Best Data Science courses in 2023

Many courses are available on Udemy for the best data science courses in 2023. Some of what we mentioned below are as per ratings and reviews.

 

1-      The Data Science Course 2022: Complete Data Science Bootcamp

Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

 

What you'll learn

·         The course provides the entire toolbox you need to become a data scientist

·         Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow

·         Impress interviewers by showing an understanding of the data science field

·         Learn how to pre-process data

·         Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)

·         Start coding in Python and learn how to use it for statistical analysis

·         Perform linear and logistic regressions in Python

·         Carry out cluster and factor analysis

·         Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn

·         Apply your skills to real-life business cases

·         Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data

·         Unfold the power of deep neural networks

·         Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance

·         Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

 

2-      Python for Data Science and Machine Learning Bootcamp

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

 

Course Link: https://www.udemy.com/course/the-data-science-course-complete-data-science-bootcamp/

 

What you'll learn

·         Use Python for Data Science and Machine Learning

·         Use Spark for Big Data Analysis

·         Implement Machine Learning Algorithms

·         Learn to use NumPy for Numerical Data

·         Learn to use Pandas for Data Analysis

·         Learn to use Matplotlib for Python Plotting

·         Learn to use Seaborn for statistical plots

·         Use Plotly for interactive dynamic visualizations

·         Use SciKit-Learn for Machine Learning Tasks

·         K-Means Clustering

·         Logistic Regression

·         Linear Regression

·         Random Forest and Decision Trees

·         Natural Language Processing and Spam Filters

·         Neural Networks

·         Support Vector Machines

 

 

Course Link: https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/

 

 

3-      Complete Machine Learning & Data Science Bootcamp 2023

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

 

What you'll learn

·         Become a Data Scientist and get hired

·         Master Machine Learning and use it on the job

·         Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0

·         Use modern tools that big tech companies like Google, Apple, Amazon and Meta use

·         Present Data Science projects to management and stakeholders

·         Learn which Machine Learning model to choose for each type of problem

·         Real life case studies and projects to understand how things are done in the real world

·         Learn best practices when it comes to Data Science Workflow

·         Implement Machine Learning algorithms

·         Learn how to program in Python using the latest Python 3

·         How to improve your Machine Learning Models

·         Learn to pre process data, clean data, and analyze large data.

·         Build a portfolio of work to have on your resume

·         Developer Environment setup for Data Science and Machine Learning

·         Supervised and Unsupervised Learning

·         Machine Learning on Time Series data

·         Explore large datasets using data visualization tools like Matplotlib and Seaborn

·         Explore large datasets and wrangle data using Pandas

·         Learn NumPy and how it is used in Machine Learning

·         A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided

·         Learn to use the popular library Scikit-learn in your projects

·         Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry

·         Learn to perform Classification and Regression modelling

·         Learn how to apply Transfer Learning

 

 

Course Link: https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/

 

 

4-      Data Science: Supervised Machine Learning in Python

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

 

What you'll learn

·         Understand and implement K-Nearest Neighbors in Python

·         Understand the limitations of KNN

·         User KNN to solve several binary and multiclass classification problems

·         Understand and implement Naive Bayes and General Bayes Classifiers in Python

·         Understand the limitations of Bayes Classifiers

·         Understand and implement a Decision Tree in Python

·         Understand and implement the Perceptron in Python

·         Understand the limitations of the Perceptron

·         Understand hyperparameters and how to apply cross-validation

·         Understand the concepts of feature extraction and feature selection

·         Understand the pros and cons between classic machine learning methods and deep learning

·         Use Sci-Kit Learn

·         Implement a machine learning web service

 

 

Course Link: https://www.udemy.com/course/data-science-supervised-machine-learning-in-python/

 

5-      Unsupervised Deep Learning in Python

Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA

 

What you'll learn

·         Understand the theory behind principal components analysis (PCA)

·         Know why PCA is useful for dimensionality reduction, visualization, de-correlation, and denoising

·         Derive the PCA algorithm by hand

·         Write the code for PCA

·         Understand the theory behind t-SNE

·         Use t-SNE in code

·         Understand the limitations of PCA and t-SNE

·         Understand the theory behind autoencoders

·         Write an autoencoder in Theano and Tensorflow

·         Understand how stacked autoencoders are used in deep learning

·         Write a stacked denoising autoencoder in Theano and Tensorflow

·         Understand the theory behind restricted Boltzmann machines (RBMs)

·         Understand why RBMs are hard to train

·         Understand the contrastive divergence algorithm to train RBMs

·         Write your own RBM and deep belief network (DBN) in Theano and Tensorflow

·         Visualize and interpret the features learned by autoencoders and RBMs

 

 

Course Link: https://www.udemy.com/course/unsupervised-deep-learning-in-python/