Master In Artificial Intelligence

AI Programming with Python Nanodegree

Click here for Detail Curriculum

Part 01 : Introduction to Python

Start coding with Python, drawing upon libraries and automation scripts to solve complex problems quickly.

Part 02 : SQL For Data Analysis

Welcome to SQL For Data Analysis

Part 03 : Data Visualization in Python

Welcome to Visualize the data in Python.

Part 04 : Command Line Essentials

Welcome to Command Line Essentials.

Part 05 : Git & Github

Welcome to Git & Github.

Part 06 : Practical Statistics

Welcome to Practical Statistics.

Part 07 : Numpy, Pandas, Matplotlib

Let's focus on library packages for Python, such as : Numpy (which adds support for large data), Pandas (which is used for data manipulation and analysis) And Matplotlib (which is used for data visualization).

Part 08 : Linear Algebra Essentials

Learn the basics of the beautiful world of Linear Algebra and why it is such an important mathematical tool in the world of AI.

Part 09 : Calculus Essentials

Welcome to Calculus Essentials.

Part 10 : Supervised Learning

Learn to build supervised machine learning models to make data-informed decisions. Learn to evaluate and validate the quality of your models.

Part 11 : Deep Learning

Gain a solid foundation in neural networks, deep learning, and PyTorch.

Part 12 : Unsupervised Learning

Learn to build unsupervised machine learning models, and use essential data processing techniques like scaling and PCA.

Part 13 : Convolutional Neural Networks

Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image de-noising.

Part 14 : Recurrent Neural Networks

Build your own recurrent networks and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts.

Part 15 : Generative Adversarial Networks

Learn to understand and implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs.

Part 16 :  Deploying a Model

Train and deploy your own sentiment analysis model using Amazon's SageMaker. Deployment gives you the ability to use a trained model to analyze new, user input. Build a model, deploy it, and create a gateway for accessing it from a website.

Part 17 : Machine Learning, Case Studies

Welcome to learn the Machine Learning, Case Studies.

Part 18 : Software Engineering

Software engineering skills are increasingly important for data scientists. In this course, you'll learn best practices for writing software. Then you'll work on your software skills by coding a Python package and a web data dashboard.

Part 19 : Data Engineering

In data engineering for data scientists, you will practice building ETL, NLP, and machine learning pipelines.

Part 20 : Experimental Design & Recommendations

Learn to design experiments and analyze A/B test results. Explore approaches for building recommendation systems.

Part 21 : Data Modeling

Learn to create relational and NoSQL data models to fit the diverse needs of data consumers. Use ETL to build databases in PostgreSQL and Apache Cassandra.

Part 22 : Cloud Data Warehouses

Welcome to learn the Cloud Data Warehouses.

Part 23 : Data Lakes with Spark

Welcome to learn the Data Lakes with Spark.

Part 24 : Data Pipelines with Airflow

Welcome to learn the Data Pipelines with Airflow.

Part 25 : C++ Programming

Welcome to learn the beauty of C++ Programming.

Part 26 : Computer Vision

Welcome to learn the beauty of Computer Vision.

Part 27 : Cloud Computing

Learn how to build and train your data in Cloud Computing

Part 28 : Advanced Computer Vision & Deep Learning

Learn the Advanced Computer Vision & Deep Learning.

Part 29 : Object Tracking and Localization

Welcome to Object Tracking and Localization.

Part 30 : Natural Language Processing

This section provides an overview of the program and introduces the fundamentals of Natural Language Processing through symbolic manipulation, including text cleaning, normalization, and tokenization. You'll then build a part of speech tagger using hidden Markov models.

Part 31 : Computing with Natural Language

Learn how to Computing with Natural Language.

Part 32 : Communicating with Natural Language

Learn how to Communicating with Natural Language.

Part 33 : Introduction to Deep Reinforcement Learning

Welcome to Learn the beauty of Deep Reinforcement Learning.

Part 34 : Value-Based Methods

Learn how Value-Based Methods works.

Part 35 : Policy-Based Methods

Learn how Policy-Based Methods works.

Part 36 : Introduction to Artificial Intelligence

Welcome to the Introduction to Artificial Intelligence.

Part 37 : Constraint Satisfaction Problems

Take a deep dive into the constraint satisfaction problem framework and further explore constraint propagation, backtracking search, and other CSP techniques. Complete a classroom exercise using a powerful CSP solver on a variety of problems to gain experience framing new problems as CSPs.

Part 38 : Classical Search

Learn classical graph search algorithms--including uninformed search techniques like breadth-first and depth-first search and informed search with heuristics including A*. These algorithms are at the heart of many classical AI techniques, and have been used for planning, optimization, problem solving, and more. Complete the lesson by teaching PacMan to search with these techniques to solve increasingly complex domains.

