Complete Data Science And Machine Learning Bootcamp In Python

Learn the Data Science process, Python for Data Science and Machine Learning, and the different kinds of Machine Learning.

Instructor

Derrick

Derrick Mwiti is a Google Developer Expert in machine learning. He has a great passion for sharing knowledge. He is an avid contributor to the data science community.

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About This Course

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

What you’ll learn:

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
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)

Requirements:

No prior knowledge is required to take this course

This course offers a practical approach to mastering the essential concepts in data science and machine learning. You will learn to explore fundamental techniques and tools used in real-world data analysis and predictive modeling scenarios. With both theory and practical exercises, students will develop a strong foundation in data science methodologies and their applications.

Key Topics:

Understanding the Data Science Process: Learn the essential steps involved in data collection, preprocessing, modeling, and evaluation.
Python Essentials for Data Science: Master Python libraries for data manipulation, analysis, and visualization.
Numerical Computing with NumPy: Explore NumPy for efficient numerical computation and array handling.
Data Manipulation with Pandas: Harness Pandas for seamless data manipulation and transformation tasks.
Visualizing Data with Matplotlib, Seaborn, and Plotly: Create informative and visually appealing plots and charts using these popular visualization libraries.
Introduction to Machine Learning: Gain insights into basic machine learning concepts, algorithms, and their practical applications.
Big Data Handling with Dask: Explore Dask for parallel computing and scalable data analysis, ideal for large datasets.
Association Rule Mining with Apriori: Discover techniques for finding frequent itemsets using the Apriori algorithm.
Deep Learning Fundamentals: Dive into deep learning concepts, covering neural networks and advanced topics like CNNs and RNNs.
Understanding Algorithm Principles: Gain insight into how various machine learning algorithms work and when to use them.
Evaluating Models and Fighting Overfitting: Learn techniques for model evaluation, cross-validation, and strategies to combat overfitting.
Optimizing Model Performance: Explore hyperparameter tuning and feature importance methods to enhance model performance.
Handling Imbalanced Data: Address challenges associated with imbalanced datasets and bias mitigation techniques.
TensorFlow and Keras for Deep Learning: Gain practical experience in creating and training deep learning models with TensorFlow and Keras.
Automated Machine Learning (AutoML): Explore automated techniques for model selection, hyperparameter tuning, and feature engineering.

This course is designed for individuals aspiring to become proficient in Python for data science and machine learning, including professionals and enthusiasts. By the course’s conclusion, students will be equipped with the skills and confidence to tackle real-world data challenges effectively and derive valuable insights from data.

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.

Derrick Mwiti is a Google Developer Expert in machine learning with a great passion for sharing knowledge. Visit his instructor profile page to get more courses on data science and machine learning.

