Machine Learning with Python Diploma

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DIPLOMA OVERVIEW:

  • Level up your skills by learning how to apply machine learning techniques using Python.
  • Level up your skills by learning deep learning and advanced  techniques for machine learning
  • Learn Python from scratch and practice through lab exercises and hands-on methods. Moreover, you will get both the theoretical knowledge along the practical know-how needed to apply these techniques in Machine Learning.

WHY STUDY MACHINE LEARNING?

  • Career Chances: Machine Learning job positions were chosen as one of the top careers in 2020 as the fastest-paced jobs.
  • High-Paying Jobs: Machine Learning job positions are one of the highest-paying jobs.
  • High Demand: Demand for Machine Learning job positions will be growing rapidly in time to come.

WHY STUDY PYTHON AS A MACHINE LEARNING LANGUAGE

  • Easy to read: Python code is more virtually described and visible to users.
  • Easy to learn: Python has few keywords,  easy structure, and a clearly described syntax.
  • Easy to maintain: Python source code is fairly easy to preserve.
  • A broad standard library: Python library is cross-platform compatible with Windows and Macintosh.
  • Interactive Mode: Python allows interactive checking out of snippets of code.
  • Portable: Python can run on a vast range of hardware structures and has an equal interface on all platforms.
  • Databases: Python offers interfaces to all fundamental business.
  • Scalable: Python affords a better structure and support for large applications than shell scripting.
  • Accessible: Python is a free, flexible, and powerful open-source language.

DIPLOMA OBJECTIVES:

Upon completing the diploma you will:

Part 1: MACHINE LEARNING WITH PYTHON

  • Master Machine Learning fundamentals.
  • Learn all Python basics for machine learning.
  • Learn OOP and data structures in Python.
  • Learn the important topics in statistics and mathematics for machine learning.
  • Learn most needed machine learning algorithms.
  • Be able to deal with all files using Python.
  • Be able to implement machine learning projects using  Python.

Part 2: ADVANCED MACHINE LEARNING  AND DEEP LEARNING:

  • Have a quick revision for the basics of Python,  statistics, and mathematics.
  • Learn advanced machine learning techniques.
  • Learn advanced deep learning techniques.
  • Be able to implement deep learning projects using Python

ROUNDS:

Besides rounds that are conducted in IMP head office in Egypt; IMP courses are also delivered LIVE with fully interactive sessions allowing for a highly engaging Q & A and assuring that you get the best ever learning experience.

Start DateTimeDaysDelivery Type
18-Mar5:00 PM – 10:00 PMFridayInteractive Online Training
Terms and Conditions
CERTIFICATE OF COMPLETION

To have successfully completed; a trainee should:

  • have an attendance rate of not less than 80% (or such higher attendance requirement as prescribed for the course);
  • Pass the course practical assessments and projects in at least 75% of the total number of assignments as required in each training course.

1- Introduction

What is data? Data types?

Data vs. Information

What‘s Data science?

Data Analysis vs. Data Analytics

What’s Machine Learning and deep learning?

What’s Artificial Intelligence?

Difference between Excel Data Analysis and Programming

Why Python?

Different fields Python can fit

Different Python Versions

Environment setup

2- Statistics and Mathematics Review

Descriptive Statistics: Population vs samples

Descriptive Statistics : Measures of Central Tendency

Descriptive Statistics : Measures of Variability

Descriptive Statistics : Detection of Outliers

Probability : Probability Laws

Probability : Probability Distribution

Probability : Bayesian Theorem

Probability : Central Limit Theorem

Probability : Confidence Interval

Inferential Statistics : ANOVA

Inferential Statistics : Pearson Correlation Coefficient

Inferential Statistics : Spearman Correlation Coefficient

Inferential Statistics : Regression Analysis

Inferential Statistics : Hypothesis Testing

Linear Algebra : Matrix Operations, Inverse and Decomposition

Linear Algebra : Vectors

3-Python Basics

Basic Syntax

Data Types

Operators

Control flow statements

Decisions

Loops

Functions

4- OOP

Classes

Objects

Data members

Overloading

Inheritance

5- Data Structures

List and tuples

Sets

Dictionaries

Strings

6- Files and Databases

Reading from Files

Writing into files

Database connections

Pandas

7- Most Needed Libraries for Data Science and Machine Learning

Numpy

Matplotlib

Plotly

Seaborn

Pandas

Sklearn

8- Machine Learning & Data Science Techniques

Machine Learning & Data Science Overview :Machine Learning vs. Deep Learning vs. Data Science

Machine Learning & Data Science Overview : Supervised Learning vs. Unsupervised Learning vs. Reinforcement Learning

Supervised Learning :Linear Regression

Supervised Learning :Simple Linear Regression in Python

Supervised Learning :Multiple Linear Regression in Python

Supervised Learning :Logistic Regression in Python

Supervised Learning : Nearest neighbor

Unsupervised Learning: Kmeans clustering

9- Introduction To Related Topics

Big data

NLP

Cloud computing

Deep Learning

Neural Network Architecture and How it works

Tensor Flow & keras

Recommender System

10- Advanced Machine Learning – Refreshment

Statistics refreshment

Linear Algebra refreshment

11- Supervised Learning

Linear Regression Review

Logistics Regression Review

SVM Review

K-Nearest Neighbor (K-NN) classification

Decision Tree Classification

Naive Bayes

Random Forest Classification

12- Unsupervised Learning

K Means clustering : K-Means Random Initialization Trap

K Means clustering : K-Means Selecting The Number Of Clusters

Hierarchical clustering : Hierarchical Clustering How Dendrograms Work

Hierarchical clustering : Hierarchical Clustering Using Dendrograms

Principal Component Analysis (PCA)

DB scan

13- Reinforcement Learning

Upper Confidence Bound

Thompson Sampling

14- Deep Learning Part

Neural Network Neural : Network Architecture and How it Works

Neural Network : Artificial Neural Network

Neural Network : Convolutional Neural Networks

Neural Network : Recurrent neural network

Natural Language Processing (NLP)

Tensor Flow

Recommender System

Level: advanced
100 Hours