Magnimind

Project/Mentorship Bootcamp that will help launch your data science career. You will work on a real project with a professional data scientist, prepare for interviews, polish your resume, and receive valuable post interview feedback. The flexible schedule of the bootcamp program lasts seven weeks with office space provided by Magnimind so that you can work on-site. Are you ready to start planning for your new career? Do you want to learn more? How the mentor guided training - project building program works? Students will be assigned to at least two mentors and they will be given a project. Each student is expected to work 15 hours a week on their project and to also meet with at least two mentors. Mentors from industry leaders You will review concepts and get project feedback during your weekly mentor check-in and during onsite workshops. Your mentor will also help you with career planning, job search advice, and interview tips. When you have completed your interview, we will discuss the results as needed and the best ways to follow up. Resume and profile review Get expert eyes on your resume and project portfolio. We’ll work with you until they’re perfect. Job search support You will get personalized career coaching from the Magnimind team. Learn how to reach out to companies effectively and connect with Magnimind hiring partners. Is this program a perfect fit for you? • You have covered the basic theory behind machine learning or data analysis and feel a lack of support for further progress. • You're planning to look for a job within the next year. • You're able to commit to 1-on-1 meetings, carry out assigned homework, and then follow steps in pursuit of a job in your new career. The project will teach following in general: ● NLP pipepline (online for sentiment projects) ● Data Feature engineering ● Simple prediction models (LR) ● Evaluations Metrics ● Deep learning, neural network models.

The Magnimind Academy Cyber Security program is a 6-month online bootcamp designed to prepare you for landing your first job at the front lines of digital defense under the guidance of top-notch security experts in the Silicon Valley and Europe. The projects in the program are the key to success and help you to learn by getting your hands dirty. Uniquely created contents, live sessions, and exercises are just a few important things that you will find in this program. This program will be broken up into four teaching modules with an additional fifth module for career coaching. The following outline presents the topics to be covered in each module. Introduction to Cybersecurity and Onboarding Network Cyber attacks Cyber Security CompTIA Exam preparation Career Coaching There are no prerequisites for this course in the Magnimind curriculum, however, students are encouraged to have a familiarity with basic information technology concepts.

Thinking about starting a boot camp or a new field and unsure of your background or level of technical skills? We’ve got you covered. Join our Data Analysis with Python Mini Bootcamp where you’ll learn the necessary prerequisite skills while learning basic data science. This boot camp is for non-coders who are interested in learning data science or those who have a technical background although are not familiar with the fundamentals of data science. Data Analysis with Python Mini Bootcamp will teach you the fundamental skills of Python, SQL, Statistics and an introduction to machine learning. With those skills, you will be ready to take any kind of data science boot-camp or you’ll be ready to start learning data science on your own. During our prep course you will explore a hands-on experience that will ignite your enthusiasm and confidence to learn data science and its’ theory. What will you learn? ● Week 1 : 1. Review Python common functionalities and data structures used in data science 2. Learn the important Python libraries in data science (Pandas, Numpy, Matplotlib) ● Week 2: 1. Read and write data from/to different formats (excel, csv, text, etc.) 2. Cleanse and select important records from Dataframes 3. Deal with missing data: identify, replace, and eliminate records 4. Sort Dataframes by multiple columns ● Week 3: 1. Leverage the functions apply, lambda, filter, and map 2. Merge/Join Dataframes by foreign keys 3. Learn pivot tables in Pandas ● Week 4: 1. Learn data visualizations with the libraries Matplotlib and Seaborn 2. Introduction to the Machine Learning library Sklearn 3. Apply linear and logistic regression with Sklearn

Whether you’re training machine learning algorithms or performing a complex Analysis of data using statistical techniques the Quality and Quantity of your data determines the performance of your ML Model. Today, organizations have a difficult time working with huge amount of datasets such as IoT, Click Stream, Mobile and Sensor Data etc.., In addition, big data processing and analyzing needs to be done in real time to gain valuable insights quickly. This is where Distributed Machine Learning comes in. Understanding how to extract, process and analyze such huge amount of data will only become an ever more important skill for any data analyst / data scientist. This training course is for you because… ● You are an aspiring or beginning data scientist/engineer. ● You have a comfortable intermediate-level knowledge of Python/Java/Scala and a very basic familiarity with statistics and linear algebra. ● You are a working programmer or student who is motivated to expand your skills to include machine learning and BigData. ● You have some familiarity with the fundamentals of machine learning or have taken the Beginning Machine Learning. Prerequisites ● A first course in Python and/or working experience as a programmer ● College level basic mathematics ● Recommended: Attend or view Beginning Machine Learning What will you learn? ● Fundamentals of Apache Spark and Machine Learning ● Analyzing massive amounts of data using Spark SQL ● Learning different Data Quality and Data Cleaning techniques ● Learning how to implement Spark ML pipelines. ● Hands on experience with some Kaggle Projects

A 15-week data science bootcamp in Santa Clara, California. Students will learn core data science skills such as Python, SQL, Probability and Statistics, Linear Algebra, and Data Visualization.

