Breaking into Data Science Machine Learning with Python

Breaking into Data Science & Machine Learning with Python, available at $59.99, has an average rating of 4.75, with 111 lectures, based on 33 reviews, and has 479 subscribers.

You will learn about How to use Python for Data Science Applications Python Libraries: Pandas, NumPy, Sci-kit learn Data Visualization Libraries: Matplotlib, Seaborn, Plotly Exploratory data Analysis (EDA), Descriptive Analysis, Predictive Modeling using Machine Learning Data Science Best Practices: How techniques and tools are being used by Data Scientist in industries. Machine Learning Model: Linear and Logistics Regression, KNN, Naive Bayes, Multinomial Models Why and when to use a particular ML Models This course is ideal for individuals who are Anyone interested to break into Data science or College Students Aspiring to be a Data Analyst/Scientist or Data Analyst or any Data Professional or Beginner and Intermediate level Data Scientist or Professional with STEM degree breaking in to Data Science or Technical Program Managers working with Data Scientist or Business Analyst wanted to know Data Science techniques or Anyone Started Learning Journey towards AI It is particularly useful for Anyone interested to break into Data science or College Students Aspiring to be a Data Analyst/Scientist or Data Analyst or any Data Professional or Beginner and Intermediate level Data Scientist or Professional with STEM degree breaking in to Data Science or Technical Program Managers working with Data Scientist or Business Analyst wanted to know Data Science techniques or Anyone Started Learning Journey towards AI.

Enroll now: Breaking into Data Science & Machine Learning with Python

Summary

Title: Breaking into Data Science & Machine Learning with Python

Price: $59.99

Average Rating: 4.75

Number of Lectures: 111

Number of Published Lectures: 110

Number of Curriculum Items: 111

Number of Published Curriculum Objects: 110

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

How to use Python for Data Science Applications

Python Libraries: Pandas, NumPy, Sci-kit learn

Data Visualization Libraries: Matplotlib, Seaborn, Plotly

Exploratory data Analysis (EDA), Descriptive Analysis, Predictive Modeling using Machine Learning

Data Science Best Practices: How techniques and tools are being used by Data Scientist in industries.

Machine Learning Model: Linear and Logistics Regression, KNN, Naive Bayes, Multinomial Models

Why and when to use a particular ML Models

Who Should Attend

Anyone interested to break into Data science

College Students Aspiring to be a Data Analyst/Scientist

Data Analyst or any Data Professional

Beginner and Intermediate level Data Scientist

Professional with STEM degree breaking in to Data Science

Technical Program Managers working with Data Scientist

Business Analyst wanted to know Data Science techniques

Anyone Started Learning Journey towards AI

Target Audiences

Anyone interested to break into Data science

College Students Aspiring to be a Data Analyst/Scientist

Data Analyst or any Data Professional

Beginner and Intermediate level Data Scientist

Professional with STEM degree breaking in to Data Science

Technical Program Managers working with Data Scientist

Business Analyst wanted to know Data Science techniques

Anyone Started Learning Journey towards AI

Let me tell you my story. I graduated with my Ph. D. in computational nano-electronics but I have been working as a data scientist in most of my career. My undergrad and graduate major was in electrical engineering (EE) and minor in Physics. After first year of my job in Intel as a “yield analysis engineer” (now they changed the title to Data Scientist), I literally broke into data science by taking plenty of online classes. I took numerous interviews, completed tons of projects and finally I broke into data science. I consider this as one of very important achievement in my life. Without having a degree in computer science (CS) or a statistics I got my second job as a Data Scientist. Since then I have been working as a Data Scientist.

If I can break into data science without a CS or Stat degree I think you can do it too!

In this class allow me sharing my journey towards data science and let me help you breaking into data science. Of course it is not fair to say that after taking one course you will be a data scientist. However we need to start some where. A good start and a good companion can take us further.

We will definitely discuss Python, Pandas, NumPy, Sk-learn and all other most popular libraries out there. In this course we will also try to de-mystify important complex concepts of machine learning. Most of the lectures will be accompanied by code and practical examples. I will also use “white board” to explain the concepts which cannot be explained otherwise. A good data scientist should use white board for ideation, problem solving. I also want to mention that this course is not designed towards explaining all the math needed to “practice” machine learning. Also, I will be continuously upgrading the contents of this course to make sure that all the latest tools and libraries are taught here. Stay tuned!

Course Curriculum

Chapter 1: Data Science Tool Box

Lecture 1: Welcome to the course!

