NEXT DATA's Machine Learning training prepares for the Data Scientist role: processing data of different kinds with suitable algorithms to answer decision questions. It teaches how to choose the best-performing model among supervised and unsupervised algorithms and deep neural networks (Deep Learning).
Being a Data Scientist is above all a vocation, which involves processing data of various kinds, using suitable algorithms, to meet decision-making challenges. Within a Data team, you will be responsible for choosing the best-performing model from a range of algorithms: supervised, unsupervised or deep neural networks (Deep Learning). The importance of this particular type of algorithm lies in its power to let machines act without being explicitly programmed, and it is precisely the mastery of these algorithms that has made it possible to industrialise self-driving cars, benefit from speech recognition, run efficient web searches and build a vastly improved understanding of the human genome.
Machine Learning in fact draws on several interdisciplinary skills, such as statistics, linear algebra, optimisation and computer science, to develop automated systems able to sift through large volumes of data in order to make decisions while minimising human intervention. As for its fields of application, Machine Learning is now remarkably pervasive, with applications ranging from business intelligence to national security, from the analysis of biochemical interactions to the structural monitoring of ageing bridges, and more.
Course certification:
- “Data Science” certification.
- Option to take IBM Data Science certifications.
Registration and practical information
Price: 5,500 MAD
Format: In person in Mohammedia, Morocco or live online.
Duration: 6 days (6 hours/day)
Upcoming live online sessions:
- Hours: 9:00 to 15:00
Upcoming in-person sessions (Mohammedia):
- Venue: angle boulevard Hassan II, rue El Jahid, Mohammedia
Contacts:
Mohammedia: (+212) 0652-073560 | (+212) 0608 06 89 89
Register now for the Machine Learning course
This 5-day online module introduces learners to the basics of the Python programming environment, including fundamental Python programming techniques such as lambda functions, reading and manipulating csv files, and the numpy library. The course introduces data manipulation and cleaning techniques using the popular Python data science library pandas, and presents the Series and DataFrame abstractions as the core data structures for data analysis. It also covers Seaborn and Matplotlib, two of Python's most powerful visualisation libraries. By the end of this course, students will be able to take tabular data, clean it, manipulate it and run basic inferential statistical analyses. The objectives of the module are:
- Master the Python programming language.
- Manipulate and analyse data using the Pandas library.
- Manipulate matrices or multidimensional arrays using the Pandas library.
- Create visual representations using Seaborn and Matplotlib, two of Python's most powerful visualisation libraries.
This 5-day module presents the details needed to approach Machine Learning effectively and to build knowledge from data of many different kinds. Topics covered include:
- Understand the basic theory underlying machine learning.
- Be able to formulate machine learning problems corresponding to different applications.
- Understand a range of machine learning algorithms along with their strengths and weaknesses.
- Be able to apply machine learning algorithms to solve problems of moderate complexity.
- Apply the algorithms to a real problem, optimise the learned models and report on the accuracy expected when applying the models.
- Appreciate the importance of visualisation in data analysis solutions
- Apply structured thinking to unstructured problems
- Understand a very broad collection of machine learning algorithms and problems
- Learn algorithmic machine learning topics in enough depth to introduce the theory
- Develop an appreciation of what learning from data involves
The course also draws on numerous case studies and applications, so that the algorithms can be applied to new problems such as text classification.
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- Machine Learning and Statistics trainer: A state engineer and consulting statistician, graduate of the Institut National de Statistique et d’Économie Appliquée (INSEA), the trainer also holds a PhD in Statistics. Our trainer has more than 13 years of professional experience (private and public sectors), with expertise in data processing and modelling and a sound knowledge of economic sectors. He is also a university professor at Al Akhawayn University (AUI). He has delivered several Machine Learning courses as well, within various working groups and for university students and key accounts such as Inwi.
Detailed programme of the Machine Learning & Artificial Intelligence course: 05 days in person and live online + 05 days online
Welcome, general presentation of the course, prerequisites and basic Machine Learning concepts
The aim of the day is to introduce the basic concepts of Machine Learning. Trainees will gain a clear idea of the prerequisites, the Data Analytics Lifecycle and the norms and standards of Machine Learning. They will need to master certain techniques for querying their databases. They will thus be able to distinguish and characterise several types of variables, and to tell a deterministic problem from a probabilistic one. Practical scenarios will be covered during this first session. The day's programme is as follows:
- Objectives and needs identification
- Introduction to machine learning
- Supervised vs unsupervised vs reinforcement
- ML applications and use cases
- Data analytics LifeCycle
- Working environment (Python Fundamentals)
- Overview of python
- Setting up the environment
- Code your first script
Topic 2.1 Data preparation.
