Machine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, Kaggle Welcome to the " Complete Data Science & Machine Learning A-Z with Python " Course Machine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, Kaggle Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field. 🧠 NumPy & Pandas Course – Timestamps 00:00 Installing Anaconda Distribution for Windows (10:35) 10:35 Notebook Project Files Link regarding NumPy Python Programming Language Library (00:02) 10:37 Installing Anaconda Distribution for MacOS (06:17) 16:54 6 Article Advice And Links about NumPy (00:26) 17:20 Installing Anaconda Distribution for Linux (14:43) 32:03 Introduction to NumPy Library (06:24) 38:27 The Power of NumPy (16:04) 54:31 Quiz – 1 Question 54:31 Reshaping a NumPy Array: reshape() (05:56) 1:00:27 Identifying the Largest Element of a NumPy Array (03:45) 1:04:12 Detecting Least Element: min() (02:35) 1:06:47 Concatenating NumPy Arrays: concatenate() (09:40) 1:16:27 Splitting One-Dimensional Arrays: split() (05:45) 1:22:12 Splitting Two-Dimensional Arrays (09:33) 1:31:45 Sorting NumPy Arrays: sort() (04:16) 1:36:01 Quiz – 6 Questions 1:36:01 Indexing NumPy Arrays (07:39) 1:43:40 Slicing One-Dimensional Arrays (06:08) 1:49:48 Slicing Two-Dimensional Arrays (09:30) 1:59:18 Assigning Value to One-Dimensional Arrays (05:02) 2:04:20 Assigning Value to Two-Dimensional Arrays (09:57) 2:14:17 Fancy Indexing of One-Dimensional Arrays (06:09) 2:20:26 Fancy Indexing of Two-Dimensional Arrays (12:32) 2:32:58 Combining Fancy Index with Normal Indexing (03:25) 2:36:23 Combining Fancy Index with Normal Slicing (04:36) 2:40:59 Operations with Comparison Operators (06:09) 2:47:08 Arithmetic Operations in NumPy (15:10) 3:02:18 Statistical Operations in NumPy (06:35) 3:08:53 Solving Second-Degree Equations with NumPy (07:00) 3:15:53 Introduction to Pandas Library (06:38) 3:22:31 Pandas Project Files Link (00:00) 3:22:31 Quiz – 1 Question 3:22:31 Creating a Pandas Series with a List (10:21) 3:32:52 Creating a Pandas Series with a Dictionary (04:53) 3:37:45 Creating Series with a NumPy Array (03:10) 3:40:55 Object Types in Series (05:14) 3:46:09 Examining Primary Features of Pandas Series (04:55) 3:51:04 Most Applied Methods on Pandas Series (12:53) 4:03:57 Indexing and Slicing Pandas Series (07:12) 4:11:09 Quiz – 4 Questions 4:11:09 Creating DataFrame with List (05:33) 4:16:42 Creating DataFrame with NumPy Array (03:03) 4:19:45 Creating DataFrame with Dictionary (04:01) 4:23:46 Examining DataFrame Properties (06:32) 4:30:18 Quiz – 2 Questions 4:30:18 Element Selection in DataFrames – Lesson 1 (07:41) 4:37:59 Element Selection in DataFrames – Lesson 2 (06:04) 4:44:03 Top Level Element Selection – Lesson 1 (08:42) 4:52:45 Top Level Element Selection – Lesson 2 (07:33) 5:00:18 Top Level Element Selection – Lesson 3 (05:35) 5:05:53 Element Selection with Conditional Operations (11:23) 5:17:16 Quiz – 4 Questions