freeCodeCamp.org

Learn Calculus 1 in this full college course.

This course was created by Dr. Linda Green, a lecturer at the University of North Carolina at Chapel Hill. Check out her YouTube channel: https://www.youtube.com/channel/UCkyLJh6hQS1TlhUZxOMjTFw

Lecture Notes

Course Contents:

• (0:00:00) [Corequisite] Rational Expressions
• (0:09:40) [Corequisite] Difference Quotient
• (0:18:20) Graphs and Limits
• (0:25:51) When Limits Fail to Exist
• (0:31:28) Limit Laws
• (0:37:07) The Squeeze Theorem
• (0:42:55) Limits using Algebraic Tricks
• (0:56:04) When the Limit of the Denominator is 0
• (1:08:40) [Corequisite] Lines: Graphs and Equations
• (1:17:09) [Corequisite] Rational Functions and Graphs
• (1:30:35) Limits at Infinity and Graphs
• (1:37:31) Limits at Infinity and Algebraic Tricks
• (1:45:34) Continuity at a Point
• (1:53:21) Continuity on Intervals
• (1:59:43) Intermediate Value Theorem
• (2:03:37) [Corequisite] Right Angle Trigonometry
• (2:11:13) [Corequisite] Sine and Cosine of Special Angles
• (2:19:16) [Corequisite] Unit Circle Definition of Sine and Cosine
• (2:24:46) [Corequisite] Properties of Trig Functions
• (2:35:25) [Corequisite] Graphs of Sine and Cosine
• (2:41:57) [Corequisite] Graphs of Sinusoidal Functions
• (2:52:10) [Corequisite] Graphs of Tan, Sec, Cot, Csc
• (3:01:03) [Corequisite] Solving Basic Trig Equations
• (3:08:14) Derivatives and Tangent Lines
• (3:22:55) Computing Derivatives from the Definition
• (3:34:02) Interpreting Derivatives
• (3:42:33) Derivatives as Functions and Graphs of Derivatives
• (3:56:25) Proof that Differentiable Functions are Continuous
• (4:01:09) Power Rule and Other Rules for Derivatives
• (4:07:42) [Corequisite] Trig Identities
• (4:15:14) [Corequisite] Pythagorean Identities
• (4:20:35) [Corequisite] Angle Sum and Difference Formulas
• (4:28:31) [Corequisite] Double Angle Formulas
• (4:36:01) Higher Order Derivatives and Notation
• (4:39:22) Derivative of e^x
• (4:46:52) Proof of the Power Rule and Other Derivative Rules
• (4:56:31) Product Rule and Quotient Rule
• (5:02:09) Proof of Product Rule and Quotient Rule
• (5:10:40) Special Trigonometric Limits
• (5:17:31) [Corequisite] Composition of Functions
• (5:29:54) [Corequisite] Solving Rational Equations
• (5:40:02) Derivatives of Trig Functions
• (5:46:23) Proof of Trigonometric Limits and Derivatives
• (5:54:38) Rectilinear Motion
• (6:11:41) Marginal Cost
• (6:16:51) [Corequisite] Logarithms: Introduction
• (6:25:32) [Corequisite] Log Functions and Their Graphs
• (6:36:17) [Corequisite] Combining Logs and Exponents
• (6:40:55) [Corequisite] Log Rules
• (6:49:27) The Chain Rule
• (6:58:44) More Chain Rule Examples and Justification
• (7:07:43) Justification of the Chain Rule
• (7:10:00) Implicit Differentiation
• (7:20:28) Derivatives of Exponential Functions
• (7:25:38) Derivatives of Log Functions
• (7:29:38) Logarithmic Differentiation
• (7:37:08) [Corequisite] Inverse Functions
• (7:51:22) Inverse Trig Functions
• (8:00:56) Derivatives of Inverse Trigonometric Functions
• (8:12:11) Related Rates – Distances
• (8:17:55) Related Rates – Volume and Flow
• (8:22:21) Related Rates – Angle and Rotation
• (8:28:20) [Corequisite] Solving Right Triangles
• (8:34:54) Maximums and Minimums
• (8:46:18) First Derivative Test and Second Derivative Test
• (8:51:37) Extreme Value Examples
• (9:01:33) Mean Value Theorem
• (9:09:09) Proof of Mean Value Theorem
• (0:14:59) [Corequisite] Solving Right Triangles
• (9:25:20) Derivatives and the Shape of the Graph
• (9:33:31) Linear Approximation
• (9:48:28) The Differential
• (9:59:11) L’Hospital’s Rule
• (10:06:27) L’Hospital’s Rule on Other Indeterminate Forms
• (10:16:13) Newtons Method
• (10:27:45) Antiderivatives
• (10:33:24) Finding Antiderivatives Using Initial Conditions
• (10:41:59) Any Two Antiderivatives Differ by a Constant
• (10:45:19) Summation Notation
• (10:49:12) Approximating Area
• (11:04:22) The Fundamental Theorem of Calculus, Part 1
• (11:15:02) The Fundamental Theorem of Calculus, Part 2
• (11:22:17) Proof of the Fundamental Theorem of Calculus
• (11:29:18) The Substitution Method
• (11:38:07) Why U-Substitution Works
• (11:40:23) Average Value of a Function
• (11:47:57) Proof of the Mean Value Theorem for Integrals

Learn all about Data Structures in this lecture-style course from freeCodeCamp.org

You will learn what Data Structures are, how we measure a Data Structures efficiency, and then hop into talking about 12 of the most common Data Structures which will come up throughout your Computer Science journey.

