The Data Science & Machine Learning Bootcamp in Python
Get acquainted with Python for Data Science and Perform Statistical Analysis
Updated on Oct, 2024
Language - English
Development ,Data Science,Machine Learning
Lectures -248
Resources -8
Duration -11 hours
Lifetime Access
Lifetime Access
30-days Money-Back Guarantee
Get your team access to 10000+ top Tutorials Point courses anytime, anywhere.
Course Description
In this course, you'll learn how to get started in data science. You don't need any prior knowledge of 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.
Deep Learning & Next Steps.
For the machine learning section here are some items we'll cover:
How Algorithms Work.
Advantages & Disadvantages of Various Algorithms.
Feature Importances.
Metrics.
Cross-Validation.
Fighting Overfitting.
Hyperparameter Tuning.
Handling Imbalanced Data.
What you’ll learn:
- LightGBM.
- XGBoost.
- CatBoost.
- Linear Regression.
- Logistic Regression.
- Decision Trees.
- Random Forest.
- Deep Learning using Keras and TensorFlow.
- Artificial Neural Networks.
- How Artificial Neural Networks Work.
- How Artificial Neural Networks Learn.
- Loss Functions Used in Artificial Neural Networks.
- Activation Functions Used in Artificial Neural Networks.
- Cost Functions Used in Artificial Neural Networks.
- Optimizer Functions Used in Artificial Neural Networks.
- What Backpropagation is?
- Different Types of Gradient Descent.
- How to Choose an Activation Function.
- Preparing your Data for Deep Learning Models.
- Monitoring Loss Functions.
- Monitoring Model Metrics.
- Use of CallBacks in Deep Learning.
- Fighting overfitting in TensorFlow.
- Convolutional Neural Networks.
- Natural Language Processing.
- Support Vector Machines
- KNearest Neighbors.
- T-Test.
- Chi-square Test.
- K-Means Clustering.
- Principal Component Analysis.
- Flask.
Goals
- Get acquainted with Python for Data Science.
- Understand the Data Science Process.
- Perform Numerical Computation with NumPy.
- Manipulate Data using Pandas.
- Visualize using Matplotlib.
- Build interactive visuals with Plotly.
- Perform Statistical Analysis.
- Build beautiful visuals using Seaborn.
- Implement Machine Learning Models.
- Load in Big Data using Dask.
- Handle Imbalanced Data.
- Understand the Intuition Behind Popular Machine Learning Algorithms.
- Implement Cross-Validation to improve model performance.
- Search for the best model parameters using Grid Search CV.
- Use experimental algorithms from Scikit-Learn.
- Solve Time Series Problems using Prophet.
- Predict the Price of a Commodity using Linear Regression.
- Host a Machine Learning Model on Heroku.
- Build classification and regression models using LightGBM.
- Implement classification and regression models using XGBoost.
- Build classification and regression models using CatBoost.
- Classify data using Logistic Regression.
- Build models using Decision Trees & Random Forests.
- Perform customer segmentation using KMeans Clustering.
- Solve problems using Support Vector Machines.
Prerequisites
- A great sense of curiosity!
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
5 Lectures
-
Welcome
01:20
01:20
-
Assignment: Introduce Yourself
-
Install Anaconda
01:01
01:01
-
Understand the Data Science Process
02:12
02:12
-
About Reviews
Understand Python for Data Science
20 Lectures
Manage Packages in Python
3 Lectures
Perform Numerical Computation with NumPy
9 Lectures
Manipulate Data with Pandas
12 Lectures
Pandas Project Solutions
7 Lectures
Data Visualization in Matplotlib
10 Lectures
Data Visualization in Seaborn - Categorical Plots
10 Lectures
Data Visualization in Seaborn - Visualizing Distributions
5 Lectures
Seaborn with Matplotlib Subplots
2 Lectures
Matrix Visualization in Seaborn
1 Lectures
Visualize Linear Relationships in Seaborn
2 Lectures
Seaborn Multi-Plot Grids
2 Lectures
Word Cloud
1 Lectures
Seaborn & Word Cloud Exercise and Solutions
1 Lectures
Build Interactive Visuals with Plotly
10 Lectures
Building Data Science Applications with Streamlit
6 Lectures
Building Dashboards in Power BI Desktop
27 Lectures
Supervised Machine Learning
60 Lectures
K-Means - Unsupervised Machine Learning
10 Lectures
Feature Ranking with Recursive Feature Elimination
8 Lectures
Association Rule Mining - Apriori
4 Lectures
Natural Language Processing
16 Lectures
Deep Learning & Next Steps
11 Lectures
Automated Machine Learning
4 Lectures
File Resources
1 Lectures
Instructor Details
Derrick Mwiti
Derrick Mwiti is a data scientist who has a great passion for sharing knowledge. He is an avid contributor to the data science community.
Experienced in data science, machine learning, and deep learning with a keen eye for building machine learning communities.
Derrick works as a machine learning developer advocate, where he helps companies build products that developers want. It involves getting feedback to the companies as well as getting feedback to developers.
Course Certificate
Use your certificate to make a career change or to advance in your current career.
Our students work
with the Best
Related Video Courses
View MoreAnnual Membership
Become a valued member of Tutorials Point and enjoy unlimited access to our vast library of top-rated Video Courses
Subscribe now
Online Certifications
Master prominent technologies at full length and become a valued certified professional.
Explore Now