Upon completing the diploma you will:
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.
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 8.1
Machine Learning & Data Science Techniques :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 :Multiple Linear Regression in Python
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 12.1
K Means clustering : K-Means Random Initialization Trap
Lecture 12.2
K Means clustering : K-Means Selecting The Number Of Clusters
Lecture 12.3
Hierarchical clustering : Hierarchical Clustering How Dendrograms Work
Lecture 12.4
Hierarchical clustering : Hierarchical Clustering Using Dendrograms
Lecture 12.5
Principal Component Analysis (PCA)
Lecture 12.6
DB scan
Lecture 14.1
Neural Network Neural : Network Architecture and How it Works
Lecture 14.2
Neural Network : Artificial Neural Network
Lecture 14.3
Neural Network : Convolutional Neural Networks
Lecture 14.4
Neural Network : Recurrent neural network
Lecture 14.5
Natural Language Processing (NLP)
Lecture 14.6
Tensor Flow
Lecture 14.7
Recommender System
duration | 100 Hours |
---|---|
skill-level | Advanced |
language | Arabic & English |
after youu enlist in this course you'll recieve a call from our sales dpt. regarding our payment options
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