Foundation Courses
1. Programming Tools and Foundational Concepts
1. Linux for Data Science
Preview
2. Getting Started with Git
3. Python Foundations
Preview
4. Machine Learning Prerequisites(Including Numpy, Pandas and Linear Algebra)
5. Getting Started with SQL
6. Statistics Foundations
Course on Machine Learning
1. Machine Learning Applications & Landscape
1. Introduction to Machine Learning
2. Machine Learning Application
3. Introduction to AI
4. Types of Machine Learning algorithms - Supervised, Unsupervised
2. Building end-to-end Machine Learning Project
1. Machine Learning Projects Checklist
2. Get the data
3. Explore the data to gain insights
4. Prepare the data for Machine Learning algorithms
5. Explore many different models and short-list the best ones
6. Fine-tune model
7. Launch, monitor, and maintain the system
3. Training Models
1. Linear Regression
2. Gradient Descent
3. Model Evaluation and Metrics
4. Polynomial Regression
5. Overfitting and Underfitting
6. Regularized Linear Models
7. Logistic Regression
4. Classification
1. Training a Binary classifier
2. Multiclass, Multilabel and Multioutput Classification
3. Performance Metrics
4. Confusion Matrix
5. Precision and Recall
6. Precision/Recall Tradeoff
7. The ROC Curve
5. Support Vector Machines
1. Introduction to Support Vector Machines
2. SVM for Classification
3. SVM for Regression
4. HyperParameter Tuning
6. Decision Trees
1. Training and Visualizing a Decision Tree
2. Making Predictions
3. The CART Training Algorithm
4. HyperParameter Tuning
5. Handling overfitting
7. Ensemble Learning
1. Introduction to Ensemble Learning
2. Demonstrating Why Multiple Models Attain Superior Accuracy
3. Types of Ensemble Learning methods
8. Dimensionality Reduction
1. The Curse of Dimensionality
2. Main Approaches for Dimensionality Reduction
3. Principal Component Analysis (PCA)
Course on Deep Learning and Reinforcement Learning
1. Introduction to Artificial Neural Networks
1. From Biological to Artificial Neurons
2. Backpropogation from Scratch
3. Activation Functions
4. Implementing MLPs using Keras with TensorFlow Backend
5. Fine-Tuning Neural Network Hyperparameters
2. Convolutional Neural Networks and Computer Vision
1. The Architecture of the Visual Cortex
2. Convolutional Layer
3. Pooling Layer
4. CNN Architectures
5. Classification with Keras
6. Transfer Learning with Keras
7. Object Detection
8. YOLO
3. Stable Diffusion
1. Introduction to Stable Diffusion
2. Stable Diffusion Components
3. Diffusion Model
4. Stable Diffusion Architecture and Training
4. Recurrent Neural Networks
1. Recurrent Neurons and Layers
2. Basic RNNs in TensorFlow
3. Training RNNs
4. Deep RNNs
5. Forecasting a Time Series
6. LSTM
5. Natural Language Processing
1. Introduction to Natural Language Processing
2. Word Embeddings
3. Creating a Quiz Using TextBlob
4. Finding Related Posts with scikit-learn
5. Sentiment Analysis
6. Encoder-Decoder Network for Neural Machine Translation
6. Training Deep Neural Networks
1. The Vanishing / Exploding Gradients Problems
2. Reusing Pretrained Layers
3. Faster Optimizers
4. Avoiding Overfitting Through Regularization
5. Practical Guidelines to Train Deep Neural Networks
7. Custom Models and Training with Tensorflow
1. A Quick Tour of TensorFlow
2. Customizing Models and Training Algorithms
8. Autoencoders and GANs
1. Efficient Data Representations
2. Performing PCA with an Under Complete Linear Autoencoder
3. Stacked Autoencoders
4. Unsupervised Pre Training Using Stacked Autoencoders
5. Denoising Autoencoders
6. Sparse Autoencoders
7. Variational Autoencoders
8. Generative Adversarial Networks
9. Reinforcement Learning
1. Learning to Optimize Rewards
2. Policy Search
3. Introduction to OpenAI Gym
4. Neural Network Policies
5. Evaluating Actions: The Credit Assignment Problem
6. Policy Gradients
7. Markov Decision Processes
8. Temporal Difference Learning and Q-Learning
9. Deep Q-Learning Variants
10. The TF-Agents Library
Course on Large Language Models
1. Transformers
1. Attention Mechanism
2. Transformer Architecture and components
3. Transfer Learning
4. Transformer Variants
2. OpenAI's ChatGPT
1. Introduction to ChatGPT
2. Architecture of GPT
3. ChatGPT Architecture and Training
3. Vector Databases
1. Introduction to Vector Databases
2. Architecture of Vector Databases
3. Indexing Techniques
4. Distance Metrics and Similarity Measures
5. Nearest Neighbor Search
6. Open Source Vector Databases:- Chroma and Milvus
4. Langchain
1. Introduction to Langchain
2. The building blocks of LangChain:- Prompt, Chains, Retrievers, Parsers, Memory and Agents
5. Creating LLM powered apps with Langchain
1. Demonstration:- Building personalized chatbot using Langchain
Course on Large-Scale System Design (Data Engineering)
