DataScientest Syllabus
Machine Learning course included all concepts of
• Python
• Mathematics Linear Algebra & statics
• Data processing and visualization
• All Machine Learning algorithms
• Examples & case studies with practical
• Deep learning algorithms
• Assignments
• Real time application projects in micro hardware & electronics
Duration
navratri special discount offer
50%
Available Seats
30
Schedule
Weekly:- 5.00 pm - 7.00 pm
WeekEnd:- Saturday - Sunday : 11:00 am - 2:00 pm
1 Python Fundamentals
• Discovery of the different variables, lists and Tuples
• Initiation to the concept of a programming loop and its different types
• Introduction to functions and their documentation
• Creation of classes and use of modules
2 NumPy
• Creation and manipulation of a NumPy array
• Presentation of matrix operations and management of a NumPy Array
• Creation of a statistical indicator and operations on a NumPy Array
3 Pandas
• Introduction to Pandas library
• Loading and first exploration of a dataset
• Introduction to Data Cleaning
• Introduction to Data Processing
4 Matplotlib
•Presentation of different types of graphs :
• Curves
• Charts
• Point clouds
• Histograms
• Introduction to graph customizatio
5 Bokeh (optional)
• Training in all types of interactive graphics that can be integrated into a Web page
• Visualization of geographical data
• Discovery and creation of Widgets
6 Seaborn
• Control of distribution analysis
• Implementation of statistical analysis
• Introduction to multivariate analysis
7 Classification models and algorithms
• Introduction to Scikit-learn
• Presentation of classic algorithms:
Logistic regression, KNN, SVM…
• Bagging and Boosting techniques
8 Advanced classification of models
• Selection of models
• Semi-supervised Classification
• Anomaly detection
9 Clustering methods
• Unsupervised classification models
(K-Means, CAH, Mean Shift…)
• Evaluation metrics for clustering
10 Regression methods
• Simple and multiple linear regression
• Regularized linear regression
11 Time Series with Statsmodels
• Discovery of ARIMA and SARIMA models
• Analysis and decomposition of a temporal signal
12 Text mining
• Introduction to regular expressions
• Management of text data
• Creation of a WordCloud
13 Machine Learning and Graph Theory with Network X (optional)
• Introduction to graph theory
• Application of fundamental algorithms:
Krustal and Dijkstra
• Detection of communities
• Application of the PageRank algorithm
(classify webpages)
14 Size reduction methods
• Feature selection process
• Introduction to principal componentanalysis
• Application of the Manifold Learningapproach
15 Langage SQL
• Discover Relational Databases
• Implementation of SQL queries
PySpark
• Discovery of PySpark’s different functionalities :
• Large databases management
• Machine Learning
• Pipelines
• Optimization
Deep Learning with Keras framework
• Discovery of fundamental concepts:
• Dense Neural Networks
• Convolutional Neural Networks
• LeNet Architecture
• Transfer Learning
TensorFlow
• Linking TensorFlow with Keras
• Application of Word Embedding with Word2vec
• Presentation of the Recurent Neurals
Networks (GRU, LSTM…)
• Presentation of the Generative adversialNetwork
Introduction to Reinforcement Learning
• Development of mathematics for Reinforcement Learning
• Application of the Monte-Carlo method
• Discovery of the Temporal Difference
• Comparison of learnings: SARSA and Q-Learning
Deep Reinforcement Learning
• Presentation of Deep Q Learning
• Introduction to the Policy Gradient method