MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 32 lectures (1h 56m) | Size: 684 MB Begin your ML and DS Journey
What you'll learn: Introduction to Python Data Structures in Python Control Structures and Functions Python for Data Science Introduction to NumPy Operations on NumPy Arrays Introduction to Pandas Getting and Cleaning Data Data Visualisation in Python Introduction to Data Visualisation Basics of Visualisation Descriptionting Data Distributions Descriptionting Categorical and Time-Series Data Requirements No programming experience is needed. Description Module-1 Welcome to the Pre-Program Preparatory Content Session-1: 1) Introduction 2) Preparatory Content Learning Experience MODULE-2 INTRODUCTION TO PYTHON Session-1: Understanding Digital Disruption Course structure 1) Introduction 2) Understanding Primary Actions 3) Understanding es & Important Pointers Session-2: Introduction to python 1) Getting Started - Installation 2) Introduction to Jupyter Notebook The Basics Data Structures in Python 3) Lists 4) Tuples 5) Dictionaries 6) Sets Session-3: Control Structures and Functions 1) Introduction 2) If-Elif-Else 3) Loops 4) Comprehensions 5) Functions 6) Map, Filter, and Reduce 7) Summary Session-4: Practice Questions 1) Practice Questions I 2) Practice Questions II Module-3 Python for Data Science Session-1: Introduction to NumPy 1) Introduction 2) NumPy Basics 3) Creating NumPy Arrays 4) Structure and Content of Arrays 5) Subset, Slice, Index and Iterate through Arrays 6) Multidimensional Arrays 7) Computation Times in NumPy and Standard Python Lists 8) Summary Session-2: Operations on NumPy Arrays 1) Introduction 2) Basic Operations 3) Operations on Arrays 4) Basic Linear Algebra Operations 5) Summary Session-3: Introduction to Pandas 1) Introduction 2) Pandas Basics 3) Indexing and Selecting Data 4) Merge and Append 5) Grouping and Summarizing Data frames 6) Lambda function & Pivot tables 7) Summary Session-4: Getting and Cleaning Data 1) Introduction 2) Reading Delimited and Relational Databases 3) Reading Data from Websites 4) Getting Data from APIs 5) Reading Data from PDF Files 6) Cleaning Datasets 7) Summary Session-5: Practice Questions 1) NumPy Practice Questions 2) Pandas Practice Questions 3) Pandas Practice Questions Solution Module-4 Session-1: Vectors and Vector Spaces 1) Introduction to Linear Algebra 2) Vectors: The Basics 3) Vector Operations - The Dot Product 4) Dot Product - Example Application 5) Vector Spaces 6) Summary Session-2: Linear Transformations and Matrices 1) Matrices: The Basics 2) Matrix Operations - I 3) Matrix Operations - II 4) Linear Transformations 5) Determinants 6) System of Linear Equations 7) Inverse, Rank, Column and Null Space 8) Least Squares Approximation 9) Summary Session-3: Eigenvalues and Eigenvectors 1) Eigenvectors: What Are They? 2) Calculating Eigenvalues and Eigenvectors 3) Eigen decomposition of a Matrix 4) Summary Session-4: Multivariable Calculus Module-5 Session-1: Introduction to Data Visualisation 1) Introduction: Data Visualisation 2) Visualisations - Some Examples 3) Visualisations - The World of Imagery 4) Understanding Basic Chart Types I 5) Understanding Basic Chart Types II 6) Summary: Data Visualisation Session-2: Basics of Visualisation Introduction 1) Data Visualisation Toolkit 2) Components of a Description 3) Sub-Descriptions 4) Functionalities of Descriptions 5) Summary Session-3: Descriptionting Data Distributions Introduction 1) Univariate Distributions 2) Univariate Distributions - Rug Descriptions 3) Bivariate Distributions 4) Bivariate Distributions - Descriptionting Pairwise Relationships 5) Summary Session-4: Descriptionting Categorical and Time-Series Data 1) Introduction 2) Descriptionting Distributions Across Categories 3) Descriptionting Aggregate Values Across Categories 4) Time Series Data 5) Summary Session-5: 1) Practice Questions I 2) Practice Questions II Who this course is for Beginner Python developers curious about Machine Learning Homepage