1. Introduction to Hadoop
1. Introduction
Preview
2. Distributed systems
Preview
3. Big Data Use Cases
4. Various Solutions
5. Overview of Hadoop Ecosystem
6. Spark Ecosystem Walkthrough
2. Foundation & Environment
1. Understanding the CloudxLab
Preview
2. Getting Started - Hands on
Preview
3. Hadoop & Spark Hands-on
4. Understanding Regular Expressions
5. Setting up VM
3. Zookeeper
1. ZooKeeper - Race Condition
Preview
2. ZooKeeper - Deadlock
Preview
3. How does election happen - Paxos Algorithm?
4. Use cases
5. When not to use
4. HDFS
1. Why HDFS?
Preview
2. NameNode & DataNodes
Preview
3. Advance HDFS Concepts (HA, Federation)
4. Hands-on with HDFS (Upload, Download, SetRep)
5. Data Locality (Rack Awareness)
5. YARN
1. Why YARN?
Preview
2. Evolution from MapReduce 1.0
Preview
3. Resource Management: YARN Architecture
4. Advance Concepts - Speculative Execution
6. MapReduce Basics
1. Understanding Sorting
Preview
2. MapReduce - Overview
Preview
3. Word Frequency Problem - Without MR
4. Only Mapper - Image Resizing
5. Temperature Problem
6. Multiple Reducer
7. Java MapReduce
7. MapReduce Advanced
1. Writing MapReduce Code Using Java
Preview
2. Apache Ant
Preview
3. Concept - Associative & Commutative
4. Combiner
5. Hadoop Streaming
6. Adv. Problem Solving - Anagrams
7. Adv. Problem Solving - Same DNA
8. Adv. Problem Solving - Similar DNA
9. Joins - Voting
10. Limitations of MapReduce
8. Analyzing Data with Pig
1. Pig - Introduction
Preview
2. Pig - Modes
Preview
3. Example - NYSE Stock Exchange
4. Concept - Lazy Evaluation
9. Processing Data with Hive
1. Hive - Introduction
Preview
2. Hive - Data Types
Preview
3. Loading Data in Hive (Tables)
4. Movielens Data Processing
5. Connecting Tableau and HiveServer 2
6. Connecting Microsoft Excel and HiveServer 2
7. Project: Sentiment Analyses of Twitter Data
8. Advanced - Partition Tables
9. Understanding HCatalog & Impal
10. NoSQL and HBase
1. NoSQL - Scaling Out / Up
Preview
2. ACID Properties and RDBMS Story
Preview
3. CAP Theorem
4. HBase Architecture - Region Servers etc
5. Hbase Data Model - Column Family Orientedness
6. Getting Started - Create table, Adding Data
7. Adv Example - Google Links Storage
8. Concept - Bloom Filter
9. Comparison of NOSQL Databases
11. Importing Data with Sqoop and Flume, Oozie
1. Sqoop - Introduction
Preview
2. Sqoop Import - MySQL to HDFS
Preview
3. Exporting to MySQL from HDFS
4. Concept - Unbounding Dataset Processing or Stream Processing
5. Flume Overview: Agents - Source, Sink, Channel
6. Data from Local network service into HDFS
7. Example - Extracting Twitter Data
8. Example - Creating workflow with Oozier
12. Introduction to Spark
1. Apache Spark ecosystem walkthrough
2. Spark Introduction - Why Spark?
Preview
13. Scala Basics
1. Introduction, Access Scala on CloudxLab
Preview
2. Variables and Methods
Preview
3. Interactive, Compilation, SBT
4. Types, Variables & Values
5. Functions
6. Collections
7. Classes
8. Parameters
14. Spark Basics
1. Apache Spark ecosystem
Preview
2. Why Spark?
Preview
3. Using the Spark Shell on CloudxLab
4. Example 1 - Performing Word Count
5. Understanding Spark Cluster Modes on YARN
6. RDDs (Resilient Distributed Datasets)
7. General RDD Operations: Transformations & Actions
8. RDD lineage
9. RDD Persistence Overview
10. Distributed Persistence
15. Writing and Deploying Spark Applications
1. Creating the SparkContext
2. Building a Spark Application (Scala, Java, Python)
3. The Spark Application Web UI
4. Configuring Spark Properties
5. Running Spark on Cluster
6. RDD Partitions
7. Executing Parallel Operations
8. Stages and Tasks
16. Common Patterns in Spark Data Processing
1. Common Spark Use Cases
1. Example 1 - Data Cleaning (Movielens)
1. Example 2 - Understanding Spark Streaming
2. Understanding Kafka
3. Example 3 - Spark Streaming from Kafka
4. Iterative Algorithms in Spark
5. Project: Real-time analytics of orders in an e-commerce company
17. Data Formats & Management
1. XML
2. AVRO
3. How to store many small files - SequenceFile?
4. Parquet
5. Protocol Buffers
6. Comparing Compressions
7. Understanding Row Oriented and Column Oriented Formats - RCFile?
18. DataFrames and Spark SQL
1. Spark SQL - Introduction
Preview
2. Spark SQL - Dataframe Introduction
Preview
3. Transforming and Querying DataFrames
4. Saving DataFrames
5. DataFrames and RDDs
6. Comparing Spark SQL, Impala, and Hive-on-Spark
19. Machine Learning with Spark
1. Machine Learning Introduction
Preview
2. Applications Of Machine Learning
Preview
3. MlLib Example: k-means
4. SparkR Example
