5 Months
Duration
Physical/Online
Classes
Weekday/Weekend
Mon, Wed & Fri (Saturdays)
10am - 1pm (WAT)
Time
AI Projects We Will Build
In addition to the artificial intelligence projects below, you will be given resources to build others independently.
Object Detection
Using computer vision to detect face, body movement or hand gestures.
Video Analysis
Analyze football/tennis match, fire detection or car parking lot analysis using computer vision.
Sentiment Analysis
Use natural language processing to analyze text, review or spam detection
Loan repayment
Predicting if a customer will repay a loan or not using machine learning.
Disease detection
Breast cancer detection using computer vision.
Recommendation
Recommend similar movie or product to customers.
** The projects can be changed anytime
Training Benefits
Here are some of the benefits of learning at Tech365
- Beginner-friendly
- Project-based training
- Excellent support
- Job alerts
- Career guidance and CV review
- Certificate of completion
- Flexible payment plan
- Conducive learning environment
- Unlimited internet access
- Remote Internship with top firms
Artificial Intelligence Outline
Learn how to create artificial intelligence solutions using deep learning, computer vision and natural language processing (NLP).
ARTIFICIAL INTELLIGENCE
- Introduction to Artificial Intelligence
- Definition and history of AI
- Types of AI (Narrow AI vs. General AI)
- AI vs. Machine Learning vs. Deep Learning
- Current state and future trends in AI
- Ethical considerations in AI development
DEEP LEARNING
- Introduction To Deep Learning
- What is Artificial Neural Network?
- Neurons and Perceptrons
- Machine Learning vs Deep Learning
- Why Deep Learning
- Applications of Deep Learning
Artificial Neural Network
- Neural Network: An Overview
- Architecture: Components of the Perceptron
- Fully Connected Neural Network
- Types of Neural Networks
- How Neural Networks work
- Propagation: Forward and Back Propagation
- Chain Rule in Backpropagation
- Optimizers In NN
Activation Functions
- Introduction to Activation Functions
- Sigmoid Activation Function
- Vanishing Gradient
- TanH Activation Function
- ReLU Activation Function
- Leaky ReLU Activation Function
- ELU Activation Function
- SoftMax Activation Function
TensorFlow and Keras
- Introduction to TensorFlow
- Tensors and Dataflows in Tensorflow
- Tensorflow Versions
- Keras
Building Neural Network
- Building your first neural network
- Handling Overfitting in Neural Network
- Building Neural Network with MNIST
COMPUTER VISION
- Working with Images
- The concept of Pixels
- Gray-Scale Image
- Color Image
- Different Image formats
- Image Transformation: Filtering
- Affine and Projective Transformation
- Image Feature Extraction
CPU vs GPU vs TPU
- Introduction to CPUs, GPUs and TPUs
- Accessing GPUs for Deep Learning
- CPU vs GPU speed
Transfer Learning
- Understanding Transfer Learning
- Steps to perform transfer learning
- When to use Transfer learning and when NOT to use.
Object Detection
- Computer Vision Task
- Datasets Powering Object Detection
- Image Classification vs Image Localization
- Challenges of Object Detection
Performance Metrics for Object Detection
- Intersection Over Union(IoU)
- Precision and Recall
- Mean Average Precision(mAP)
- Objection Detection Techniques
OPENCV
- Introduction To OpenCV
- OpenCV Installation
- OpenCV Setup
- Reading Images
- Reading Video
- Live Streaming with OpenCV
- Stacking Images together
- OpenCV Join
- OpenCV Functions
- Image Detection Techniques
- Edge Detection
- Dilation and Erode
OpenCV Conventions
- Adding Shapes
- Creating Lines
- Creating Shapes(Rectangle)
- Adding Text
- Warp Perspective
- IMAGE: Face Detection with OpenCV
- VIDEO: Face Detection with OpenCV
NATURAL LANGUAGE PROCESSING(NLP)
- What is NLP?
