# Data Science: Python for Machine Learning

Machine learning in data science using Python with hands on examples.

# Data Science: Python for Machine Learning

Machine learning in data science using Python with hands on examples.
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A tour of the Jupyter Notebook screen with explanations for each drop-down menu, icon, etc. To help the student familiarise themselves with the Jupyter Notebook interface.
34:07
Links to Python installation instructions have been provided for Mac, Windows and Linux. We begin with the basics of Python such as: Data types - A detailed explanation of all the data types available in Python Declaring Variables and Assigning Values - How to declare a variable and then assign a value to it. Printing Values- After declaring and assigning values to a variable, it must be printed in order to be visible. Importing Libraries - How to import Python Libraries so that they can be used in the notebook. 5. Python Operators - How to conduct basic mathematical operations such as: • Addition• Subtraction• Multiplication • Division • Comparison (greater than, less than or equal to) • Logical operators (and, or , not, xor) • Bitwise operators• Assignment operators • Identity operators • Membership operators. 6. Strings • Find the length of a string • Replace a word • Find a word 7. Lists • Add an element to a list • Print a section of a list • Remove an element of a list 8. Tuples9. Dictionaries • Creating a dictionary • Print an element • Delete an element
Continuation of Python basics: 1. Control Flow 2. Loops • For loop • While loop • Nested loop • Adding a Break • Continuing a loop 3. Syntax 4. Functions • Built-in functions • User-defined functions
The final part of the series 'Python Basics': Classes Objects Modules Exception Handling
10:39
10:39
Basics of Numpy such as: Creating Arrays Changing Elements in Arrays Random Arrays Linear Algebra • Addition • Subtraction • Multiplication • Division • Square roots 5. Broadcasting 6. Statistics • Finding the mean • Finding the sum • Finding the cumulative sum
9:38
9:38
1) Integration 2) Ordinary Differential Equations 3 )Linear Algebra 4) Optimization 5) Statistics
11:00
11:00
1) Basic Graphs • Plotting a basic line graph • Inserting a graph within a graph 2) Adding Legends 3) Customization of the Line Graph 4) Axis Grids 5) Plotting Styles 6) Histograms 7) Annotations 8) Color Maps
13:26
13:26
1) Creating Series 2) Creating Data Frames 3) Summary 4) Transpose 5) Selection 6) Slicing 7) Handling Missing Values 8) Operations • Mean • Cumulative Sum • Minimum & Maximum 9) Merging 10) Grouping 11) Sharing 12) Input/output
Simplified explanations of: 1) Supervised Learning 2) Unsupervised Learning 3) Classification 4) Regression 5) Clustering 6) Underfitting 7) Overfitting 8) Gradient Descent 9) Features 10) Hypothesis Function 11) Parameter 12) Cost Function 13) Back propagation
9:20
1. Learning 2. Predicting 3. Model persistence

This class is not meant to be merely observed, it is a practical lesson which requires you to open your Jupyter notebook and type the code as you see it on the screen. This way, you will understand how the code works. Feel free to experiment with the code by entering your own values.

It is a hands-on approach to learn data science using Jupyter Notebooks. All explanations are present on the screen so that you can pause the video and read them at your own pace. Installation instructions have been provided for Python as well as it's libraries.

This course provides installation instructions for:

• Jupyter Notebooks
• Python
• Numpy
• Scipy
• Matplotlib
• Pandas
• Seaborn
A tour of the Jupyter Notebook screen with explanations for each drop-down menu, icon, etc. To help the student familiarise themselves with the Jupyter Notebook interface.
34:07
Links to Python installation instructions have been provided for Mac, Windows and Linux. We begin with the basics of Python such as: Data types - A detailed explanation of all the data types available in Python Declaring Variables and Assigning Values - How to declare a variable and then assign a value to it. Printing Values- After declaring and assigning values to a variable, it must be printed in order to be visible. Importing Libraries - How to import Python Libraries so that they can be used in the notebook. 5. Python Operators - How to conduct basic mathematical operations such as: • Addition• Subtraction• Multiplication • Division • Comparison (greater than, less than or equal to) • Logical operators (and, or , not, xor) • Bitwise operators• Assignment operators • Identity operators • Membership operators. 6. Strings • Find the length of a string • Replace a word • Find a word 7. Lists • Add an element to a list • Print a section of a list • Remove an element of a list 8. Tuples9. Dictionaries • Creating a dictionary • Print an element • Delete an element
Continuation of Python basics: 1. Control Flow 2. Loops • For loop • While loop • Nested loop • Adding a Break • Continuing a loop 3. Syntax 4. Functions • Built-in functions • User-defined functions
The final part of the series 'Python Basics': Classes Objects Modules Exception Handling
10:39
10:39
Basics of Numpy such as: Creating Arrays Changing Elements in Arrays Random Arrays Linear Algebra • Addition • Subtraction • Multiplication • Division • Square roots 5. Broadcasting 6. Statistics • Finding the mean • Finding the sum • Finding the cumulative sum
9:38
9:38
1) Integration 2) Ordinary Differential Equations 3 )Linear Algebra 4) Optimization 5) Statistics
11:00
11:00
1) Basic Graphs • Plotting a basic line graph • Inserting a graph within a graph 2) Adding Legends 3) Customization of the Line Graph 4) Axis Grids 5) Plotting Styles 6) Histograms 7) Annotations 8) Color Maps
13:26
13:26
1) Creating Series 2) Creating Data Frames 3) Summary 4) Transpose 5) Selection 6) Slicing 7) Handling Missing Values 8) Operations • Mean • Cumulative Sum • Minimum & Maximum 9) Merging 10) Grouping 11) Sharing 12) Input/output
Simplified explanations of: 1) Supervised Learning 2) Unsupervised Learning 3) Classification 4) Regression 5) Clustering 6) Underfitting 7) Overfitting 8) Gradient Descent 9) Features 10) Hypothesis Function 11) Parameter 12) Cost Function 13) Back propagation
9:20
1. Learning 2. Predicting 3. Model persistence

#### Vinita Silaparasetty

Data Scientist
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Vinita Silaparasetty is currently exploring the field of Artificial Intelligence, particularly in Machine Learning, Deep Learning and Neural Networks.

She shares her knowledge of Machine Learning & Deep Learning on Quora and Medium.

She is the co-organizer of the "Bangalore Artificial Intelligence Meetup" as well as the "AI for Women" meetup groups.

#datascience #artificialintelligence #machinelearning #vinitas #vinita #vinitamohan #deeplearning

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