Course Contents

• (00:00) Introduction
• (01:06) Timestamps
• (01:23) Script and Visuals
• (01:34) References + Research
• (01:56) Questions
• (02:12) Shameless Plug
• (02:51) What are Data Structures?
• (04:36) Series Overview
• (06:55) Measuring Efficiency with BigO Notation
• (09:45) Time Complexity Equations
• (11:13) The Meaning of BigO
• (12:42) Why BigO?
• (13:18) Quick Recap
• (14:27) Types of Time Complexity Equations
• (19:42) Final Note on Time Complexity Equations
• (20:21) The Array
• (20:58) Array Basics
• (22:09) Array Names
• (22:59) Parallel Arrays
• (23:59) Array Types
• (24:30) Array Size
• (25:45) Creating Arrays
• (26:11) Populate-First Arrays
• (28:09) Populate-Later Arrays
• (30:22) Numerical Indexes
• (31:57) Replacing information in an Array
• (32:42) 2-Dimensional Arrays
• (35:01) Arrays as a Data Structure
• (42:21) Pros and Cons
• (43:33) The ArrayList
• (44:42) Structure of the ArrayList
• (45:19) Initializing an ArrayList
• (47:34) ArrayList Functionality
• (49:30) ArrayList Methods
• (53:57) Remove Method
• (55:33) Get Method
• (55:59) Set Method
• (56:57) Clear Method
• (57:30) toArray Method
• (59:00) ArrayList as a Data Structure
• (1:03:12) Comparing and Contrasting with Arrays
• (1:05:02) The Stack
• (1:05:06) The Different types of Data Structures
• (1:05:51) Random Access Data Structures
• (1:06:10) Sequential Access Data Structures
• (1:07:36) Stack Basics
• (1:09:01) Common Stack Methods
• (1:09:45) Push Method
• (1:10:32) Pop Method
• (1:11:46) Peek Method
• (1:12:27) Contains Method
• (1:13:23) Time Complexity Equations
• (1:15:28) Uses for Stacks
• (1:18:01) The Queue
• (1:18:51) Queue Basics
• (1:20:44) Common Queue Methods
• (1:21:13) Enqueue Method
• (1:22:20) Dequeue Method
• (1:23:08) Peek Method
• (1:24:15) Contains Method
• (1:25:05) Time Complexity Equations
• (1:27:05) Common Queue Uses
• (1:33:55) Adding and Removing Information
• (1:41:28) Time Complexity Equations
• (1:48:44) Visualization
• (1:50:56) Adding and Removing Information
• (1:58:30) Time Complexity Equations
• (1:59:06) Uses of a Doubly-LinkedList
• (2:00:21) The Dictionary
• (2:01:15) Dictionary Basics
• (2:02:00) Indexing Dictionaries
• (2:02:40) Dictionary Properties
• (2:05:53) Hash Table Mini-Lesson
• (2:13:26) Time Complexity Equations
• (2:16:39) Trees
• (2:16:55) Introduction to Hierarchical Data
• (2:18:54) Formal Background on the Tree
• (2:20:03) Tree Terminology and Visualization
• (2:25:08) Different types of Trees
• (2:28:07) Uses for the Tree
• (2:29:00) Tries
• (2:29:50) Trie Basics
• (2:30:41) Trie Visualization
• (2:34:33) Flagging
• (2:35:15) Uses for Tries
• (2:38:25) Heaps
• (2:38:51) Heap Basics
• (2:39:19) Min-Heaps
• (2:40:07) Max-Heaps
• (2:40:59) Building Heaps
• (2:44:20) Deleting from Heaps
• (2:46:00) Heap Implementations
• (2:48:15) Graphs
• (2:49:25) Graph Basics
• (2:52:04) Directed vs. Undirected Graphs
• (2:53:45) Cyclic vs. Acyclic Graphs
• (2:55:04) Weighted Graphs
• (2:55:46) Types of Graphs
• (2:58:20) Conclusion
• (2:58:43) Shameless Plug

Learn how to use TensorFlow 2.0 in this full tutorial course for beginners.

This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.

Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.

## Course Contents

• ⌨️ Module 1: Machine Learning Fundamentals (00:03:25)
• ⌨️ Module 2: Introduction to TensorFlow (00:30:08)
• ⌨️ Module 3: Core Learning Algorithms (01:00:00)
• ⌨️ Module 4: Neural Networks with TensorFlow (02:45:39)
• ⌨️ Module 5: Deep Computer Vision – Convolutional Neural Networks (03:43:10)
• ⌨️ Module 6: Natural Language Processing with RNNs (04:40:44)
• ⌨️ Module 7: Reinforcement Learning with Q-Learning (06:08:00)
• ⌨️ Module 8: Conclusion and Next Steps (06:48:24)

Siraj Raval shows off examples of machine learning apps from his students.

If you’re wondering about my stance on the recent controversies around Siraj, I recorded a Data Point about that.

Machine Learning powers almost every internet service we use these days, but it’s rare to find a full pipeline example of machine learning being deployed in a web app. In this episode, I’d like to present 5 full-stack machine learning demos submitted as midterm projects from the students of my current course. The midterm assignment was to create a paid machine learning web app, and after receiving countless incredible submissions, I’ve decided to share my favorite 5 publicly. I was surprised by how many students in the course had never coded before and to see them building a full-stack web app in a few weeks was a very fulfilling experience. Use these examples as a template to help you ideate on potential business ideas to make a positive impact in the world using machine learning. And if you’d like, be sure to reach out and support each of the students I’ve demoed here today in any way can you offer. They’ve been working their butts off. Enjoy!