Our Promise to You

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

10 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
	Package Management Introduction	00:00:00
	pip And Virtualenv Intuition	00:00:00
	pip And Virtualenv Practical	00:00:00
	Installing Packages Using The Anaconda Navigator	00:00:00
Section 4 - NumPy For Numerical Computation
	NumPy For Numerical Computation Introduction	00:00:00
	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
	Manipulate Data Using Pandas Introduction	00:00:00
	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
	Data Visualization In Matplotlib Introduction	00:00:00
	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 Introduction	00:00:00
	Plotly And Jupyter Notebooks	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 - Interactive Web Applications With Dash
	Interactive Web Applications With Dash Introduction	00:00:00
	Dash Introduction	00:00:00
	Install Packages	00:00:00
	First Application	00:00:00
	Styles	00:00:00
	Dash Callbacks	00:00:00
	Drop Down Component	00:00:00
	Custom CSS	00:00:00
	Change Title	00:00:00
	Data Upload	00:00:00
	Drag And Drop – Table	00:00:00
	Drag And Drop – Visualize	00:00:00
	Prevent Update	00:00:00
	Sunburst	00:00:00
	Div Data Store – Share Data Between Two Callbacks	00:00:00
	Get Hover, Select And Click Data	00:00:00
	Change Pie Chart From Hover Data	00:00:00
	Dash Tabs	00:00:00
	Single Date Picker	00:00:00
	Dash Date Range Picker	00:00:00
	Caching In Dash	00:00:00
	Loading Animations	00:00:00
	Live Updates	00:00:00
	Plotly Animations	00:00:00
	Mapping	00:00:00
	Multiple Pages Version 1	00:00:00
	Multiple Pages Version 2	00:00:00
	Basic Authentication	00:00:00
	Host On Heroku	00:00:00
	Dash Dev Tools	00:00:00
	Chart Studio	00:00:00
	Dash Libraries	00:00:00
Section 17 - Building Dashboard With Power BI Desktop
	Introduction To Power BI	00:00:00
	Virtual Box	00:00:00
	File Sharing	00:00:00
	Power BI Dashboard And Overview	00:00:00
	Stacked Column Chart	00:00:00
	Clustered Bar Chart	00:00:00
	100% Stacked Bar Chart	00:00:00
	100% Stacked Column Chart	00:00:00
	Line Chart	00:00:00
	Area Chart	00:00:00
	Stacked Area Chart	00:00:00
	Line And Stacked Column Chart	00:00:00
	Ribbon Chart	00:00:00
	Water Fall Chart	00:00:00
	Funnel Chart	00:00:00
	Scatterplot	00:00:00
	Pie Chart	00:00:00
	Donut Chart	00:00:00
	Treemap	00:00:00
	Map	00:00:00
	Gauge And Slicer	00:00:00
	Card	00:00:00
	Multi-Row Card	00:00:00
	Matrix	00:00:00
	Table	00:00:00
	Dashboard	00:00:00
	Project	00:00:00
Section 18 - Data Visualization With Google Data Studio
	Data Visualization With Google Data Studio Introduction	00:00:00
	Data Studio Introduction	00:00:00
	Creating A Data Source	00:00:00
	Table Styling	00:00:00
	Data Freshness	00:00:00
	Add A Logo	00:00:00
	Add Scorecards	00:00:00
	Adding Filters	00:00:00
	Download Data	00:00:00
	Create New Pages	00:00:00
	Create Combo Chart	00:00:00
	Create A Pie Chart	00:00:00
	Add A Google Map	00:00:00
	Create A Scatter Plot	00:00:00
	Create Area Chart	00:00:00
	Create A Pivot Table	00:00:00
	Create A Treemap	00:00:00
	Table With Bars	00:00:00
	Table With Heatmap	00:00:00
	Stacked Column Chart	00:00:00
	Duplicate Page	00:00:00
	100% Stacked Bar Chart	00:00:00
	Donut Chart	00:00:00
	Use Community Visualizations	00:00:00
	Create A Dashboard	00:00:00
	The Google Data Studio Explorer	00:00:00
	Embed Report To Website	00:00:00
Section 19 - Supervised Machine Learning
	Supervised Machine Learning Introduction	00:00:00
	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 20 - K-Means - Unsupervised Machine Learning
	Unsupervised Machine Learning Introduction	00:00:00
	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 21 - Feature Ranking With Recursive Feature Elimination
	Introduction	00:00:00
	Feature Ranking And Selection	00:00:00
	Recursive Feature Implementation	00:00:00
	Creating A Pipeline	00:00:00
	Repeated Stratified K Fold	00:00:00
	Fitting The Pipeline	00:00:00
	Automatic Feature Selection	00:00:00
	Exercise	00:00:00
Section 22 - Association Rule Mining With Apriori
	Association Rule Mining With Apriori Introduction	00:00:00
	Introduction	00:00:00
	Apriori Data Preparation	00:00:00
	Apriori Implementation	00:00:00
	Apriori Solutions	00:00:00
Section 23 - Building Data Science Applications
	Introduction	00:00:00
	StreamLit	00:00:00
	StreamLit Implementation	00:00:00
	Heroku Sign Up	00:00:00
	Heroku Hosting	00:00:00
	Heroku Data Application Project	00:00:00
Section 24 - Natural Language Processing
	Installing Natural Language Toolkit	00:00:00
	Natural Language Processing	00:00:00
	Importing Packages	00:00:00
	Loading Data	00:00:00
	Cleaning Data	00:00:00
	Removing Stop Words	00:00:00
	Lemmatizing	00:00:00
	A Bag Of Words Model	00:00:00
	Occurrences To Frequencies	00:00:00
	Fit To Model	00:00:00
	Save The Model	00:00:00
	Flask	00:00:00
	Flask Logic	00:00:00
	HTML	00:00:00
	Procfile	00:00:00
	Heroku Deployment	00:00:00
	Project	00:00:00
Section 25 - Automated Machine Learning
	Introduction	00:00:00
	Auto-ML Intuition	00:00:00
	Auto-ML Google Collaboration	00:00:00
	Exercise	00:00:00
Section 26 - Big Data Analysis With Apache Spark
	Apache Spark Introduction	00:00:00
	Apache Spark Intuition	00:00:00
	Google Colab Installation	00:00:00
	Using Spark – Resilient Distributed Datasets (RDD)	00:00:00
	Refresher Of Important Concepts – Map, Filter, Reduce, Lambda	00:00:00
	Applying The Concepts In Spark	00:00:00
	Apache Spark DataFrames	00:00:00
	Renaming A Column In Spark DataFrame	00:00:00
	Selecting Columns In A DataFrame	00:00:00
	Filtering A DataFrame	00:00:00
	Group By DataFrame Operation	00:00:00
	SQL Queries In Spark	00:00:00
	Spark On Databricks	00:00:00
	Machine Learning In PySpark – Code Along Project	00:00:00
	Local Set Up – Set Up Ubuntu Virtual Machine	00:00:00
	Local Set Up – Install Docker And The Spark Jupyter Container	00:00:00
Section 27 - Deep Learning And Next Steps
	Deep Learning And Next Steps Introduction	00:00:00
	Core Concepts	00:00:00
	Scikit-Learn	00:00:00
	Scikit-Learn Neural Networks	00:00:00
	Exercise	00:00:00
	Solutions	00:00:00
	Regression In Scikit-Learn Neural Networks	00:00:00
	Exercise	00:00:00
	Solutions	00:00:00
	Points To Note	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 28 - Congratulations
	Final Thoughts	00:00:00

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