At the end of the Course you will have all the skills to become a Data Engineering Professional. (The most comprehensive Data Engineering course )
1) Python Programming Basics For Data Science - Python programming plays an important role in the field of Data Science
2) Introduction to Machine Learning - [A -Z] Comprehensive Training with Step by step guidance
3) Setting up the Environment for Machine Learning - Step by step guidance
4) Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest)
5) Unsupervised Learning
6) Evaluating the Machine Learning Algorithms
7) Data Pre-processing
8) Algorithm Analysis For Data Scientists
9) Deep Convolutional Generative Adversarial Networks (DCGAN)
10) Java Programming For Data Scientists
Course Learning Outcomes
To provide awareness of Supervised & Unsupervised learning
Describe intelligent problem-solving methods via appropriate usage of Machine Learning techniques.
To build comprehensive neural models from using state-of-the-art python framework.
To build neural models from scratch, following step-by-step instructions. [Step by step guidance with clear explanation]
To build end - to - end comprehensive solutions to resolve real-world problems by using appropriate Machine Learning techniques from a pool of techniques available.
To critically review and select the most appropriate machine learning solutions
To use ML evaluation methodologies to compare and contrast supervised and unsupervised ML algorithms using an established machine learning framework.
Beginners guide for python programming is also inclusive.
Introduction to Machine Learning - Indicative Module Content
Introduction to Machine Learning:- What is Machine Learning ?, Motivations for Machine Learning, Why Machine Learning? Job Opportunities for Machine Learning
Setting up the Environment for Machine Learning:-Downloading & setting-up Anaconda, Introduction to Google Collabs
Supervised Learning Techniques:-Regression techniques, Bayer’s theorem, Naïve Bayer’s, Support Vector Machines (SVM), Decision Trees and Random Forest.
Unsupervised Learning Techniques:- Clustering, K-Means clustering
Artificial Neural networks [Theory and practical sessions - hands-on sessions]
Evaluation and Testing mechanisms :- Precision, Recall, F-Measure, Confusion Matrices,
Data Protection & Ethical Principles
Setting up the Environment for Python Machine Learning
Understanding Data With Statistics & Data Pre-processing (Reading data from file, Checking dimensions of Data, Statistical Summary of Data, Correlation between attributes)
Data Pre-processing - Scaling with a demonstration in python, Normalization , Binarization , Standardization in Python,feature Selection Techniques : Univariate Selection
Data Visualization with Python -charting will be discussed here with step by step guidance, Data preparation and Bar Chart,Histogram , Pie Chart, etc..
Artificial Neural Networks with Python, KERAS
KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step
Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ]
Naive Bayes Classifier with Python [Lecture & Demo]
Introduction to clustering [K - Means Clustering ]
K - Means Clustering
The course will have step by step guidance for machine learning & Data Engineering with Python.
You can enhance your core programming skills to reach the advanced level. By the end of these videos, you will get the understanding of following areas the
Python Programming Basics For Data Science - Indicative Module Content
Setting up the environment
Python For Absolute Beginners : Setting up the Environment : Anaconda
Python For Absolute Beginners : Variables , Lists, Tuples , Dictionary
Conditions , Loops
(Sequence , Selection, Repetition/Iteration)
File Handling in Python
Algorithm Analysis For Data Scientists
This section will provide a very basic knowledge about Algorithm Analysis. (Big O, Big Omega, Big Theta)
Java Programming for Data Scientists
Deep Convolutional Generative Adversarial Networks (DCGAN)
Generative Adversarial Networks (GANs) & Deep Convolutional Generative Adversarial Networks (DCGAN) are one of the most interesting and trending ideas in computer science today. Two models are trained simultaneously by an adversarial process. A generator , learns to create images that look real, while a discriminator learns to tell real images apart from fakes.
At the end of this section you will understand the basics of Generative Adversarial Networks (GANs) & Deep Convolutional Generative Adversarial Networks (DCGAN) .
This will have step by step guidance
Import TensorFlow and other libraries
Load and prepare the dataset
Create the models (Generator & Discriminator)
Define the loss and optimizers (Generator loss , Discriminator loss)
Define the training loop
Train the model
Analyze the output
Does the course get updated?
We continually update the course as well.
What if you have questions?
we offer full support, answering any questions you have.
Who this course is for:
Beginners with no previous python programming experience looking to obtain the skills to get their first programming job.
Anyone looking to to build the minimum Python programming skills necessary as a pre-requisites for moving into machine learning, data science, and artificial intelligence.
Who want to improve their career options by learning the Python Data Engineering skills.