missing Values and Outlier
• Cleansing Twitter Data
• Performing Twitter Sentiment Analysis
• Hands-on Exercises 

• What is Feature Engineering?
• Why Feature Engineering?
• How to apply Feature Engineering?
• Discussions on various scenarios
• Hands-on Exercises 

• Types of Machine Learning
• Key Algorithms in Machine Learning
• Practical Applications of Machine Learning
• Various frameworks/Libraries popular for ML
• Concepts and Terms
• Why Scikit Learn?
• Code Walkthrough
• Hands-on Exercises 

• Key Classification Algorithms
• Naïve Bayes Classifier
• Confusion Matrix
• Accuracy
• Key Regression Algorithms
• Linear, Logistic Regressions
• Gradient Descent
• Loss function
• Bias vs Variance Tradeoff
• Evaluating Models
• Hands-on Exercises 

• Why Un-supervised learning is important?
• Where it can be applied?
• Principal Component Analysis
• Performing Clustering of data
• Hands-on Exercises 

• Overview
• Understand What is Spark?
• Why Spark?
• Languages used in Spark Programming
• Logical Architecture
• Physical Architecture
• Pros & Cons of using Python, Java & Scala
• Key Terms & Concepts
• Ways to create RDDs
• Operations on RDD
• Pair RDD
• Key Spark Components
• Working with various types of data
• Hands-on exercises 

• Introduction
• Using the SQL API – sqlContext.sql
• Dataframe API
• Working with various Datasources
• Inferred Schemas
• Querying DataFrames Using Column Expressions
• Data formats
• Hands-on Exercises 

• Quick introduction to Machine Learning
• Introduction to ML and MLLib Spark
• Data types - Vectors, Matrices, LabeledPoint
• Summary Statistics
• Calculating Correlations
• Transformers, Estimators and Pipelines
• Evaluation of Model
• Walkthrough of a Regression model
• Walkthrough of a K Means Clustering model
• Hands-on on Working with Machine Learning Pipelines 

• What is Deep Learning?
• Relationship between Deep Learning and Machine Learning
• Deep Learning Use cases
• Concepts and Terms
• How to implement Deep Learning?
• Various Libraries, Pros & Cons
• Hands-on: Recap on Machine Learning 

• Introduction to Neural Networks
• Introduction to Perceptron
• Neural Network Activation Functions
• Basic Neural Nets
• Concepts 

• What is TensorFlow?
• Installing TensorFlow
• TensorFlow Graphs
• Variables and Placeholders
• Activation Functions
• Hands-on exercises 

• CNN History
• Understanding CNNs
• CNN Application
• Hands-on exercises 

• Spinning up Cloud DataLab
• Experiencing Datalab notebooks
• Exploring Google Cloud Storage
• Leveraging relational data with Google Cloud SQL
• Reading and writing streaming Data with Google BigTable
• Querying Data from Google BigQuery
• Making Google API Calls from notebooks 

• Why Cloud ML?
• Running TensorFlow model in Local mode
• Porting TensorFlow models to GCP
• Deploying Models in Production
• Model Predictions
• Hands-on exercise(s) 

• Intro to ChatBots
• Key options available
• Building ChatBot
• Hands-on Exercise using DialogFlow API 

Anything to ask?

If you have any question about training, fees, courses or anything else, feel free to ask us anytime!


Address

Nexperts Academy Sdn Bhd,
Unit 313, Block E, Phileo Damansara 1, Jalan 16/11 off Jalan Damansara 46350, PJ Selangor, Malaysia

Working Hours

Monday Tuesday Wednesday Thursday Friday

09:00 - 17:30 09:00 - 17:30 09:00 - 17:30 09:00 - 17:30 09:00 - 17:30

Contact

Email: vaheed@nexpertsacademy.com
Phone: +6 011 1221 6872
Office: +6 03 7931 8872