- Data analysis with Pandas.
- Pandas DataFrame and Series.
- Importing and exporting data.
- Filtering and slicing data.
- Cleaning data.
Topic 2.2 Data transformation.
- Joining data.
- Transforming data.
- Aggregating data.
Topic 2.3 Data visualisation.
- Data visualisation with Matplotlib and Seaborn.
- Visualising statistical relationships with scatter plots.
- Visualising categorical data with Bar Plot.
- Visualising correlation with Pair Plot and Heatmap.
- Visualising linear relationships with regression.
Topic 2.4 Data analysis.
- Statistical data analysis.
- Time series analysis.
Topic 2.5 Advanced data analysis.
- Data pipelines.
- Group by and applying custom functions.
- Linear regression.
Topic 3.1 Introduction to machine learning and Scikit Learn
- Introduction to machine learning
- Supervised vs unsupervised learning
- Machine learning applications and case studies
- What is Scikit Learn?
- Installing Scikit-Learn
Topic 3.2 Classification
- What is classification?
- Classification algorithms
- Classification workflow
- Confusion matrix
- Binary classification metrics
- ROC and AUC
Topic 3.3 Regression
- What is regression?
- Regression algorithms
- Regression workflow
- Regression metrics
- Overfitting and regularisation
Topic 3.4 Clustering
- What is clustering?
- K-Means clustering
- Silhouette analysis
- Dendrogram and hierarchical clustering
Topic 3.5 Principal component analysis
- The curse of dimensionality
- What is principal component analysis (PCA)?
- Dimensionality reduction with PCA
- Classification recap
- Classification in detail
- Presentation of the use case
- Applying the LifeCycle
- Data preparation
- Exploratory analysis
- Modelling: Machine Learning
- Deployment
- Prediction recap
- Prediction in detail
- Presentation of the use case
- Applying the LifeCycle
- Data preparation
- Exploratory analysis
- Modelling: Machine Learning
- Deployment
Detailed programme of Python for data science - online - 5 days
Python part 1
The aim of this part is to reach an intermediate level of proficiency in Python programming. It covers the following modules:
- Introduction
- Why Python?
- HELLO WORLD
- INDENTATION
- VARIABLES
- DATA TYPES
- NUMBERS
- CASTING
- STRINGS
- BOOLEAN
- LISTS
- TUPLES
- Set
- Dictionaries
Python part 2
The aim of this part is to reach an intermediate level of proficiency in Python programming. It covers the following modules:
- If … Else
- Loops
- Functions
- Lambda Functions
- Array
- Classes/Objects
- Inheritance
- Iterators
- Scope
- Modules
- Datetime
- JSON
- RegEx
- PIP
- Try … Except
- User Input
- String Formatting
Numpy
This module covers using the numpy library to create and manipulate arrays.
- Introduction
- Creating arrays
- Lab 1 – Creating array
- NumPy Array Indexing
- Array Slicing
- Lab 2 – Array Slicing
- Data Types
- Lab 3 – Data Type
- Array Shape
- Array Reshape
- Lab 4 – Array reshape
- Array Iterating
- Lab 5 – Array iterating
- Joining Array
- Lab 6 – Joining array
- Splitting Array
- Lab 7 – Splitting array
- Searching Arrays
- Lab 8 – Searching array
- Sorting Arrays
- Lab 9 – Sorting array
- Filter Array
- Random Numbers
- Lab 10 – Random number
Pandas
This module covers using the pandas module with Python to create and structure data.
- Introduction
- Creating Data
- Lab 1 – Creating data
- Naive accessors
- Indexing in Pandas
- Lab 2 – Indexing
- Summary Functions and Maps
- Lab 3 – Summary Functions and Maps
- Grouping and Sorting
- Lab 4 – Grouping and Sorting
- Data Types & Missing Values
- Lab 5 – Data Types & Missing Values
- Renaming and Combining
Data Visualization
This module covers creating data visualizations with matplotlib and seaborn in Python.
