Python Deep Learning Online Course

Python Deep Learning Online Course

This comprehensive course will teach you how to harness the power of Python to build intelligent systems that learn from data—just like humans—and make accurate predictions. You'll also master essential data preprocessing techniques to prepare datasets effectively for machine learning algorithms.

By the end of this course, you'll have a solid understanding of deep neural networks (DNNs) and be able to implement deep learning models using real-world datasets.

Who Should Take This Course?

This course is ideal for anyone passionate about data science or looking to advance their career in this growing field. It's especially beneficial for students and professionals aiming to specialize in data science, machine learning, or artificial intelligence.

What You’ll Learn:

• The fundamentals of machine learning and neural networks
• Neural network architecture and design
• The basics of deep neural networks (DNNs)
• How to build a complete DNN using NumPy
• Step-by-step guidance to create a DNN from scratch with Python
• Hands-on experience through practical projects

Course Curriculum

Introduction

• Course Promo
• Introduction to Instructor
• Introduction to Course

Basics of Deep Learning

• Problem to Solve Part 1
• Problem to Solve Part 2
• Problem to Solve Part 3
• Linear Equation
• Linear Equation Vectorized
• 3D Feature Space
• N-Dimensional Space
• Theory of Perceptron
• Implementing Basic Perceptron
• Logical Gates for Perceptrons
• Perceptron Training Part 1
• Perceptron Training Part 2
• Learning Rate
• Perceptron Training Part 3
• Perceptron Algorithm
• Coding Perceptron Algo (Data Reading and Visualization)
• Coding Perceptron Algo (Perceptron Step)
• Coding Perceptron Algo (Training Perceptron)
• Coding Perceptron Algo (Visualizing the Results)
• Problem with Linear Solutions
• Solution to Problem
• Error Functions
• Discrete Versus Continuous Error Function
• Sigmoid Function
• Multi-Class Problem
• Problem of Negative Scores
• Need of SoftMax
• Coding SoftMax
• One-Hot Encoding
• Maximum Likelihood Part 1
• Maximum Likelihood Part 2
• Cross Entropy
• Cross Entropy Formulation
• Multi-Class Cross Entropy
• Cross Entropy Implementation
• Sigmoid Function Implementation
• Output Function Implementation

Deep Learning

• Introduction to Gradient Descent
• Convex Functions
• Use of Derivatives
• How Gradient Descent Works
• Gradient Step
• Logistic Regression Algorithm
• Data Visualization and Reading
• Updating Weights in Python
• Implementing Logistic Regression
• Visualization and Results
• Gradient Descent Versus Perceptron
• Linear to Non-Linear Boundaries
• Combining Probabilities
• Weighted Sums
• Neural Network Architecture
• Layers and DEEP Networks
• Multi-Class Classification
• Basics of Feed Forward
• Feed Forward for DEEP Net
• Deep Learning Algo Overview
• Basics of Backpropagation
• Updating Weights
• Chain Rule for Backpropagation
• Sigma Prime
• Data Analysis NN (Neural Networks) Implementation
• One-Hot Encoding (NN Implementation)
• Scaling the Data (NN Implementation)
• Splitting the Data (NN Implementation)
• Helper Functions (NN Implementation)
• Training (NN Implementation)
• Testing (NN Implementation)

Optimizations

• Underfitting vs Overfitting
• Early Stopping
• Quiz
• Solution and Regularization
• L1 and L2 Regularization
• Dropout
• Local Minima Problem
• Random Restart Solution
• Vanishing Gradient Problem
• Other Activation Functions

Final Project

• Final Project Part 1
• Final Project Part 2
• Final Project Part 3
• Final Project Part 4
• Final Project Part 5