About This Course
Are you prepared to begin your way to getting to be a Information Researcher! This comprehensive course will be your direct to learning how to utilize the control of Python to analyze information, make lovely visualizations, and utilize capable machine learning algorithms!
Data Researcher has been positioned the number one work on Glassdoor and the normal compensation of a information researcher is over $120,000 within the Joined together States concurring to Indeed! Data Science could be a fulfilling career that permits you to unravel a few of the world's most curiously problems!
This course is outlined for both tenderfoots with a few programming involvement or experienced designers looking to form the bounce to Information Science!
This comprehensive course is comparable to other Information Science bootcamps that as a rule fetched thousands of dollars, but presently you'll be able learn all that information at a fraction of the taken a toll! With over 100 HD video addresses and nitty gritty code note pads for each address this can be one of the foremost comprehensive course for information science and machine learning on Techzex!
We'll educate you how to program with Python, how to form astonishing information visualizations, and how to utilize Machine Learning with Python! Here a fair many of the themes we'll be learning:
- Programming with Python
- NumPy with Python
- Using pandas Data Frames to solve complex tasks
- Use pandas to handle Excel Files
- Web scraping with python
- Connect Python to SQL
- Use matplotlib and seaborn for data visualizations
- Use plotly for interactive visualizations
- Machine Learning with SciKit Learn, including:
- Linear Regression
- K Nearest Neighbors
- K Means Clustering
- Decision Trees
- Random Forests
- Natural Language Processing
- Neural Nets and Deep Learning
- Support Vector Machines
- and much, much more!
Enroll in the course and become a data scientist today!
Curriculum
27 Lessons
3 Month
Introduction to the Course
Course Help and Welcome
Course FAQs
Python Environment Setup
Updates to Notebook Zip
Jupyter Notebooks
Optional: Virtual Environments
Welcome to the Python Crash Course Section!
Introduction to Python Crash Course
Python Crash Course - Part 1
Python Crash Course - Part 2
Python Crash Course - Part 3
Python Crash Course - Part 4
Python Crash Course Exercises - Overview
Python Crash Course Exercises - Solutions
Welcome to the NumPy Section!
Introduction to Numpy
Numpy Arrays
Quick Note on Array Indexing
Numpy Array Indexing
Numpy Operations
Numpy Exercises Overview
Numpy Exercises Solutions
Welcome to the Pandas Section!
Introduction to Pandas
Series
DataFrames - Part 1
DataFrames - Part 2
DataFrames - Part 3
Missing Data
Groupby
Merging Joining and Concatenating
Operations
Data Input and Output
Note on SF Salary Exercise
SF Salaries Exercise Overview
SF Salaries Solutions
Ecommerce Purchases Exercise Overview
Ecommerce Purchases Exercise Solutions
Welcome to the Data Visualization Section!
Introduction to Matplotlib
Matplotlib Part 1
Matplotlib Part 2
Matplotlib Part 3
Matplotlib Exercises Overview
Matplotlib Exercises - Solutions
Introduction to Seaborn
Distribution Plots
Categorical Plots
Matrix Plots
Grids
Regression Plots
Style and Color
Seaborn Exercise Overview
Seaborn Exercise Solutions
Pandas Built-in Data Visualization
Pandas Data Visualization Exercise
Pandas Data Visualization Exercise
Introduction to Plotly and Cufflinks
READ ME FIRST BEFORE PLOTLY PLEASE!
Plotly and Cufflinks
Introduction to Geographical Plotting
Choropleth Maps - Part 1 - USA
Choropleth Maps - Part 2 - World
Choropleth Exercises
Choropleth Exercises - Solutions
Welcome to the Data Capstone Projects!
911 Calls Project Overview
911 Calls Solutions - Part 1
911 Calls Solutions - Part 2
Bank Data
Finance Data Project Overview
Finance Project - Solutions Part 1
Finance Project - Solutions Part 2
Finance Project - Solutions Part 3
Welcome to Machine Learning. Here are a few resources to get you started!
Welcome to the Machine Learning Section!
Supervised Learning Overview
Evaluating Performance - Classification Error Metrics
Evaluating Performance - Regression Error Metrics
Machine Learning with Python
Linear Regression Theory
model_selection Updates for SciKit Learn 0.18
Linear Regression with Python - Part 1
Linear Regression with Python - Part 2
Linear Regression Project Overview
Linear Regression Project Solution
Bias Variance Trade-Off
Logistic Regression Theory
Logistic Regression with Python - Part 1
Logistic Regression with Python - Part 2
Logistic Regression with Python - Part 3
Logistic Regression Project Overview
Logistic Regression Project Solutions
KNN Theory
KNN with Python
KNN Project Overview
KNN Project Solutions
Decision Trees and Random Forest Solutions Part 1
Introduction to Tree Methods
Decision Trees and Random Forest with Python
Decision Trees and Random Forest Project Overview
Decision Trees and Random Forest Solutions Part 2
SVM Theory
Support Vector Machines with Python
SVM Project Overview
SVM Project Solutions
K Means Algorithm Theory
K Means with Python
K Means Project Overview
K Means Project Solutions
Principal Component Analysis
PCA with Python
Recommender Systems
Recommender Systems with Python - Part 1
Recommender Systems with Python - Part 2
Natural Language Processing Theory
NLP with Python - Part 1
NLP with Python - Part 2
NLP with Python - Part 3
NLP Project Overview
NLP Project Solutions
Download TensorFlow Notebooks Here
Quick Check for Notes
Welcome to the Deep Learning Section!
Introduction to Artificial Neural Networks (ANN)
Installing Tensorflow
Perceptron Model
Neural Networks
Activation Functions
Multi-Class Classification Considerations
Cost Functions and Gradient Descent
Backpropagation
TensorFlow vs Keras
TF Syntax Basics - Part One - Preparing the Data
TF Syntax Basics - Part Two - Creating and Training the Model
TF Syntax Basics - Part Three - Model Evaluation
TF Regression Code Along - Exploratory Data Analysis
TF Regression Code Along - Exploratory Data Analysis - Continued
TF Regression Code Along - Data Preprocessing and Creating a Model
TF Regression Code Along - Model Evaluation and Predictions
TF Classification Code Along - EDA and Preprocessing
TF Classification - Dealing with Overfitting and Evaluation
TensorFlow 2.0 Project Options Overview
TensorFlow 2.0 Project Notebook Overview
Keras Project Solutions - Dealing with Missing Data
Keras Project Solutions - Dealing with Missing Data - Part Two
Keras Project Solutions - Categorical Data
Keras Project Solutions - Data PreProcessing
Keras Project Solutions - Creating and Training a Model
Keras Project Solutions - Model Evaluation
Tensorboard
Welcome to the Big Data Section!
Big Data Overview
Spark Overview
Local Spark Set-Up
AWS Account Set-Up
Quick Note on AWS Security
EC2 Instance Set-Up
SSH with Mac or Linux
PySpark Setup
Lambda Expressions Review
Introduction to Spark and Python
RDD Transformations and Actions
Bonus Lecture
Your Instructors
Shuvam Sahoo
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