AI & ML (Data science) – Divasprik Academy

Unleash the potential of data and artificial intelligence. Master the foundations of data science, including data analysis, machine learning, and predictive modeling. Harness the power of AI algorithms to gain valuable insights and make informed decisions.

Download Prospects

Download Certification

What is Conda?
Conda Environments
Windows Environment Setup
Jupyter Notebook Walkthrough
Pandas - Data Analysis
Pandas Introduction
Series, Data Frames and CSVs
Describing Data with Pandas
Selecting and Viewing Data with Pandas
Manipulating Data
NumPy
NumPy Introduction
NumPy DataTypes and Attributes
Creating NumPy Arrays
NumPy Random Seed
Viewing Arrays and Matrices
Manipulating Arrays
Standard Deviation and Variance
Reshape and Transpose
Dot Product vs Element Wise
Exercise
Comparison Operators

Python Data Types
Numbers
Math Functions
Operators
Operational - bin and Complex
Variables
Expressions vs Statements
Augmented Assignment Operator
Strings
Type
Escape Sequences
Formatted Strings
String Indexes
Immutability

Machine Learning 101
What Is Machine Learning
AI Machine Learning Data Science
Exercise - Machine Learning Playground
Types of Machine Learning
Machine Learning and Data Science Framework
Introducing Our Framework
Steps in Machine Learning Framework
Types of Machine Learning Problems
Types of Data
Types of Evaluation
Features In Data
Modelling - Splitting Data
Modelling - Picking the Model
Modelling - Tuning
Modelling - Comparison
Experimentation
Tools We Will Use
Learn Python
What Is A Programming Language
Python Interpreter
How To Run Python Code
Sorting Arrays

Built-In Functions + Methods
Booleans Lists
Matrix
Dictionaries
Tuples
Sets
Data Science Environment Setup
Introducing Our Tools
Turn Images Into NumPy Arrays

Scikit-learn - Creating Machine Learning Models
Scikit-learn Introduction
Typical scikit-learn Workflow
Optional - Debugging Warnings In Jupyter
Splitting Your Data
Convert Data To Numbers
Handling Missing Values With Pandas
Choosing The Right Model For Your Data
How ML Algorithms Work
Fitting A Model To The Data
Making Predictions With Our Model

Predict() vs predict_proba()
Making Predictions With Our Model (Regression)
Evaluating A Machine Learning Model (Score)
Evaluating A Machine Learning Model 2 (Cross Validation)
Evaluating A Classification Model 1 (Accuracy)
Evaluating A Classification Model 2 (ROC Curve)

Evaluating A Regression Model 2 (MAE)
Evaluating A Regression Model 3 (MSE)
Evaluating A Model With Cross Validation and Scoring Parameter
Evaluating A Model With Scikit-learn Functions
Improving A Machine Learning Model
Tuning Hyperparameters
Correlation Analysis
Saving And Loading A Model
Putting It All Together
Milestone Project 1 - Supervised Learning (Classification)
Milestone Project 2 - Supervised Learning (Time Series Data)

Predict() vs predict_proba()
Making Predictions With Our Model (Regression)
Evaluating A Machine Learning Model (Score)
Evaluating A Machine Learning Model 2 (Cross Validation)
Evaluating A Classification Model 1 (Accuracy)

Data Engineering
Data Engineering Introduction
What is a Data Engineer?
Types of Databases
OLTP Databases
Hadoop, HDFS and MapReduce
Apache Spark and Apache Flink
Kafka and Stream Processing
Neural Networks - Deep Learning, Transfer Learning and TensorFlow 2
Deep Learning and Unstructured Data
Setting Up Google Colab
Google Colab Workspace
Uploading Project Data
Setting Up Our Data
Importing TensorFlow 2
TensorFlow 2.0 Default Issue
Using A GPU
GPU and Google Colab

Building A Deep Learning Model
Building A Deep Learning Model 2
Building A Deep Learning Model 3
Building A Deep Learning Model 4
Summarizing Our Model
Evaluating Our Model
Preventing Overfitting
Training Your Deep Neural Network
Evaluating Performance With TensorBoard

Make And Transform Predictions
Transform Predictions To Text
Visualizing Model Predictions
Visualizing And Evaluate Model Predictions 2
Visualizing And Evaluate Model Predictions 3
Saving And Loading A Trained Model
Training Model On Full Dataset
Making Predictions On Test Images
Submitting Model to Kaggle
Making Predictions On Our Images
Storytelling + Communication - How To Present Your Projects
Communicating Your Work
Communicating With Managers
Communicating With Co-Workers
Weekend Project Principle
Communicating With Outside World

Reloading Colab Notebook
Loading Our Data Labels
Preparing The Images
Turning Data Labels Into Numbers
Creating Our Own Validation Set
Preprocess Images
Turning Data Into Batches
Visualizing Our Data
Preparing Our Inputs and Outputs

There are many ways to learn BROWSE BY TEACHER

Bruno Acselrad
Math Teacher
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since.
Bruno Acselrad
Senior Lecturor
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since.
Bruno Acselrad
Development
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since.
Bruno Acselrad
professor
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since.

Career Opportunities

Cyber Security can open up a wide range of career opportunities in a rapidly evolving and high-demand field.

EMAIL

CALL NOW

There are many ways to learn BROWSE BY TEACHER

Bruno Acselrad

Bruno Acselrad

Bruno Acselrad

Bruno Acselrad

Career Opportunities

Quick Links

COURSES

CONTACT US