Data Science with Python+ ML+ NLP
Objective
The Data Science Course with Python, ML and NLP captures in details the Python programming, basic statistics. Through this training, you will gain knowledge in Data Analysis, Advanced Statistics Machine Learning, Reinforcement Learning, Data Visualization with Tableau,Tensorboard, web scraping, and Natural Language Processing. Upon course completion, you will master the essential tools of Data Science with Python.
Benefits
Data Science is an evolving field and Python has become a required skill for 46-percent of jobs in Data Science. The demand for Data Science professionals will grow an estimated 1581-percent by 2020 and professionals with Python skills will have an additional advantage.
Eligibility
The demand for Data Science professionals has surged, making this course well-suited for participants at all levels of experience. This Data Science with Python training is beneficial for analytics professionals willing to work with Python, software and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science.
Pre-requisites
To best understand the Data Science with Python course, it is recommended that you begin with the courses including, Python Basics, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science which is available in You Tube.
To run Python, your system must fulfil the following basic requirements:
•32 or 64-bit Operating System
•1GB RAM
The instruction uses Anaconda and Jupyter notebooks.
REGISTER
GROUP OF 3 OR MORE [USD 800 PER PARTICIPANT]
INDIVIDUAL STANDARD PRICE [USD 1000 PER PARTICIPANT]
Course Features
- Lectures 84
- Quizzes 0
- Language English
- Students 95
- Certificate No
- Assessments Yes
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Python and Basic Stats
- Intro to Machine Learning
- Basic installation of python
- Numpy; Pandas; Reading external dataframes
- Indexing a matrix;
- Selection Techniques;
- Introduction to Visualization; Matplotlib,seaborn and Plotly;
- Hands on visualization techniques (comparing plots);
- Numpy, Pandas Lab exercise;
- Descriptive Statistics;
- Introduction and Importance of statistics;
- Case Study
- Practice Project
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Advanced Statistics
- Intro to Statistics;
- Big Data;
- 4 pillars of Statistics; Frequency Distribution & Plots;
- Central Tendency(Mean, Median, Mode); Probability;
- Bayes Theorem; Probability Distribution;
- Binomial Distribution with Python
- Poisson Distribution
- Normal Distribution and exercise using Excel
- Normal Distribution using Python
- Hypothesis Testing
- Exercise on Hypothesis Testing
- Hypothesis Testing Mathematically and using Python
- Linear Algebra
- Anova
- Chi square test
- z test
- No Content Release- Project Release
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Supervised Learning
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Ensembling Methods
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Quarterly Program Break for completing submission & No Session
No items in this section -
Unsupervised Learning
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Featurization& Model Tuning
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Recommendation Systems
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ML Program Break & No Session
No items in this section -
Pre- Work for DL
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Intro to Neural Networks
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Computer Vision
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Natural Language Processing
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Visualization using TensorBoard
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Capstone Project
