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

• We can build a much brighter future where humans are relieved of menial work using AI capabilities. - Professor Andrew Ng

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]

Linear regression

Logistic regression

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

• Python Programming

  Setting up the environment

  Python For Absolute Beginners : Setting up the Environment : Anaconda

  Python For Absolute Beginners : Variables , Lists, Tuples , Dictionary

• Boolean operations

• Conditions , Loops

• (Sequence , Selection, Repetition/Iteration)

• Functions

• 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.