Ontrak Onstak Certified- Machine Learning/Deep Learning
Overview
This path is specifically designed for tech-savvy individuals seeking interest in the field of Machine Learning and Artificial Intelligence. The path is also recommended for technical individuals looking for exposure in the field of machine Learning and Artificial Intelligence.
This path will give an edge in the learning process to students and individuals who wish to understand and prepare for ML/AI exam.
The data scientists are responsible for machine learning and getting outputs but the business people are the ones who are going to use it for business purpose so the rules and insights extracted from machine learning should be interpretable.
Module 1: Introduction to Programming on Python Timeline: 4 weeks Skill Level: Beginner
PREREQUISITES AND REQUIREMENTS:
- Basic understanding of use of computers
- Previous experience with any programming is a plus
What you will learn:
- Programming Fundamentals
- Environment Setup
- JupyterOverview
- Python Arithmetic Operators
- Python Data Types
- Python Logical/Comparison/Relational Operators
- Python IDE’s
- Conditional Statements
- Loops
- Functions
- Lists, Tuples and Dictionaries
Module 2: Python for Data Science Timeline: 8 weeks Skill Level: Intermediate
PREREQUISITES AND REQUIREMENTS
- Previous experience with any programming language is expected.
- Understanding of concepts like loops, if/else and variable is required.
What you will learn:
- Data Analysis using NumPy
- Data analysis using Pandas
- Data Visualization using Matplotlib
- Data Visualization using Seaborn
- Data Visualization using Pandas
- Data Visualization using Plotlyand Cufflinks
- Geo Plotting and Analysis
- Data Analysis Pipeline
- Project Work
Module 3: Introduction to Machine Fundamentals Timeline: 8 weeks Skill Level: Intermediate
PREREQUISITES AND REQUIREMENTS
- Python* programming
- Calculus
- Linear algebra
- Statistics
What you will learn:
- Introduction to Machine Learning
- Introduction to Supervised Learning
- Regression and Classification problems
- KNN algorithm for classification
- Cross Validation and Bias-Variances tradeoffs
- The difference between over-fitting and under-fitting a model
- Decision Trees and Random Forests
- Support Vector Machines
- Recommender Systems
Module 4: Introduction to Deep Learning Fundamentals Timeline: 8 weeks Skill Level: Advanced
PREREQUISITES AND REQUIREMENTS
- Python* programming
- Calculus
- Linear algebra
- Statistics
- Introduction to Machine Learning
What you will learn:
- Introduction to Deep Learning
- Introduction to TensorFlow
- Learn how to implement a basic gradient descent in TensorFlow
- Implement Single Layer Perceptron
- Implement Multi Layer Perceptron
- Understand and implement Backpropagation
- MNIST with Multi-Layer Perceptron
- TensorFlow with ContribLearn
- Natural Language Processing
Module 5: Bigdata Analytics using Spark Timeline: 8 weeks Skill Level: Advanced
PREREQUISITES AND REQUIREMENTS
- Python* programming
- Introduction to Deep Learning
What you will learn:
- Introduction to BigData
- Introduction to SPARK
- Local Spark setup
- AWS Account Set-Up
- Azure Account Set-Up
- SSH with Mac or Linux
- PySparkSetup
- RDDs in Spark
- Implementation of Spark Applications
- Spark Machine Learning and Streaming
- Deep Learning with Spark