Part 39 : Automated Planning

Learn to represent general problem domains with symbolic logic and use search to find optimal plans for achieving your agent’s goals. Planning & scheduling systems power modern automation & logistics operations, and aerospace applications like the Hubble telescope & NASA Mars rovers.

Part 40 : Optimization Problems

Learn about iterative improvement optimization problems and classical algorithms emphasizing gradient-free methods for solving them. These techniques can often be used on intractable problems to find solutions that are "good enough" for practical purposes, and have been used extensively in fields like Operations Research & logistics. Finish the lesson by completing a classroom exercise comparing the different algorithms' performance on a variety of problems.

Part 41 : Adversarial Search -

Learn how to search in multi-agent environments (including decision making in competitive environments) using the minimax theorem from game theory. Then build an agent that can play games better than any human.

Part 42 : Probabilistic Models

Learn to use Bayes Nets to represent complex probability distributions, and algorithms for sampling from those distributions. Then learn the algorithms used to train, predict, and evaluate Hidden Markov Models for pattern recognition. HMMs have been used for gesture recognition in computer vision, gene sequence identification in bioinformatics, speech generation & part of speech tagging in natural language processing, and more.

Part 43 : Intro to Self-Driving Cars

Welcome to Learn How Self-Driving Cars works.

Part 44 : Bayesian Thinking

Learn the framework that underlies a self-driving car’s understanding of itself and the world around it, and to see the world the way a self-driving car does.

Part 45 : Working with Matrices

This course will focus on two tools which are vital to self-driving car engineers: object oriented programming and linear algebra.

Part 46 : C++ Basics

This course is the first step in a rewarding journey towards C++ expertise. The goal is translation: get a program written in Python, and translate it into C++.

Part 47 : Performance Programming in C++

Explore how to write good code that runs correctly. We’ll focus primarily on low level features of C++, but we’ll discuss other best practices as well.

Part 48 : Navigating Data Structures

Algorithmic thinking is a skill you’ll refine throughout your career. In this course you’ll focus on frequently used data structures and algorithms.

Part 49 : Vehicle Motion and Control

This course is a crash course in two branches of mathematics which are crucial to self driving cars: calculus and trigonometry. You will learn how a self driving car uses various motion sensors to help it understand its own motion. At the end of this course you will use raw sensor data (which give information about distance driven, acceleration, and rotation rates) to reconstruct a vehicle's trajectory through space.

Part 50 : Computer Vision, Deep Learning, and Sensor Fusion

Here, you'll first become an expert in applying Computer Vision and Deep Learning on automotive problems. You will teach the car to detect lane lines, predict steering angle, and more all based on just camera data, along with working with lidar and radar data later on.

Part 51 : Localization, Path Planning, Control, and System Integration

Here, you'll expand on your sensor knowledge to localize and control the vehicle. You'll evaluate sensor data from camera, radar, lidar, and GPS, and use these in closed-loop controllers that actuate the vehicle, finishing by combining all your skills on a real self-driving car!.

Part 52 : Unscented Kalman Filters, Model Predictive Control, Advanced Deep Learning / Semantic Segmentation, and Functional Safety here

Here you'll Learn few topics like Unscented Kalman Filters, Model Predictive Control, Advanced Deep Learning / Semantic Segmentation, and Functional Safety here.

Part 53 : Functional Safety

learn to make safer vehicles using risk evaluation and systems engineering.

Part 54 : Technical Interview Prep

Learn the skills technical interviewers expect you to know—efficiency, common algorithms, manipulating popular data structures, and how to explain a solution.

Part 55 : Introduction to Autonomous Flight

In this course, you will get an introduction to flight history, challenges, and vehicles. You will learn about our quadrotor test platform, work in our custom simulator, and build your first project—getting a quadrotor to take-off and fly around a backyard!.

Part 56 : Planning

Flying robots must traverse complex, dynamic environments. Wind, obstacles, unreliable sensor data, and other randomness all present significant challenges. In this course, you will learn the fundamentals of aerial path planning. You will begin with 2D problems, optimize your solutions using waypoints, and then scale your solutions to three dimensions. You will apply these skills in your second project—autonomously navigating your drone through a dense urban environment.

Part 57 : Controls

In the previous course, we implemented 3D path planning but assumed a solution for actually following paths. In reality, moving a flying vehicle requires determining appropriate low-level motor controls. In this course, you will build a nonlinear cascaded controller and incorporate it into your software in the project.

Part 58 : Estimation

In this course, we will finish peeling back the layers of your autonomous flight solution. Instead of assuming perfect sensor readings, you will utilize sensor fusion and filtering. You will design an Extended Kalman Filter (EKF) to estimate attitude and position from IMU and GPS data of a flying robot.

Part 59 : Fixed Wing

While quadrotors are the ideal test platform for aerial robotics, flying cars and other long-range aircrafts leverage the aerodynamic efficiencies of fixed-wing flight. In this course, you will learn how to adapt the concepts you’ve learned so far and successfully fly a fixed-wing aircraft in simulation.