About This Course

In this course, you’ll learn how to get started in Data Science. You don’t need any prior knowledge in programming. We’ll teach you the Python basics you need to get started. 

Here are the items we’ll cover in this course:

  • The Data Science Process
  • Python for Data Science
  • NumPy for Numerical Computation
  • Pandas for Data Manipulation
  • Matplotlib for Visualization
  • Seaborn for Beautiful Visuals
  • Plotly for Interactive Visuals
  • Introduction to Machine Learning
  • Dask for Big Data
  • Association Rule Mining – Apriori
  • Deep Learning and next steps

For the machine learning section, here are some items we’ll cover:

  • How algorithms work
  • Advantages and disadvantages of various algorithms
  • Feature Importances
  • Metrics
  • Cross-Validation
  • Fighting Overfitting
  • Hyperparameter Tuning
  • Handling Imbalanced Data
  • TensorFlow And Keras
  • Automated Machine Learning (AutoML)

Who this course is for:

  • People who would like to get started with Data Science and Machine Learning without any prior knowledge
  • People who would like to refresh their Data Science and Machine Learning knowledge

Our Promise to You

By the end of this course, you will have learned Python for Data Science and Machine Learning.

30 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about Data Science and Machine Learning.

Course Curriculum

Section 1 - Introduction
Plan Of Attack 00:00:00
Downloadable Resources 00:00:00
Install Anaconda 00:00:00
Understand The Data Science Process 00:00:00
Section 2 - Understand Python For Data Science
Python For Data Science 00:00:00
Linux Launch Notebook 00:00:00
Windows Launch Notebook 00:00:00
Folder Structure 00:00:00
Python Operations And Comments 00:00:00
Python Data Types 00:00:00
Python Lists 00:00:00
Lists – Negative Indexing 00:00:00
Python Dictionaries 00:00:00
Python Tuples 00:00:00
Python Sets 00:00:00
Python Boolean Type 00:00:00
Conditional Statements 00:00:00
Python Functions 00:00:00
Python For Loop 00:00:00
Python While Loop 00:00:00
Python Map Function 00:00:00
Python Range Function 00:00:00
Python Exercise 00:00:00
Python Project Solutions 00:00:00
Section 3 - Package Management
pip And Virtualenv Intuition 00:00:00
pip And Virtualenv Practical 00:00:00
Section 4 - NumPy For Numerical Computation
NumPy Introduction 00:00:00
NumPy Arrays 00:00:00
Checking Documentation In Notebooks 00:00:00
Indexing One Dimensional Array 00:00:00
Indexing Multi-Dimensional Array 00:00:00
Broadcasting In NumPy 00:00:00
NumPy Operations 00:00:00
NumPy Project 00:00:00
NumPy Project Solutions 00:00:00
Section 5 - Manipulate Data Using Pandas
Pandas Introduction 00:00:00
Pandas Dataframe 00:00:00
Resetting The Index 00:00:00
Deleting Columns 00:00:00
Dealing With Null Values 00:00:00
Creating New Columns 00:00:00
Selecting In Pandas 00:00:00
Grouping Data 00:00:00
Exporting A Pandas Dataframe 00:00:00
Loading Datasets 00:00:00
Creating Pivot Tables 00:00:00
Pandas Project 00:00:00
Section 6 - Pandas Project Solutions
Part 1 00:00:00
Part 2 00:00:00
Part 3 00:00:00
Part 4 00:00:00
Part 5 00:00:00
Part 6 00:00:00
Part 7 00:00:00
Section 7 - Data Visualization In Matplotlib
Matplotlib Vertical Bar Plot 00:00:00
Matplotlib Horizontal Bar Plot 00:00:00
Matplotlib Scatter Plot 00:00:00
Matplotlib Histogram 00:00:00
Matplotlib Pie Chart 00:00:00
Matplotlib Line Plot 00:00:00
Matplotlib Subplots 00:00:00
Matplotlib Figure And Axes Part one 00:00:00
Matplotlib Figure And Axes Part Two 00:00:00
Matplotlib Project And Solutions 00:00:00
Section 8 - Data Visualization In Seaborn - Categorical Plots
Seaborn Count Plot 00:00:00
Seaborn Violin Plot 00:00:00
Seaborn – Adding Hue 00:00:00
Seaborn Strip Plot 00:00:00
Swarm Plot With Hue 00:00:00
Seaborn Order X Values 00:00:00
Strip Plot with Hue 00:00:00
Seaborn Boxplot 00:00:00
Seaborn Boxen Plot 00:00:00
Seaborn Barplot 00:00:00
Section 9 - Data Visualization In Seaborn - Visualizing Distributions
Joint And Scatter Plots 00:00:00
Seaborn Hexagonal Bins & Kernel Density Estimation 00:00:00
Seaborn Distplot 00:00:00
Seaborn Pair Plot 00:00:00
Seaborn Line Plot 00:00:00
Section 10 - Seaborn With Matplotlib Subplots
Subplots In Seaborn 00:00:00
Seaborn Subplots With Figure And Axes 00:00:00
Section 11 - Matrix Visualization In Seaborn
Seaborn Heatmap 00:00:00
Section 12 - Visualize Linear Relationships In Seaborn