- Applications of NLP
- The Must-Know NLP Terminologies
- Word
- Tokens and Tokenizations
- Corpus
- Sentence and Document
- Vocabulary
- Stopwords
NLP: Text Pre-processing
- Tokenization with NLTK , SpaCy and Gensim
- Removing Stopwords with NLP Libraries
- Text Normalization
- Stemming and Lemmatization
Part Of Speech (POS) Tagging
- Understanding POS Tagging
- Chunking
- Frequency of Words
- Bi-Gram
- N-Grams
Introduction To Statistical NLP Techniques
- Bag of Words (BoW)
- TF-IDF
- Language Modeling
Word Embeddings
- Understanding Word Embeddings
- Feature Representations
Word2Vec
- The Challenge with BoW and TF-IDF
- CBOW and Skip-Gram
GloVe
- Understanding GloVe
- Sentence Parsing
- Chunking & Chinking & Syntax Tree
- Sequential Models
Recurrent Neural Network (RNN)
- What is a Recurrent Neural Network (RNN)
- Types of RNNs
- Use Cases of RNNs
- Vanilla Neural Network (NN) vs Recurrent Neural Network (RNN)
- Backpropagation Through Time (BTT)
- Mathematics Behind BTT
- Vanishing and Exploding Gradient
- The problem of Long Term Dependencies
- Bidirectional RNN (BRNN)
- Gated Recurrent Unit(GRU)
Sequence To Sequence Models (Seq2Seq)
- Introduction to sq2seq
- Encoder & Decoder
- Language Translation
Name Entity Recognition (NER)
- NER : An Introduction
- Example of Name Entity Recognition
- How Name Entity Recognition works
- Applications of NER
Other modules
Introduction to Data Science
Introduction and onboarding
- Introduction to data science
- Data analytics vs data science
- Application of data science in real-world
- Data science life cycle
- Data science vs machine learning vs artificial intelligence
- Data science expert areas
Introduction to Statistics
Introduction to statistics
- What is statistics
- Importance of statistics
- Application of statistics in data science
Understanding Data
- What is data
- Types of data
- Data attributes
- Data sources
- Structured and unstructured data
Statistical Methods
- Types Of Statistics
- Descriptive statistics
- Frequency Distribution
- Measures of Central Tendency
- Measure of dispersion
- Coefficient of variation
- Normal distribution
- Interquartile range
- Central Limit Theorem
- Correlation
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Probability
- Basic concepts of probability
- Probability expression
- Mutually Exclusive Events
- Independent Events
- Rules for Computing Probability
- P-Value
- Confidence Level
Bayes’ Theorem Hypothesis Testing
- Null Vs Alternative hypothesis
- One-tailed vs. two-tailed testing
- Type 1 and Type 2 errors
- Hypothesis Testing Process
Machine Learning
Understanding of how machine learning is used to make predictions and classifications and help businesses achieve more.
Getting Started with Machine Learning
- What is Machine learning?
- History and Evolution
- Machine learning workflow
- Machine learning vs programming vs AI
- Key concepts in machine learning
- Types of machine learning
- Advantages of machine learning
- Application of machine learning in real-world
- Supervised and unsupervised learning
Data and Its Processing:
- Introduction to Data in Machine Learning
- Understanding Data Processing
- Data Cleaning
- Label Encoding of datasets
- One Hot Encoding of Datasets
Naive Baye’s
- Key concepts of Naive bayes
- Baye’s theorem
- The “Naive” Assumption
- Algorithm steps
- Feature selection
- Advantages and limitations
- Real-world application
Supervised learning :
Linear regression
- Overview of linear regression
- Types of linear regression
- Mathematical representation
- Cost function
- Feature scaling
- Regularization
- Evaluation metrics
- Assumptions and limitations
Logistic Regression
- Overview of logistic regression
- The logistic function
- Model representation
- Decision boundary
- Cost function
- Evaluation metrics
- Feature engineering
- Real-world application
K-Nearest Neigbhiour (KNN)
- Overview of K-Nearest Neighbour
- The core concept of “Neigbour” in KNN
- Choosing K in KNN
- Algorithm steps
- Cross-validation
- Advantages and limitations
- Comparison with other algorithms
- Application of KNN
- Support Vector Machine
- Overview of support vector machine (SVM)
- Types of support vector machines
- Key components of support vector machine
- Hyperparameter
- Advantages and disadvantages
- Feature scaling
- Application of support vector machine
Decision Tree
- Key concepts of decision tree
- Types of Decision Tree Tree components
- Building decision tree
- Splitting criteria
- Hyperparameter
- Advantages and limitation
- Real-world application
Random Forest
- Overview of random forest
- Decision tree, bagging and random feature selection
- Algorithm steps
- Key parameters
- Ensemble methods
- Advantages and limitations
- Application of random forest
Unsupervised learning
- Overview of unsupervised learning
- Differences between supervised and unsupervised learning
- Clustering
- Dimesionality reduction
- Association rule
- Evaluation metrics
- Application of unsupervised learning
Introduction To Data Analytics and Excel
Participants will learn what data analytics is and how to analyze and visualize data using Microsoft Excel or WPS.
