Course overview
 Provider
 Udemy
 Course type
 Paid course
 Level
 All Levels
 Duration
 27 hours
 Lessons
 169 lessons
 Certificate
 Available on completion
 Course author
 Academy of Computing & Artificial Intelligence

 Python Programming Basics For Data Science
 Machine Learning  [A Z] Comprehensive Training with Step by step guidance
 Supervised Learning  (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest)
 Unsupervised Learning  Clustering, KMeans clustering
 Evaluating the Machine Learning Algorithms : Precision, Recall, FMeasure, Confusion Matrices,
 Data Preprocessing  Data Preprocessing is that step in which the data gets transformed, or Encoded, to bring it to such a state that now the machine can easily parse it.
 Algorithm Analysis For Data Scientists
 KERAS Tutorial  Developing an Artificial Neural Network in Python Step by Step
 Deep Learning Handwritten Digits Recognition [Step by Step] [Complete Project ]
 Deep Convolutional Generative Adversarial Networks (DCGAN)
 Java Programming For Data Scientists
 Kaggle  Covid 19 Classification (Chest Xray.)  Covid19 & Pneumonia
 Developing a CNN From Scratch for CIFAR10 Photo Classification
Description
At the end of the Course you will have all the skills to become a Data Science Professional. (The most comprehensive Data Science 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 Preprocessing
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 the two most integral branches (Supervised & Unsupervised learning) coming under Machine Learning
Describe intelligent problemsolving methods via appropriate usage of Machine Learning techniques.
To build appropriate neural models from using stateoftheart python framework.
To build neural models from scratch, following stepbystep instructions.
To build end  to  end solutions to resolve realworld 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 & settingup 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, KMeans clustering
Artificial Neural networks [Theory and practical sessions  handson sessions]
Evaluation and Testing mechanisms : Precision, Recall, FMeasure, Confusion Matrices,
Data Protection & Ethical Principles
Setting up the Environment for Python Machine Learning
Understanding Data With Statistics & Data Preprocessing (Reading data from file, Checking dimensions of Data, Statistical Summary of Data, Correlation between attributes)
Data Preprocessing  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 Science 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 prerequisites for moving into machine learning, data science, and artificial intelligence.
Who want to improve their career options by learning the Python Data Engineering skills.
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