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Machine Learning Bootcamps

From Zero to Deep Learning

Artificial Intelligence and Machine Learning Bootcamp

Duration: 5 Days

An intense  training that brings you from zero to deep learning in a week. This course is a head first introduction to modern machine learning and deep learning techniques using Python, Tensorflow, Scikit-learn, and Keras.

Audience:  Designed for software engineers, data analysts or anyone looking for an intense introduction to Machine Learning.

Core Technologies and Concepts: Tensorflow, Python, Scikit-learn Machine Learning, Deep Learning, Data Science

COURSE OUTLINE

Review of Linear Algebra and Statistics
  • Tensors/Matrix Operations
  • Samples and Populations
  • Distribution Curves
  • Regression
  • Statistics Primer
Machine Learning Landscape
  • History
  • What is Machine Learning
  • Real World Applications
  • Challenges
  • Delivering Value to Business
  • Descriptive vs Prescriptive Analytics
Data Preparation
  • Importance of Data
  • Data Wrangling
  • Data Cleaning
  • Data Normalization
  • Data Understanding
Model Constrution
  • Building Feature Tensors
  • Weights
  • Activation Functions
  • Estimators
  • Hyper Parameters
  • Models Overview
  • Experiments
Machine Learning Applications
  • Predictive vs Prescriptive Analytics
  • Time Series Analysis
  • Recommendation Engines
  • Image Processing Use Cases
  • Beyong Devops
  • Natural Language Processing
  • Network and Graph Analysis
Linear Regression
  • Linear Regression
  • Polynomial Regression
  • Regularization
  • Performance Metrics
Classification
  • Classification vs Regression
  • KNN
  • Logistiv Regression
  • Support Vector Machines
  • Decision Trees
  • Ensemble Methods/Random Forest
Model Evaluation
  • Training vs Validation
  • Biad Variance Trade-off
  • Evaluation and Metrics
  • Accuracy
  • Precision
  • F1 Score
  • AUC and ROC
  • Disappearing Gradient
Data Visualization
  • Loss Curve
  • Curse of Dimensionality
  • Clustering
  • TSNE
  • TensorBoard
Neural Networks Overview
  • What is Neural Network
  • Deep Learning
  • Tensors
  • Multi Layer Perceptrons
  • Regression
Neural Networks Architecture
  • Inputs
  • Output Layer
  • Layers
  • Activation Functions
Training Neural Networks
  • Back Propagation
  • Gradient Descent
  • Hyper Parameter Optimization
  • Vanishing/Exploding Gradient
  • Overfitting
Deep Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • LSTM
  • NLP Models
  • Sequence to Sequence Models

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