Understanding Data Analytics
- What data analytics?
- Benefits of data analytics.
- Data Analytics vs data science vs data engineering.
- Applications of Data Analytics
- Data analytics process and lifecycle.
- Reading CSV, tabbed and fixed column data
- Data manipulation
Formula basics
- Cell reference, formula and function?
- How to use addition, subtraction, multiplication, division, exponents in a formula
Formatting
- How to format cells using number, date and other options.
Statistics
- How to use the COUNT function
- Using the COUNTIF function
- Using the SUM and SUMIF function
- How to use the AVERAGE function
- How to calculate the minimum and maximum value
Lookups
- How to use VLOOKUP
- VLOOKUP vs Nested IFs
- How to use HLOOKUP
- Using the index function
- Using the match function
- XLOOKUP function
- SUMPRODUCT function
Pivot table
- What is a pivot table
- How to create a pivot table
- How to add fields to pivot tables
- How to rearrange fields in the pivot table
- How to adjust aggregate function in pivot table
- Styling pivot table
- How to add slicers
- How to filter pivot table
- How to sort pivot table
- How to group pivot table
Excel chart
- How to create a basic chart
- How to move and resize a chart in Excel
- How to edit and add to chart data
- How to add a title and legend to a chart
- How to change chart type
- How to work with pivot chart options
- How to filter pivot chart
Working with data and lists
- How to freeze rows
- How to quickly sort using one column in Excel
- How to sort using more than one column
- How to filter a list
- How to filter with multiple criteria
References
- Relative references
- Absolute reference
- Creating a reference to another sheet
Conditional formatting in Excel
- Using the if function
- How to create a formula with nested IFs
Working with Text
- How to join values with an ampersand or concatenating
- How to clean text with trim
- How to count characters
- Changing case in Excel
- How to extract text
Tasks: Analyzing sales data with Excel. Visualizing the data for better communication with stakeholders using pivot tables and pivot chart
SQL For Database Manipulation
Learn how to clean, query, manipulate, and aggregate data using SQL.
Introduction to SQL
- What is SQL?
- Create Table Statement
- ALTER TABLE Statements
- Rename Columns of a Table
- Modify Column DataType
- Drop Columns from Table
- Rename Tables
- Drop Tables
- What is Null Value?
DML Statements
- Insert Statement
- Update Statement
- Delete Statement
- Truncate Statement
- Merge Statement
SQL Functions
- AVG()
- COUNT()
- MAX()
- MIN()
- SUM()
Select Queries
- Select Query
- WHERE Clause
- GROUP BY Clause
- HAVING Clause
- ORDER BY Clause
SQL Joins
- Inner Join
- Left Join
- Right Join
SQL Operators
- BETWEEN
- IN
- LIKE
- INTERSECT
- MINUS
- UNION
- DISTINCT
- ANY
- ALL
Project: Use the knowledge gained to analyze data for a business.
Power BI For Business Intelligence
Understand how to create reports and dashboards to communicate your findings to stakeholders.
Introduction to PowerBI
- What is PowerBI?
