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Core & Advanced Python:

• Introduction to Python
• Installation of Python and Ipython Notebook (Jupyter Notebook)
• Python Objects
• Number & Booleans
• Strings
• Mutability of objects
• Operators
• Operators Precedence
• Conditions(If else elif)
• Loops(While & for)
• Break and Continue statements
• Range functions
• String object basics
• String methods
• Splitting and Joining Strings
• String format functions
• list object basics
• list methods
• List as stack and Queues
• List comprehensions
• Tuples, Sets, Bool
• Dictionary Object basics
• Dictionary Object methods
• Dictionary View Objects
• Functions basics
• Parameter passing
• Iterators, Generator functions
• Lambda functions
• Map, Reduce, filter functions
• OOPS basic concepts
• Creating classes and Objects
• Inheritance
• Working with files
• Reading and writing files
• Buffered read and write
• Other File methods
• Using Standard Module
• Creating new modules
• Exceptions Handling- Try-except
• inserting and retrieving Table
• Updating and deleting the data
• Sending email
• GUI Programming
• Testing

Statistics:

• Descriptive Statistics
• Sample vs Population statistics
• Random Variables
• Probability distribution function
• Expected value
• Binomial Distribution
• Normal Disrtributions
• z-score
• Central limit Theoram
• Hypothesis testing
• Z-Stats vs T-stats
• Type 1 type 2 error
• confidence interval
• Chi Square test
• ANOVA test
• F-stats

Data Analysis:

• Numpy variable
• Numpy manipulation
• Scipy
• Pandas intro
• Descriptive analysis
• Pandas Input-output
• Pandas manipulation
• Pandas groupby
• Matplotlib intro
• Bar charts histogram
• Scatter plot
• Stack charts
• Legend title Style
• Data Cleaning walkthrough
• Combining multiple datasets to get a single and clean dataset

Machine Learning:

• Introduction
• Supervised
• Unsupervised
• Reinforcement
• Train & Test
• Validation Split
• Performance
• Overfitting & underfitting
• Linear Regression
• Assumptions
• R square adjusted R square
• Intro to Scikit learn
• Training methodology
• Logistics regression
• Precision Recall
• ROC curve
• F-Score
• Decision Tree
• Cross Validation
• Bias vs Variance
• Ensemble approach
• Bagging Boosting
• Randon Forest
• Variable Importance
• XGBoost
• K Nearest Neighbor
• Lazy learners
• Curse of Dimensionality
• KNN Issues
• Text Analytics
• Tokenizing
• Chunking
• Document term Matrix
• TFIDF
• Sentiment analysis hands on
• Hierarchical clustering
• K-Means
• Performance measurement
• Principal Component analysis
• Dimesionality reduction
• Factor Analysis
• Time Series Forecasting
• Moving Average
• ARIMA model

Deep Learning:

• Basic of Neural Network
• NLTK
• Type of NN
• Cost Function
• Gradient descent
• Linear Algebra basics
• Vanilla implementation of Neural Network in python
• Tensorflow basics
• Hands on Simple NN with Tensorflow
• Word Embedding
• CBOW & Skip-gram
• Word Relations
• Convolutional Neural Network
• Maxpool
• Window padding
• Image classification using Convolutional Neural Network
• Recurrent Neural Network
• Long Short Term Memory (LSTM) architecture
• Building Story writer using character level RNN
• Sentiment Analysis Hands on Seq-to-Seq model
• Encoder Decoder
• GAN
• Generative Model Using GAN
• Semi-supervised learning using GAN
• Restricted Boltzmann Machine(RBM) and Autoencoders
• Projects:
  1. Spam Detection
  2. Image Classification

Artificial Intelligence (AI):

• Why do we need to study AI?
• Applications of AI
• Branches of AI
• Defining intelligence using Turing Test
• Making machines think like humans
• Building rational agents
• General Problem Solver
• Building an intelligent agent
• Understanding deep learning
• Understanding neural networks with TensorFlow
• deep dive understanding neural network with tensorflow
• master deep networks
• Convolutional neural network
• recurrent neural networks
• rbm and autoencoder
• Keras
  1. Define Keras
  2. Sequential Composition
  3. Functional Composition
  4. Predefined Neural Network Layers
  5. What is Batch Normalization
  6. Customizing the Training Process
  7. Using TensorBoard with Keras
• TFLearn
  1. Define TFlearn
  2. Composing Models in TFlearn
  3. Sequential Composition
  4. Functional Composition
  5. Hands-On Practice
• Projects:
  1. Chatbot Applications

Raspberry Pi Basics (IoT):

• Introduction to Raspberry PI
• Hardware
• Ingredients
• Introduction to NOOBS
• SD Cards
• NOOBS for Raspbian
• SD Card Formatter
• Downloading & Installing NOOBS
• Preparing & Installing Raspberry PI
• Connecting The Raspberry PI
• Basic Networking
• Shutting Down
• Led Programming
• Switch and Led Programming
• Interfacing Relay
• Motor Interfacing
• Motor Driver ICs
• Relay Drivers ICs
• Other Peripheral Interfacing
• Projects:
  1. Device Controlling using voice command

Introduction to Sixth Sense Technology:

• What is sixth sense?
• Why choose sixth sense technology?

Applications:

• Predicting future, Chat Bots, SelfDriving Cars, Google AI Eye Doctor
• AI Music Composer
• AI Dream Machine