Magnimind Academy

Course Description:

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

Subjects:
Algorithms

Course Description:

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.

Subjects:
Machine Learning, Data Structures, Data Science

Course Description:

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

Subjects:
Data Analytics, SQL, Data Science

Course Description:

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)

Subjects:
Exploratory Testing, Python, Machine Learning, Data Science, Statistics & Probability

Course Description:

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

Subjects:
Jenkins, Python, Machine Learning, Data Visualization