- Installing Power BI
- Desktop
- Adjusting Settings in PowerBI
Connecting and shaping data
- Types of Data Connectors
- The Power BI Query Editor (Power Query)
- Basic Table Transformations
- Working with Numerical Values
- Working with Date & Time Tools
- Generating Index & Conditional Columns
- Grouping & Aggregating Records
- Merging Queries in
- Power BI Desktop
- Appending Queries
- Configuring Data Source Settings
- Defining Hierarchies
- Power BI Data Connection Best Practices
Table relationship and data model
- What is a “Data Model”?
- Principles of Database Normalization
- Understanding Data Tables vs. Lookup Tables
- Understanding Table
- Relationships vs. Merged Tables
- Creating Table Relationships
- Managing & Editing Table Relationships
- Connecting Multiple Data Tables
- Hiding Fields from the
- Power BI Report View
- Power BI Data Model Best Practices
Data Analysis Expressions (DAX)
- Introduction
- DAX Calculated Columns
- DAX Measures
- Adding Columns & DAX Measures
- Implicit vs. Explicit DAX Measures
- Filter Context Examples in Power BI
- Understanding DAX Syntax & Operators
- Common DAX Function
- Date & Time Functions
- Conditional & Logical Functions (IF/AND/OR)
- Common Text Functions
- Joining Data with RELATED
- Basic Math & Stats Functions
- COUNT Functions (COUNTA, DISTINCTCOUNT, COUNTROWS)
- CALCULATE, ALL & FILTER
- Iterator Functions (SUMX, RANKX)
- DAX Best Practices
Data Visualization and report
- Introduction
- Exploring the “Report”
- Adding Simple Objects to the Power BI Report Canvas
- Inserting Basic Charts & Visuals in Power BI
- Conditional Formatting
- Report Formatting Options
- Report Filtering Options
- Exploring Data with Matrix Visuals
- Filtering with Date Slicers
- Inserting Text Cards
- Visualizing Geospatial Data with Maps
- Visualizing Data with Treemaps
- Artificial Intelligence in PowerBI
Project: Designing of sales dashboard
Python Programming
We will focus on programming paradigms using Python. With this foundation, participants can understand how to write code to solve problems and perform automation tasks using Python.
- Overview of python
- Installation and setup
- Python Syntax
- Variables
- Reserved words
- Input and output
- Comments in python
Data Types
- Strings
- Lists
- Dictionary
- Tuple
- Sets
- Numbers
- Boolean
- None
Operators
- Arithmetic operators
- Comparison operators
- Logical operators
- Assignment operator
- Membership operator
- Identity operator
Control Flow
- If statement
- elif
Loops
- While loops
- For loops
- For in Loops
Functions
- In-built functions
- User-defined function
- Function parameter
- Lambda
- Map, filler, reduce
- *args and **kwargs
Modules
- Modular programming
- Python package
File Handling
- Reading, Writing and appending files
- Handling HTML and CSV Data
- Manipulating Data with Python
NumPy For Array Manipulation
Understand how to work with multi-dimensional arrays and integrate numpy with other libraries like Pandas and Matplotlib
Introduction to Numpy
- What is NumPy (Numerical Python)?
- Understanding dimensions
Numpy Basics
- Array Creation
- Array attributes
Random number generation
- Random sampling
- Random seeds
Array operation
- Indexing numpy array
- Slicing in NumPy
- Arithmetic operations
- Broadcasting
- Mathematical Functions in Numpy
- Sorting in numpy
- Using Axis
Pandas For Data Analysis
Hand-on data cleaning and analysis using Pandas. Can work with CSV, Excel, SQL and data from other sources.
Introduction to Pandas
- Importing pandas libary
- Creating a Pandas Series
- Creating a Pandas DataFrame
Pandas Methods
- Describe and info methods
- Head and tail method
- Apply method
- Unique and nunique method
- Sorting values
- Counting values
- Replacing values
- Deleting values
- Largest and lowest values
Manipulating data
- Dealing with Rows and Columns in Pandas
- Extracting rows
- Indexing and Selecting Data
- Conditional filtering
- Handling missing data
Grouping and merging data
- Grouping data
- Merging data
- Joining data
- Concatenating data
- Working with date and time
- Working with text data
Reading files
- Read CSV files
- Reading excel file
- Reading HTML tables from websites
Project: Applying the knowledge gained to analyze hotel data and Uber.
