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.

Handle complex data, build R packages and libraries.
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

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


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.    

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

Learn how to build, train and apply fully connected neural networks.

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.
 

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
 

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

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.
● 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

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.
 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! 

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) 

This 15-week bootcamp in data science education includes theoretical and practical applications. Each week, students have 7 hours of lectures and 3 office hours. During office hours, students can practice what they have learned and work on projects that have been assigned to them. Instructors are available during these office hours to answer questions and provide guidance.
The course curriculum includes job interview studies and CV preparation guidance. Students will attend several meetup events about job interview questions during the course, organized by MBecome a Full-Stack Data Scientist in 15 weeks.
Explore 4 phased Full-Stack Data Science Bootcamp:
SQL, Python, Statistics Phase:
There are fundamental skills that you need to acquire to be a good data scientist. This includes being able to retrieve, analyse, interpret, present and organize data. In this phase, the bootcamp content is designed to make sure that you master these fundamental skills: SQL, the standard language to communicate with database, Python, the most widely used programming language (one third of new software development uses this language), and Statistics. This bootcamp provides theoretical knowledge and homeworks, hands-on office hours where you can master the curriculum. The bootcamp schedule lets you acquire theory, and practice what you’ve learned with office hours.
Fundamentals of Machine Learning Phase:
In the second phase of the bootcamp, you will learn data preprocessing such as handling missing values, standardization, normalization, and feature scaling. You will learn the popular machine learning concepts such as clustering, classification, regression, ensembling, and dimensionality reduction. You will get your hands on  training data, to discover potentially predictive pattern. You will learn how to train algorithms with training data, and to predict the outcome for future datasets. You will also learn about cross-validation, a technique to prevent overtraining. In this phase you’ll utilize modern data science techniques with industry experts. This phase will help you get started in data science career and become ready to machine learning applications.
Natural Language Processing Phase – Cloud (Spark, AWS, Azure):
NLP  is one of the most challenging and revolutionary areas of AI. This phase will start teaching you the basics of NLP, and move to the main concepts. You will learn the latest frameworks including NLTK, TextBlob, Gensim, SpaCy, Keras, and Tensorflow. With that, you’ll get comfortable with text representation/processing/understanding, choosing efficient ML algorithms for NLP, embedding spaces and word vectors. In addition to machine learning, you will get a chance to practice a bit of Deep Learning in the context of NLP.
Landing a Job Phase (Projects, resume building, mock interviews) :
You are being acquired many theoretical knowledge and had a chance to apply those with many homework and practices. Now, it’s time to challenge yourself with real projects and see how data science is being applied in the industry. In this phase, you are working on real-time projects where you will master the whole bootcamp content. You will be placed in the work groups and have a mentor. Your mentor will track your progress, advice for your weaknesses and guide you to land your dream job. You will also build a portfolio which is necessary for your job placement. With the help of resume building and mock interviews, you will become an open target for the headhunters. agnimind Academy.

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)

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.

Magnimind Academy