In this Introduction to Python class, you will learn to code in Python which is one of the most popular programming languages as of today. Students will learn the fundamentals of Python so they can develop scripts to automate routine work and scale large-load of work. By the end of this course, you will learn common coding skills in Python, and acquire knowledge of its data structures, modules, functions, input/output, exceptions, and the basics of the object-oriented methodologies. You will learn to develop, test, debug, and improve your coding skills in Python. The following topics will be covered in the class: Variables and strings manipulations. Lists, Sets, Dictionaries, and Tuples. Sorting functions. If statements, for and while loops. Opening, reading, and writing files. Functions and modules. Functions with variable number of arguments. Exception handlings and Assertion. Lambda function and List comprehension. The modules math, datetime, os, sys, and urllib. Regular expressions module (re). Object Oriented Programming in Python.

Machine Learning Core Principles and Application Mini Bootcamp will help you get started in the data science career. This program is good for different professionals who want to utilize modern data science techniques in their jobs to improve their productivity as wells as for people who would like to switch to Data Science career with a better salary. What you’ll learn ✓ Use Python for Data Science and Machine Learning ✓ Implement Machine Learning Algorithms ✓ Make predictions using linear regression, polynomial regression, and multivariate regression ✓ Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA ✓ Use SciKit-Learn for Machine Learning Tasks ✓ Use train/test and K-Fold cross-validation to choose and tune your models ✓ Learn to use Pandas for Data Analysis ✓ Learn to use Matplotlib for Python Plotting Requirements Students should have basic college-level mathematics Simple programming skills (python is preferred) What will you learn? ● Data Preprocessing ● Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression ● Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification ● Clustering: K-Means, Hierarchical Clustering

We are expanding to offer a Bootcamp on Machine Learning Interview tactics and training. You don’t want to miss this opportunity especially if you have been preparing for interviews for Machine Learning Engineering jobs and would like to stand out from the crowd. This Bootcamp will be given by the talented Google Engineer, Osman Aka, who immerses himself in machine learning training. We will cover several interview questions at various levels. It will be interactive and you will have a great sense of actual interview questions and different approaches for answering them. You will have guidance from our mentors and participate in mock interviews which you will receive valuable feedback from at the end of the Bootcamp. Make your first impression your best impression at every interview!

What will you learn? ● Module 1 – Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning ● Machine Learning Languages, Types, and Examples ● Machine Learning vs Statistical Modelling ● Supervised vs Unsupervised Learning ● Supervised Learning Classification ● Unsupervised Learning ● Module 2 – Supervised Learning I ● K-Nearest Neighbors ● Decision Trees ● Random Forests ● Reliability of Random Forests ● Advantages & Disadvantages of Decision Trees ● Module 3 – Supervised Learning II ● Regression Algorithms ● Model Evaluation ● Model Evaluation: Overfitting & Underfitting ● Understanding Different Evaluation Models ● Module 4 – Unsupervised Learning ● K-Means Clustering plus Advantages & Disadvantages ● Hierarchical Clustering plus Advantages & Disadvantages ● Measuring the Distances Between Clusters – Single Linkage Clustering ● Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering ● Density-Based Clustering ● Module 5 – Dimensionality Reduction & Collaborative Filtering ● Dimensionality Reduction: Feature Extraction & Selection ● Collaborative Filtering & Its Challenges

In this bootcamp, we will cover how natural language processing (NLP) provides a foundation from which clustering, machine learning, classification, search engineering and graph databases make efficient sense of large volumes of data. We will bring together these various areas into a real world example to demonstrate the resulting synergy. We will define unstructured and structured data and how they form a holistic foundation from which to understand large amounts of complex data. We will then learn how unstructured text data is structured so that it can be analyzed and assimilated with structured data. We will then learn how collections of documents can be summarized, clustered and classified for a deeper, high-level understanding of the documents and concepts embedded in them. Next, we will explore search engineering which allows efficient exploration of collections of documents. Finally, we will see how graph databases can help bring diverse text data into a coherent linking of the data collection where visualization is leveraged to help glean insights. Who is this course for: Anyone who is interested in entering the world of NLP and do not know where to start. Anyone who wants an introduction to ML/Deep Learning and how to apply it to NLP Anyone who is interested in building NLP applications in Python Anyone who wants to understand how commonly used NLP applications are built Course Requirements: Having an interest in learning Natural Language Processing and data science is enough to take this course. This course has no prior coding experience requirement. What will you learn? ● Working with Test Data in Python ● Regular Expressions ● Text processing with NLTK and Spacy ● POS Tagging ● NER Tagging ● Text Normalization ● Stemming ● Lemmatization ● Topic Modeling ● Interpreting Patterns ● LDA ● LSA (Latent Semantic Analysis) ● Feature Engineering for Text ● Bag of Words ● TF-IDF ● SVD ● Word Embedding ● Identify Topics in Text ● Text Classification ● Deep Learning for NLP ● Project ● Auto. Tagging ● Article Categorize ● Social Media Info. Extract ● Spam Classification