Regression Plots In Seaborn 00:00:00
Seaborn Jointplot With Regression 00:00:00
Section 13 - Seaborn Multi-Plot Grids
Seaborn FacetGrid 00:00:00
Seaborn PairGrid 00:00:00
Section 14 - Word Cloud
Visualization Using Word Clouds 00:00:00
Seaborn And Word Cloud Exercise And Solutions 00:00:00
Section 15 - Build Interactive Visuals With Plotly
Plotly And Jupyter Notebooks 00:00:00
Plotly Introduction 00:00:00
Plotly Express 00:00:00
Plotly Line Plot 00:00:00
Plotly Bar Plot 00:00:00
Plotly Animations 00:00:00
Plotly Density Heatmap 00:00:00
Visualizing On Maps Using Plotly 00:00:00
Subplots In Plotly 00:00:00
Plotly Project And Solutions 00:00:00
Section 16 - Supervised Machine Learning
Introduction To Machine Learning 00:00:00
Linear Regression Intuition 00:00:00
Linear Regression In Scikit-Learn 00:00:00
Linear Regression Exercise 00:00:00
Linear Regression Solutions 00:00:00
Logistic Regression Intuition 00:00:00
Logistic Regression In Python 00:00:00
Logistic Regression Project 00:00:00
Logistic Regression Solutions 00:00:00
Decision Trees Intuition 00:00:00
Random Forest Intuition 00:00:00
Decision Tree And Random Forest Classifier In Scikit-Learn 00:00:00
Decision Tree And Random Forest Classification Project 00:00:00
Decision Tree And Random Forest Classifier Solutions 00:00:00
Decision Tree And Random Forest Regression Part 1 00:00:00
Decision Tree And Random Forest Regression Part 2 00:00:00
Random Forest Regression Part 3 – Feature Importance 00:00:00
Visualize Tree In Random Forest Regression 00:00:00
Random Forest Regression Exercise 00:00:00
Random Forest Regression Solutions 00:00:00
KNeighbors Intuition 00:00:00
K Nearest Neighbors – Getting Started 00:00:00
Checking For Outliers 00:00:00
More Exploratory Data Analysis 00:00:00
Student And Income Plots 00:00:00
Peasonr – Relationship Between The Income And Balance 00:00:00
Chi Square Test – Relationship Between Defaulting And Being A Student 00:00:00
T-Test – Is The mean Income Of Both Defaulters And Non Defaulters The Same? 00:00:00
Feature Engineering 00:00:00
KNN Implementation In Python 00:00:00
Support Vector Machines Intuition 00:00:00
Support Vector Classifier In Python 00:00:00
Support Vector Machine Exercise And Solutions 00:00:00
Handling Imbalanced Data 00:00:00
LightGBM Intuition 00:00:00
Dask For Loading Large Datasets 00:00:00
Dask Intuition 00:00:00
LightGBM Classifier 00:00:00
LightGBM Classifier Project 00:00:00
LightGBM Classifier Project Solutions 00:00:00
LightGBM Regressor 00:00:00
LightGBM Regressor Project 00:00:00
LightGBM Regressor Project Solutions 00:00:00
Extreme Gradient Boosting 00:00:00
XGBoost Classifier 00:00:00
XGBoost Classifier Project 00:00:00
XGBoost Classifier Project Solutions 00:00:00
XGBoost Regressor 00:00:00
XGBoost Regressor Project 00:00:00
XGBoost Regressor Solutions 00:00:00
Tuning And Model Selection 00:00:00
CatBoost Intuition 00:00:00
CatBoost Part Two 00:00:00
CatBoost Classifier 00:00:00
CatBoost Classifier Exercise 00:00:00
CatBoost Classifier Project Solutions 00:00:00
Grid Search CV And Model Selection 00:00:00
CatBoost Regression 00:00:00
CatBoost Regression Exercise 00:00:00
CatBoost Regression Project Solutions 00:00:00
Time Series Analysis 00:00:00
Time Series Exercise 00:00:00
Time Series Project Solutions 00:00:00
Section 17 - K-Means - Unsupervised Machine Learning
K-Means CLustering Intuition 00:00:00
Loading Packages 00:00:00
Convert The Data To Dummy Variables 00:00:00
Principal Component Analysis 00:00:00
Data Scaling 00:00:00
K-Means Implementation 00:00:00
Selecting the Best Number of Clusters 00:00:00
Cluster Analysis 00:00:00
K-means Exercise 00:00:00
K-Means Exercise Solutions 00:00:00
Section 18 - Host A Machine Learning Model
How To Host Your Model – Natural Language Processing 00:00:00
Section 19 - Deep Learning And Next Steps
Core Concepts 00:00:00
Data Preprocessing 00:00:00
Building The Network 00:00:00
Evaluating The Model 00:00:00
Plotting The Model Loss 00:00:00
Overfitting – Classification 00:00:00
Keras Callbacks 00:00:00
Custom Keras Callbacks 00:00:00
Visualization In TensorFlow – TensorBoard 00:00:00
Saving The Model 00:00:00
Convolutional Neural Network Concepts 00:00:00
Building A Convolutional Neural Network 00:00:00
Section 20 - Congratulations
Final Thoughts 00:00:00
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