This machine studying course will present you the basics of how corporations like Google, Amazon, and even Udemy make the most of machine studying and synthetic intelligence (AI) to glean which means and insights from large knowledge units. Glassdoor and Certainly each report that the typical wage for a knowledge scientist is $120,000. That is the usual, not the exception.
Information scientists are already fairly fascinating. It’s troublesome to maintain them on workers in at present’s tight labor market. There’s a extreme scarcity of people that possess the uncommon mixture of scientific coaching, pc experience, and analytical abilities.
At this time’s knowledge scientists are held to the identical requirements because the Wall Avenue “quants” of the ’80s and ’90s. When the necessity arose for revolutionary algorithms and knowledge approaches, physicists and mathematicians flocked to funding banks and hedge funds.
So, it’s no shock that knowledge science is rising to prominence as a promising profession path within the modern-day. It’s analytic in focus, pushed by code, and carried out on a pc. Because of this, it shouldn’t be a shock that the demand for knowledge scientists has been rising steadily within the office for the previous few years.
Alternatively, availability has been low. Acquiring the training and expertise essential to be employed as a knowledge scientist is hard. And that’s why we made this course within the first place!
Every matter is described in plain English, and the course does its finest to keep away from mathematical notations and jargon. After you have entry to the supply code, you may experiment with it and enhance upon it. Studying and making use of these algorithms in the true world, somewhat than in a theoretical or educational setting, is the main target of this course.
Every video will go away you with a brand new perspective you could implement instantly!
In case you have no background in statistics, don’t let that cease you from enrolling on this course; we welcome college students of all ranges.
Introduction
Welcome to the Python for Information Science & ML bootcamp!
Python: A Temporary Overview
The Python Set up Process
What Jupyter is?
Arrange Anaconda on Totally different Working Techniques
Find out how to combine Python into Jupyter?
Dealing with Directories in Jupyter Pocket book
Enter & Output
Working with totally different datatypes
Variables
Arithmetic Operators
Comparability Operators
Logical Operators
Conditional statements
Loops
Sequences Half 1: Lists
Sequences Half 2: Dictionaries
Sequences Half 3: Tuples
Capabilities Half 1: Constructed-in Capabilities
Capabilities Half 2: Consumer-defined Capabilities
The Should-Have Python Information Science Libraries
Finishing Library Setup
Library Importing
Pandas: A Information Science Library
NumPy: A Information Science Library
NumPy vs. Pandas
Matplotlib Library for Information Science
Seaborn Library for Information Science
NumPy Mastery: Every little thing you should find out about NumPy
Intro to NumPy arrays
Creating NumPy arrays
Indexing NumPy arrays
Array form
Iterating Over NumPy Arrays
Fundamental NumPy arrays: zeros()
Fundamental NumPy arrays: ones()
Fundamental NumPy arrays: full()
Including a scalar
Subtracting a scalar
Multiplying by a scalar
Dividing by a scalar
Elevate to an influence
Transpose
Aspect-wise addition
Aspect-wise subtraction
Aspect-wise multiplication
Aspect-wise division
Matrix multiplication
Statistics
DataFrames and Collection in Python’s Pandas
What’s a Python Pandas DataFrame?
What’s a Python Pandas Collection?
DataFrame vs Collection
Making a DataFrame utilizing lists
Making a DataFrame utilizing a dictionary
Loading CSV knowledge into python
Altering the Index Column
Inplace
Analyzing the DataFrame: Head & Tail
Statistical abstract of the DataFrame
Slicing rows utilizing bracket operators
Indexing columns utilizing bracket operators
Boolean record
Filtering Rows
Filtering rows utilizing & and | operators
Filtering knowledge utilizing loc()
Filtering knowledge utilizing iloc()
Including and deleting rows and columns
Sorting Values
Exporting and saving pandas DataFrames
Concatenating DataFrames
groupby()
Information Cleansing Methods for Higher Information
Introduction to Information Cleansing
High quality of Information
Examples of Anomalies
Median-based Anomaly Detection
Imply-based anomaly detection
Z-score-based Anomaly Detection
Interquartile Vary for Anomaly Detection
Coping with lacking values
Common Expressions
Function Scaling
Exploratory Information Evaluation in Python
Introduction
What’s Exploratory Information Evaluation?
