EGP0
Upon completing the diploma you will:
Part 1
1.Master Machine Learning fundamentals.
2.Learn all Python basics for machine learning.
3.Learn OOP and data structures in Python.
4.Learn the important topics in statistics and mathematics for machine learning.
5.Learn most needed machine learning algorithms.
6.Be able to deal with all files using Python.
7.Be able to implement machine learning projects using Python.
Part 2
1.Have a quick revision for basics of Python, statistics and mathematics.
2.Learn advanced machine learning techniques.
3.Learn advanced deep learning techniques.
4.Be able to implement deep learning projects using Python
Course Description
Level up your skills by learning deep learning and advanced
techniques for machine learning.
Training Objectives
1- Have a quick revision for basics of Python, statistics and mathematics.
2- Learn advanced machine learning techniques.
3-Learn deep learning techniques.
4-Be able to implement machine learning and deep learning projects using Python.
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.
Starting Date Days Time
Terms and Conditions
CERTIFICATE OF COMPLETION
To have successfully completed; a trainee should:
Lecture 1.1
What is data? Data types?
Lecture 1.2
Data vs. Information
Lecture 1.3
What‘s Data science?
Lecture 1.4
Data Analysis vs. Data Analytics
Lecture 1.5
What’s Machine Learning and deep learning?
Lecture 1.6
What’s Artificial Intelligence?
Lecture 1.7
Difference between Excel Data Analysis and Programming
Lecture 1.8
Why Python?
Lecture 1.9
Different fields Python can fit
Lecture 1.10
Different Python Versions
Lecture 1.11
Environment setup
Lecture 2.1
Descriptive Statistics: Population vs samples
Lecture 2.2
Descriptive Statistics : Measures of Central Tendency
Lecture 2.3
Descriptive Statistics : Measures of Variability
Lecture 2.4
Descriptive Statistics : Detection of Outliers
Lecture 2.5
Probability : Probability Laws
Lecture 2.6
Probability : Probability Distribution
Lecture 2.7
Probability : Bayesian Theorem
Lecture 2.8
Probability : Central Limit Theorem
Lecture 2.9
Probability : Confidence Interval
Lecture 2.10
Inferential Statistics : ANOVA
Lecture 2.11
Inferential Statistics : Pearson Correlation Coefficient
Lecture 2.12
Inferential Statistics : Spearman Correlation Coefficient
Lecture 2.13
Inferential Statistics : Regression Analysis
Lecture 2.14
Inferential Statistics : Hypothesis Testing
Lecture 2.15
Linear Algebra : Matrix Operations, Inverse and Decomposition
Lecture 2.16
Linear Algebra : Vectors
Lecture 7.1
Most Needed Libraries for Data Science and Machine Learning : Numpy
Lecture 7.2
Most Needed Libraries for Data Science and Machine Learning : Matplotlib
Lecture 7.3
Most Needed Libraries for Data Science and Machine Learning : Plotly
Lecture 7.4
Most Needed Libraries for Data Science and Machine Learning : Seaborn
Lecture 7.5
Most Needed Libraries for Data Science and Machine Learning : Pandas
Lecture 7.6
Most Needed Libraries for Data Science and Machine Learning : Sklearn
Lecture 8.1
Machine Learning & Data Science Overview :Machine Learning vs. Deep Learning vs. Data Science
Lecture 8.2
Machine Learning & Data Science Overview : Supervised Learning vs. Unsupervised Learning vs. Reinforcement Learning
Lecture 8.3
Supervised Learning :Linear Regression
Lecture 8.4
Supervised Learning :Simple Linear Regression in Python
Lecture 8.5
Supervised Learning :Multiple Linear Regression in Python
Lecture 8.6
Supervised Learning :Logistic Regression in Python
Lecture 8.7
Supervised Learning : Nearest neighbor
Lecture 8.8
Unsupervised Learning : Kmeans clustering
Lecture 8.9
Introduction To Related Topics : Big data
Lecture 8.10
Introduction To Related Topics : NLP
Lecture 8.11
Introduction To Related Topics : Cloud computing
Lecture 8.12
Introduction To Related Topics : Deep Learning
Lecture 8.13
Introduction To Related Topics : Neural Network Architecture and How it works
Lecture 8.14
Introduction To Related Topics : Tensor Flow & keras
Lecture 8.15
Introduction To Related Topics : Recommender system
duration | 100 Hours |
---|---|
skill-level | Advanced |
language | English |
EGP0
Copyright 2019 by IMP - Institute of Management Professionals