Matplotlib For Visualization
Learn how to plot data, style and adjust your chart with matplotlib.
Introduction
- Overview of Matplotlib
- Importing matplotlib
- Plotting your first graph
Adjusting graph
- Figure Class
- Axes Class
- Setting Limits and Tick labels
- Multiple Plots
- Adding a Legend?
Creating Different Types of Plots
- Line Graph
- Bar chart
- Histograms
- Scatter Plot
- Pie Chart
- Customizing Plots
Task: Grade student results and plot a graph to visualize it.
Seaborn For Visualization
Use different types of charts to perform univariate and multivariate analyses.
Introduction
- Importing seaborn
- Using Seaborn with Matplotlib
- Plotting graph using seaborn
Customizing Seaborn Plots
- Changing Figure Aesthetic
- Changing the figure Size
- Scaling the plots
- Color Palette
- Multiple plots with Seaborn
Creating Different Types of Plots
Relational Plots
- Scatter Plot
- Line Plot
Categorical Plots
- Bar Plot
- Count Plot
- Box Plot
Distribution Plots
- Histogram
- Distplot
- Pairplot
- Rugplot
Matrix Plots
- Heatmap
Key takeaway: Understand how to create and style plots using Seaborn
Github and Data Analytics
Portfolio building and collaboration
- What is GitHub?
- How to set up a GitHub account
- Uploading your project to the GitHub repository
- Downloading and forking code from GitHub
- Documenting your work
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Using CHATGPT
Understanding LLM
- Using LLM to solve real-world problem
Student Feedback
Training Fees
Artificial Intelligence
Covers all the Data Analytics, machine learning and Artificial intelligence. 5 months
N1,500,000
$1,500
Frequently Asked Questions (FAQs)
As more companies are embracing artificial intelligence, the need for data analyst who will clean and process the data is on the increase. The demand for data analysts is growing rapidly across industries like finance, healthcare, e-commerce, marketing, and more as companies are increasingly relying on data-driven insights to make strategic decisions.
No prior programming experience is required. Our training approach is beginner friendly, and we work on several real world projects to help our student have deeper understanding of each module. However, you need to know how to operate the computer efficiently.
We have weekend class for those who cannot attend the weekday class. 10am to 1pm (Nigerian Time)
You can pay N500,000 at the beginning of each month to attend the training.
Yes, it is recommended you come with your own laptop as it will help you to practice whatever you are learning. However, we provide laptop that can be used only within our premises if need be.
Depending on your budget, Core i7, 16 GB ram with SSD is best.
However, core i3 or i5 with 4gb or 8gb ram HDD will also work fine.
It can be HP, DELL, Mac or any other brand.
Yes. In fact, this is our strength. We are committed to supporting our students to ensure they succeed. You will be added to our WhatsApp group to connect with others and also ask questions.
We don’t guarantee job after the training. However, some of our students are connected to remote internships with companies like Accenture, PwC, KPMG etc. We share job alerts from time to time on available vacancies and tips that can help our students get job faster. We also offer CV review, LinkedIn profile optimization and letter of recommendation to diligent students.
All our training are physical at our Ikeja office. However, those who are outside Nigeria or living far can join online. Who join our classes from Canada, The UK, USA, UAE, Finland etc.
Yes. Tech365 was established to bridge the practical knowledge that people are lacking to secure a job. Hence, we focus on making the class beginner friendly with lots of projects to help the participants master the skill.
We issue a certificate of completion after the training. If you are interested in certification, you can pay to take certification exam by international bodies online.
You can chat with our representation using the WhatsApp chat button on our website or call the phone number at the top or bottom of this page. You can also click the register button on this page to show your interest and one of our team will reach out to you.
We don’t compete based on price. Our fees is based on the value we offer. If you want a place that will give you in-depth knowledge and value for your money, you are in the right place. Our past students are glad to learn from us as our training is hands-on and designed to make you job-ready. You can check what over 80 of our students had to say about our training on our Google review page.
Our Students Work at
