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Top Skills You Will Learn

Data Science using Python,AI & Deep Learning with Python and TensorFlow,AWS – Amazon Web Services,DevOps,Big Data Hadoop

Job Opportunities

Data Analyst, Data Scientist, Data Engineer, Product Analyst, Machine Learning Engineer, Decision Scientist

Who is this program for?

Data Science using Python

➢ Life Cycle of Data Science
➢ Skills required for Data Science
➢ Careers Path in Data Science
➢ Applications of Data Science

➢ Relationship between Statistics and Data Science
➢ Introduction to Data
➢ Descriptive Statistics
➢ Inferential Statistics
➢ Random Sampling and Probability Distribution

➢ Python programming

➢ Python for Exploratory Data Analysis

➢ Introduction to RDBMS
➢ Retrieving
➢ Updating
➢ Inserting
➢ Deleting
➢ Sorting AND Filtering
➢ Summarizing AND Grouping
➢ Using Subqueries
➢ Joining Tables
➢ Views
➢ Stored Procedure
➢ Python Database ConnectionAPI

➢ Introduction To Machine Learning

➢  Text preprocessing using Bag of words technique
➢ TF(Term Frequency)
➢ IDF(Inverse Document Frequency)
➢ Normalization
➢ Vectorization
➢ NLP with Python

Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans.
For example, speech recognition, problem-solving, learning and planning.

Since its inception, AI has demonstrated unprecedented growth. Sophia the AI Robot, is the quintessential example of this.
The future of Artificial intelligence is hazy. But going by the bounds of progress AI has been making, it is clear AI will permeate every sphere of our life.
Listed below are the diverse ways in which AI can change in the future.

• Breakthrough in Science • Cyber Security • Face Recognition • Data Analysis • Transport • Various Jobs • Emotion Bots • Marketing & Advertising

AI & Deep Learning with Python and TensorFlow

  • Introduction Deep Learning
  • Life Cycle of Deep Learning
  • Skills required for Deep Learning
  • Careers Path in Deep Learning
  • Applications of Deep Learning
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning
  • Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
  • Introduction to Data:
    • Data types
    • Data Collection Techniques
  • Descriptive Statistics:
    • Measures of Central Tendency
    • Measures of Dispersion
    • Measures of Skewness and Kurtosis
    • Visualization
  • Inferential Statistics:
    • Sampling variability and Central Limit Theorem
    • Confidence Interval for Mean
    • Hypothesis ,t- Test,F-Test,Chi-square Test
    • ANOVA
  • Random Sampling and Probability Distribution:
    • Probability and Limitations,Discrete Probability,Continuous Probability
    • Binomial, Poisson Distributions,Normal Distribution
  • Environment Setup
  • Jupyter Notebook Overview
  • Data types:Numbers,Strings,Printing,Lists,Dictionaries,Booleans,Tuples ,Sets
  • Comparison Operators
  • if,elif, else Statements
  • Loops:for Loops,while Loops
  • range()
  • list comprehension
  • functions
  • lambda expressions
  • map and filter
  • methods
  • Programming Exercises.
  • Object-Oriented Programming
  • Modules and packages
  • Errors and Exception Handling
  • Python Decorators
  • Python generators
  • Collections
  • Regular Expression

Python for Exploratory Data Analysis – NumPy

  • Installing numpy
  • Using numpy
  • NumPy arrays
  • Creating numpy arrays from python list
  • Creating arrays using built in methods(arrange(),zeros(),ones(),linspace(),eye(),rand(),etc.
  • Array attributes :shape, type
  • Array methods: Reshape(),min(),max(),argmax(),argmin(),etc.

Python for Exploratory Data Analysis – Pandas

  • Introduction to Pandas
  • Series
  • DataFrames
  • Missing Data
  • GroupBy
  • Merging, Joining and Concatenating
  • Operations
  • Data Input and Output

Python for Data Visualization – Matplotlib

  • Installing Matplotlib,Basic Matplotlib commands
  • Creating Multiplot on same canvas
  • Object Oriented Method:figure(),plot(),add_axes(),subplots(),etc.
  • MatplotlibExercise

Python for Data Visualization – Seaborn and Panda

  • Categorical plot
  • Distribution plot
  • Regression plot
  • Seaborn Exercise
  • Installation
  • Creating Your First Graph and Running It in a Session
  • Managing Graphs
  • Lifecycle of a Node Value
  • Linear Regression with TensorFlow
  • Implementing Gradient Descent
  • Feeding Data to the Training Algorithm
  • Saving and Restoring Models
  • Visualizing the Graph and Training Curves Using TensorBoard
  • Name Scopes, Modularity
  • Sharing Variables
  • From Biological to Artificial Neurons
  • Training an MLP with TensorFlow’s High-Level API
  • Training a DNN Using Plain TensorFlow
  • Fine-Tuning Neural Network Hyperparameters
  • Vanishing / Exploding Gradients Problems
  • Reusing Pretrained Layers
  • Faster Optimizers
  • Avoiding Overfitting Through Regularization
  • Practical Guidelines
  • The Architecture of the Visual Cortex
  • Convolutional Layer
  • Pooling Layer
  • CNN Architectures
  • Recurrent Neurons
  • Basic RNNs in TensorFlow
  • Training RNNs, Deep RNNs
  • LSTM Cell
  • GRU Cell
  • Natural Language Processing
  • Efficient Data Representations
  • Performing PCA with an Undercomplete Linear Autoencoder
  • Stacked Autoencoders
  • Unsupervised Pretraining Using Stacked Autoencoders
  • DenoisingAutoencoders
  • Sparse Autoencoders
  • Variational Autoencoders
  •  
  • Learning to Optimize RewardsPolicy Search
  • Introduction to OpenAI Gym
  • Neural Network Policies
  • Evaluating Actions: The Credit Assignment Problem, Policy Gradients, Markov Decision Processes, Temporal Difference Learning and Q-Learning, Learning to Play Ms. Pac-Man Using Deep Q-Learning

Keras API

  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras

TFLearn API

  • Define TFLearn
  • Composing Models in TFLearn
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFLearn
  • Customizing the Training Process
  • Using TensorBoard with TFLearn
  • Use-Case Implementation with TFLearn

Keras API

  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras

TFLearn API

  • Define TFLearn
  • Composing Models in TFLearn
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFLearn
  • Customizing the Training Process
  • Using TensorBoard with TFLearn
  • Use-Case Implementation with TFLearn

Final Project, The topics and scenario according to current trending subjects

Programming Languages and Tools Covered

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