Lecture 2: Installing Anaconda

Lecture 3: Exploring Jupyter Notebook

Chapter 2: Python Crash Course

Lecture 1: Python Crash Course Overview

Lecture 2: Simple Input and Output in Python

Lecture 3: String in Python

Lecture 4: Playing with Numbers

Lecture 5: List in Python

Lecture 6: Tuple

Lecture 7: Dictionary in Python

Lecture 8: More on Python Dictionary

Lecture 9: Boolean in Python

Lecture 10: Example of Boolean Data Types

Lecture 11: Conditional Statement in Python: if else

Lecture 12: Loop in Python

Lecture 13: How to Write Function in Python

Chapter 3: Obtaining Data

Lecture 1: Overview of data obtaining, cleaning and exploratory analysis

Lecture 2: Reading Data From CSV File: Part 1

Lecture 3: Reading Data From CSV File: Part 2

Lecture 4: Reading Data From Excel File

Lecture 5: Obtaining Data From SQL Server

Lecture 6: Obtaining Data From API

Chapter 4: Cleaning Data

Lecture 1: Sanity Check

Lecture 2: Data Cleaning

Lecture 3: Data Cleaning Excercise

Lecture 4: Solution to Data Cleaning Exercise, Part : 1

Lecture 5: Pandas Apply Function

Lecture 6: Solution to Data Cleaning Exercise, Part : 2

Chapter 5: Exploratory Data Analysis (EDA)

Lecture 1: Exploratory Data Analysis: Part 1

Lecture 2: Exploratory Data Analysis: Part 2

Lecture 3: Exercise on EDA

Lecture 4: Pandas Group By Function

Lecture 5: Solution to EDA Exercise

Chapter 6: Data Visualization

Lecture 1: Introduction to Data Visualization

Lecture 2: Line Plots

Lecture 3: Different Types of Chart

Lecture 4: Categorical Data Visualization: Part 1 – Distribution Plots

Lecture 5: Categorical Data Visualization: Part 2 – Violin Plots

Lecture 6: Categorical Data Visualization: Part 3 – Violin Plots

Lecture 7: Categorical Data Visualization: Part 4 – Bar Plots and more

Lecture 8: Spatial Data Visualization: Part 1

Lecture 9: Spatial Data Visualization: Part 2

Lecture 10: Time Series Data Visualization: Part 1

Lecture 11: Time Series Data Visualization: Part 2 – Seaborn Example

Lecture 12: Time Series Data Visualization: Part 3 – Plotly Example

Lecture 13: Plotly Installation Guideline

Chapter 7: Data Wrangling/Manipulation

Lecture 1: Data Wrangling Introduction

Lecture 2: Slicing/Filtering: Part 1

Lecture 3: Slicing/Filtering: Part 2

Lecture 4: Slicing/Filtering: Part 3

Lecture 5: Slicing/Filtering: Part 4

Lecture 6: Slicing/Filtering: Part 5

Lecture 7: Slicing/Filtering: Part 6

Lecture 8: Aggregation

Lecture 9: Aggregation Excercise

Lecture 10: Aggregation Exercise: Solution

Lecture 11: Reshaping: Part 1- Pivot

Lecture 12: Reshaping: Part 2 (Stacking)

Lecture 13: Reshaping: Part 3 (Unstacking)

Lecture 14: Merge/Join/Concatenation

Lecture 15: Reshaping Exercise

Lecture 16: Reshaping Exercise Solution

Chapter 8: Predictive Analysis with Machine Learning

Lecture 1: Introduction to Machine Learning with an Example

Lecture 2: Different Types of Machine Learning

Chapter 9: Linear Regression

Lecture 1: Introduction to Linear Regression

Lecture 2: Linear Regression: Part 1

Lecture 3: Linear Regression: Part 2

Lecture 4: Model Metrics

Lecture 5: Excercise

Lecture 6: Exploratory Data Analysis for the Excercise

Lecture 7: Solution of Exercise: Feature Engineering

Lecture 8: Solution of Exercise: Model Building

Lecture 9: Solution of Exercise: Model Enhancement

Chapter 10: Logistic Regression

Lecture 1: Introduction to Logistic Regression With an Example

Lecture 2: Explaining Sigmoid Function : The Math Behind the Magic

Lecture 3: Explaining Math of Logit/Logistic Function

Lecture 4: Logistic Regression Model Building

Lecture 5: Model Evaluation

Lecture 6: Model Evaluation: Part 2

Lecture 7: Explaining Math of Model Accuracy Calculation

Lecture 8: Confusion Matrix Math and Code

Lecture 9: Precision/Recall Calculation

Lecture 10: F-1 Score

Lecture 11: ROC/AUC

Lecture 12: Summarizing Model Performance Metrices

Lecture 13: Cross Validation

Lecture 14: Model Selection

Chapter 11: Multinomial Logistic Regression

Lecture 1: Introduction to Multinomial Logistic Regression

Lecture 2: Exercise

Instructors

Dr. KM Mohsin
Data Scientist, Computational Scientist: Nano-Electronics

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4 stars: 6 votes

5 stars: 27 votes

Frequently Asked Questions

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