This course is focused in practical approach with many examples and developing functional applications. This course starts explaining you, how to get the basic tools for coding and also making a review of the main machine learning concepts and algorithms. After that this course offers you an explanation of the main tools in NLP such as: Working with Test Data in Python, Text processing with NLTK and Spacy, Text Normalization, Model Building and finally developing NLP projects. Who this course is for: Aynone who is comfortable writing Python code, using loops, lists, dictionaries, etc. Anyone who want to learn more about machine learning Anyone with interest in Natural Language Processing and it’s applications Course Requirements: Basic knowledge in python coding Basic knowledge in Machine Learning Basic knowledge in linear algebra and probability What will you learn? ● Week 1: Introduction to the course and expectations What is NLP? Examples of NLP in the real world What to expect from this course? NLP Workflow Basics of language processing & understanding text Regular Expressions for text processing Scraping Websites NLTK and spaCy Tokens, Lemmas ● Week 2: Supervised Learning Models for Text Data Bag of Words with Classification Models ● Week 3: Unsupervised Learning Models for Text Data TFIDF Latent Semantic Analysis Dimension Reduction Unsupervised neural network Introduction to word2vec ● Week 4: Build your own NLP Model A hands-on project An introduction to Advanced NLP topics

The role of a data scientist is to turn raw data into actionable insights. To be an effective data scientist, you must know how to extract data from these databases using SQL. In this course you’ll learn the skills you need to extract critical insights in a database. Who this course is for: Anyone who wants to start a data analyst or data scientist role in professional life. Anyone interested in expanding data skills. Anyone who wants to start learning data science. Course Requirements: There are not prerequisites for this course. What will you learn? ● DB Basics ● Subqueries ● Aggregations ● Joins ● Rollups and Cubes ● Window Functions ● Transposing and Ranking Data ● Hierarchical Queries ● Analytical SQL Functions

Today, there’s a huge demand for data science expertise as more and more businesses apply it within their operations. Python offers the right mix of power, versatility, and support from its community to lead the way. While Python is most popular for data wrangling, visualization, general machine learning, deep learning and associated linear algebra (tensor and matrix operations), and web integration, its statistical modeling abilities are far less advertised. A large percentage of data scientists still use other special statistical languages such as R, MATLAB, or SAS over Python for their modeling and analysis. However, only by Python-based statistical modeling, one can build a powerful end-to-end data science pipeline (a complete flow extending from data acquisition to final business decision generation) using a single programming language. This bootcamp will teach fundamentals of statistical modeling concepts with easy-to-follow examples in Python to get you started in your data science journey. What will you learn? ● Descriptive statistics ● A bit of history, why statistics is ‘hot’ today, a few examples ● Central tendency, dispersion measures, other simple descriptive measures ● Bi-variate statistics, correlation, plotting ● Probability, random variables, probability distributions, ● Exploratory data analysis (EDA) example using descriptive stats ● Inferential statistics ● Estimation, inference, confidence interval ● Hypothesis testing, p-values, statistical significance ● Type-I/Type-II errors, nature of statistical learning, difference from ML ● Comparing means, t-test, ANOVA ● Application in machine learning/data science ● Linear regression, posing linear regression as statistical inference problem ● Logistic regression, inference ● Naive Bayes classification, maximum likelihood estimation ● K-means clustering, expectation-maximization (E-M), Gaussian mixture model (GMM)

Time series forecasting is an important part of machine learning that requires an additional effort to recognize the impact of the time-component of the problems, such as trends and seasonality. In this series of lectures, you will be introduced to basics of time series models in Python environment. Specifically, you will have gain hands-on experience on handling forecast models in four sessions. Who this course is for: Anyone who wants to be master in time series data with python Anyone who wants to become proficient in time series data analysis working with real life data People interested in applying machine learning techniques to time series data Course Requirements: Prior familiarity with the interface of Jupiter Notebooks Prior exposure to basic statistical techniques Be able to carry out data reading and pre-processing tasks such as data cleaning In python What will you learn? ● Week 1. Fundamentals ● Python Environment ● What is Time Series Forecasting? ● Time Series as Supervised Learning ● Load and Explore Time Series Data ● Data Visualization ● Resampling and Interpolation ● Power Transforms ● Moving Average Smoothing ● Week 2. Temporal Structure ● Introduction to White Noise ● Introduction to the Random Walk ● Decompose Time Series Data ● Use and Remove Trends ● Use and Remove Seasonality ● Stationarity in Time Series Data ● Week 3. Evaluate Models ● Backtest Forecast Models ● Forecasting Performance Measures ● Persistence Model for Forecasting ● Visualize Residual Forecast Errors ● Reframe Time Series Forecasting Problems ● Week 4. Forecast Models ● Introduction to the Box-Jenkins Method ● Autoregression Models for Forecasting ● Moving Average Models for Forecasting ● ARIMA Model for Forecasting ● Autocorrelation and Partial Autocorrelation ● Grid Search ARIMA Model Hyperparameters ● Save Models and Make Predictions ● Forecast Confidence Intervals