Univariate Evaluation
Univariate Evaluation: Steady Information
Univariate Evaluation: Categorical Information
Bivariate evaluation: Steady & Steady
Bivariate evaluation: Categorical & Categorical
Bivariate evaluation: Steady & Categorical
Detecting Outliers
Categorical Variable Transformation
Python for Time-Collection Evaluation: A Primer
Introduction to Time Collection
Getting inventory knowledge utilizing yfinance
Changing a Dataset into Time Collection
Working with Time Collection
Time Collection Information Visualization with Python
Python for Information Visualization: Library Assets, and Pattern Graphs
Introduction
Setting Up Matplotlib
Plotting Line Plots utilizing Matplotlib
Title, Labels & Legend
Plotting Histograms
Plotting Bar Charts
Plotting Pie Charts
Plotting Scatter Plots
Plotting Log Plots
Plotting Polar Plots
Dealing with Dates
Creating a number of subplots in a single determine
The Fundamentals of Machine Studying
Why do we want machine studying?
Machine Studying Use Instances
Approaches to Machine Studying
What’s Supervised studying?
What’s Unsupervised studying?
Supervised studying vs Unsupervised studying
Easy Linear Regression with Python
Introduction to regression
How Does Linear Regression Work?
Line illustration
Implementation in python: Importing libraries & datasets
Implementation in python: Distribution of the info
Implementation in python: Making a linear regression object
A number of Linear Regression with Python
Understanding A number of linear regression
Exploring the dataset
Encoding Categorical Information
Splitting knowledge into Practice and Check Units
Coaching the mannequin on the Coaching set
Predicting the Check Set outcomes
Evaluating the efficiency of the regression mannequin
Root Imply Squared Error in Python
Classification Algorithms: Okay-Nearest Neighbors
Introduction to classification
Okay-Nearest Neighbors algorithm
Instance of KNN
Okay-Nearest Neighbours (KNN) utilizing python
Importing required libraries
Importing the dataset
Splitting knowledge into Practice and Check Units
Function Scaling
Importing the KNN classifier
Outcomes prediction & Confusion matrix
Classification Algorithms: Choice Tree
Introduction to determination timber
What’s Entropy?
Exploring the dataset
Choice tree construction
Importing libraries & datasets
Encoding Categorical Information
Splitting knowledge into Practice and Check Units
Outcomes Prediction & Accuracy
Classification Algorithms: Logistic regression
Introduction
Implementation steps
Importing libraries & datasets
Splitting knowledge into Practice and Check Units
Pre-processing
Coaching the mannequin
Outcomes prediction & Confusion matrix
Logistic Regression vs Linear Regression
Clustering
Introduction to clustering
Use instances
Okay-Means Clustering Algorithm
Elbow methodology
Steps of the Elbow methodology
Implementation in python
Hierarchical clustering
Density-based clustering
Implementation of k-means clustering in python
Importing the dataset
Visualizing the dataset
Defining the classifier
3D Visualization of the clusters
3D Visualization of the anticipated values
Variety of predicted clusters
Recommender System
Introduction
Collaborative Filtering in Recommender Techniques
Content material-based Recommender System
Importing libraries & datasets
Merging datasets into one dataframe
Sorting by title and ranking
Histogram exhibiting variety of scores
Frequency distribution
Jointplot of the scores and variety of scores
Information pre-processing
Sorting the most-rated films
Grabbing the scores for 2 films
Correlation between the most-rated films
Sorting the info by correlation
Filtering out films
Sorting values
Repeating the method for one more film
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