AI & Deep Learning Python

Artificial Intelligence is the facility that enables a machine to have intelligence of its own. An ability to think, learn, plan, organize, reasoning etc. are some of the skills that when combined forms artificial intelligence. There are various methods in which a Machine gets to do this and among them Machine Learning and Artificial Neural Networks are two of them. Machine Learning is the ability of a machine to learn from its previous experiences using algorithms and these algorithms have the ability to alter themselves based on previous experiences. A machine’s ability to recognize, understand and process audio and vision is made possible by deep learning using the artificial neural networks which was inspired from the human brain. SMEClabs gives you the opportunity to learn the whole technology as one program.

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Key Highlights

Training in AI & Deep Learning Python is simply briefed as follows:

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Minimum Eligibility

Why Take This AI & Deep Learning Python Program?

Top Skills You Will Learn

This SMEClabs AI & Deep Learning Python course on ‘Artificial Intelligence’ covers all the basics of neural network-based models. Get a conceptual understanding of learning mechanisms such as Need and history of neural networks, gradient, forward propagation, loss functions, and its implementation using python libraries. Learn some essential concepts related to deep neural networks that also work with Google’s powerful library Tensorflow which comes with pre-built Keras. We will be covering all theoretical and practical aspects of AI & Deep Learning Python, in-depth. AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly. This best AI & Deep Learning Python online course in SMEClabs will enable you to take the first step toward solving important real-world problems and future-proofing your career.

Artificial Intelligence has been around for over half a century now and its advancements are growing at an exponential rate. The demand for AI is at its peak and if you wish to learn about Artificial Intelligence, you’ve landed at the right place. This blog on Artificial Intelligence With Python will help you understand all the concepts of AI with practical implementations in Python.

The following topics will be cleared  in this AI & Deep Learning Python online course:

  • Why Is Python Best For AI?
  • Demand For AI
  • What Is Artificial Intelligence?
  • Types Of Artificial Intelligence
  • Machine Learning Basics
  • Types Of Machine Learning
  • Types Of Problems Solved By Using Machine Learning
  • Machine Learning Process
  • Machine Learning With Python
  • Limitations Of Machine Learning
  • Why Deep Learning?
  • How Deep Learning Works?
  • What Is Deep Learning?
  • Deep Learning Use Case
  • Perceptrons
  • Multilayer Perceptrons
  • Deep Learning With Python
  • Introduction To Natural Language Processing (NLP)
  • NLP Applications
  • Terminologies In NLP
We provide this AI & Deep Learning Python Training in the following locations:

Thiruvananthapuram, Calicut, Kottayam, Cochin, Chennai, Coimbatore, Nagarcoil, Mangalore, Bangalore, Vishakapatnam, Hyderabad, Mumbai, Thane, Delhi, Sharjah, Abu Dhabi, Dubai, Fujairah, Singapore.

AI & Deep Learning Python Syllabus:
  • 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
  • HDLs : VHDL and Verilog HDL
  • HVL : System Verilog and Assertions
  • VLSI CAD Tools
  • Synthesis of Digital Systems
  • System level design and modelling
  • 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
  • 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.
  • Introduction to Pandas
  • Series
  • DataFrames
  • Missing Data
  • GroupBy
  • Merging, Joining and Concatenating
  • Operations
  • Data Input and Output
  • Installing Matplotlib,Basic Matplotlib commands
  • Creating Multiplot on same canvas
  • Object Oriented Method:figure(),plot(),add_axes(),subplots(),etc.
  • MatplotlibExercise
    • Categorical plot
    • Distribution plot
    • Regression plot
    • Seaborn Exercise

    Pandas built in visualization:

  • 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
  • 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
  • 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

Detailed Syllabus

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