- Introduction
- Line chart
- Lab 1 – Line Charts – Los Angeles City Museums
- Bar chart & Heatmap
- Lab 2 – Bar chart & Heatmap
- Scatter Plots
- Lab 3 – Scatter plots
- Distributions
- Lab 4 – Distributions
- Pairplot
- Boxplot
Frequently Asked Questions (FAQ)
Being a Data Scientist is above all a vocation, which involves processing data of various kinds, using suitable algorithms, to meet decision-making challenges. Within a Data team, you will be responsible for choosing the best-performing model from a range of algorithms: supervised, unsupervised or deep neural networks (Deep Learning). The importance of this particular type of algorithm lies in its power to let machines act without being explicitly programmed, and it is precisely the mastery of these algorithms that has made it possible to industrialise self-driving cars, benefit from speech recognition, run efficient web searches and build a vastly improved understanding of the human genome.
Machine Learning in fact draws on several interdisciplinary skills, such as statistics, linear algebra, optimisation and computer science, to develop automated systems able to sift through large volumes of data in order to make decisions while minimising human intervention. As for its fields of application, Machine Learning is now remarkably pervasive, with applications ranging from business intelligence to national security, from the analysis of biochemical interactions to the structural monitoring of ageing bridges, and more.
With this in mind, the course leaders designed the Mini Master in Machine Learning to give trainees a complete training course that meets international standards. The first expected outcome is to equip you to master the most effective Machine Learning techniques, so that you can practise implementing them and getting them to work on your own. More importantly, you will not only discover the theoretical foundations of learning, but also acquire the practical know-how needed to apply the techniques presented effectively to new problems in real-world situations.
The course also aims to present the details needed to understand Machine Learning effectively and to build knowledge from data of many different kinds. The topics covered include:
- Supervised learning (parametric/non-parametric algorithms);
- Unsupervised learning (clustering, dimensionality reduction, recommender systems);
- Best practices in Machine Learning (bias/variance theory, innovation process and AI).
The course also draws on numerous case studies and applications, so that the algorithms can be applied to new problems such as text classification.
- Develop a clear idea of what learning models from data involves;
- Master the concepts behind a wide variety of Machine Learning algorithms;
- Model classic and complex data and apply the algorithms to a real problem;
- Extract knowledge from the algorithms and results derived from your data;
- Be able to evaluate the models generated from the data;
- Choose between several approaches and train models;
- Optimise models according to the expected results (precision, reliability, accuracy, etc.).
The Big Data analytics market is growing worldwide, and this strong growth translates into an excellent opportunity for all IT professionals. Hiring managers are looking for certified Big Data Hadoop and Spark professionals. Our Machine Learning course helps you seize this opportunity and accelerate your career. Our Big Data Hadoop and Spark course can be taken by professionals and beginners alike. It is best suited to:
- Big Data enthusiasts
- Software developers, project managers
- Software architects
- BI consultant
- Data engineers
- Data Analyst
- Data scientist
- DBAs and database professionals
- IT engineers
- Graduates looking to build a career in Big Data
What our clients say about us
Youssef Harrak
Harrak is a seasoned data scientist specialising in machine learning models for predictive analytics. An expert in statistical analysis, he identifies trends in large datasets to guide business strategy.
Mohammed El Amrani
Mohammed is a data scientist and an expert in NLP and deep learning. He combines linguistics and machine learning to extract insights from text data, and is passionate about innovative solutions for language understanding and generation.
Sanaa Bouazza
Bouazza, a data analyst specialising in Business Intelligence (BI) and reporting. An expert in Tableau and Power BI, she turns complex data into user-friendly visualisations. Amily communicates trends effectively, enabling informed decisions for organisational success.
Karim El Mansouri
El Mansouri is an experienced data engineer in cloud and DevOps. An expert in AWS and Azure, he designs scalable data solutions. Focused on automation, Karim ensures efficient data delivery in the cloud.
Achraf Imrani
Achraf Imrani is a data analyst specialising in marketing and customer segmentation. An expert in extracting insights, he optimises campaigns for effective customer engagement.
Soufiane Tabrikt
Tabrikt is an experienced data engineer in big data processing and distributed systems. An expert in Apache Hadoop and Spark, Soufiane designs scalable architectures. His key role in building robust data infrastructures meets the varied and complex needs of organisations.
They trust us
Next Data is an IT, Data and Digital HR centre: recruitment, sourcing, training and consulting for IT, data and digital professions. We are drivers of performance, placing people at the heart of our success. What matters is not so much what we do as how we do it. In addition to our core activities, we also offer training courses to help our partners and employees develop their skills.
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